social policy communication essay

Chapter 6: 21st-century media and issues

6.10.2 Social media and communication (research essay)

Lindsey Matier

English 102, April 2021

Communication is extremely important in today’s world, whether it be verbal or nonverbal. It can take place through many different forms such as through writing, speaking, listening and physical actions. These forms of communication evolve and continue to improve over time. As humans, we rely on communication for almost everything and it is a way of life. Communication has evolved from talking to writing letters to texting or talking over the phone. Every time a new form of communication is brought up and becomes more popular, we have to adapt and evolve to that new lifestyle. Throughout all the new forms of communication and ways of evolving, social media has been one of the most influential so far. Social media has allowed us to create new ways of communicating, such as texting or posting through different apps. It can connect us with people all over the world and give us a platform to express ourselves in ways that have not been possible before. While social media started off as a small form of technology, it has morphed into aspects of our everyday life. Now there are apps for everything from social media profiles to online shopping. While social media and technology itself has evolved, this has also affected our communication with each other and the world. Social media has created a fast track for information in a matter of seconds. It can give people a platform with millions of followers overnight for doing practically anything. It can help people express themselves in new ways and connect with people who have similar interests. The end goal of social media is to make people happy and ultimately make lives easier.

Introduction

With all this being said, it is evident that social media is in our everyday lives and will continue to change. It has a very strong grip on society as social media usage continues to rise throughout the years. Generalizing social media, we are exposed to forms of media at almost all times of the day. Answering the question of what media is will help give a better understanding of social media as a whole. Media can be defined as a way of mass communication. This could include siting in the car listening to ads on the radio all the way to scrolling on twitter. We are exposed to social media less often than generalized media, but it tends to come in greater quantities when exposed. For example, for people that wake up and check twitter it is an instant flood of information with every scroll. Everything from politics to sports to celebrity news is available at the fingertips. The concern is not all focused on the overwhelming information, but also the overwhelming number of comments and opinions. If we wanted to debate or talk about something before social media it had to be done in person, face to face. Now with social media, we are able to fight with people in comment sections on a backup account with a different name and no connection to who we really are. This new form of communication takes away the vulnerability of speaking to people and having genuine conversation, and makes up for it in internet trolls. Overall, social media is impacting the way we communicate with each other and the real questions are: Is social media impacting us in a positive or negative way? Do the positive aspects outweigh the negative aspects? Is social media hindering the way we communicate in person with each other? Is their more room for improvement when it comes to dealing with communication in the social media spectrum? How is social media impacting younger generation’s communication versus older generation’s communication? How can we help improve our communication skills on social media and in real life?

Personal Research 

Along with the other studies that I found from the sources I chose, I also conducted my own study to determine more accurate and recent data. I asked students mostly within high school and college range questions relating to social media and communication. I tried to get a wide range of data dealing with social media apps, screen time, and overall communication as a result of social media. I expected to see almost all negative responses about social media and communication. I figured that most people would respond saying that it has affected them negatively rather than positively, but the results were different compared to what I expected.

The first questions I asked had to do with social media itself. I asked questions about their most used social media apps, screen time, what age they were allowed to start using social media, and whether or not they think social media has had a negative or positive impact on them. As expected, most of the social media apps were some of the most popular ones like Snapchat, Instagram, and TikTok. Overall, the average screen time for all apps was evenly split between 4-6 and 6-8 hours, which I also expected. Something that did surprise me was the amount of time spent on certain social media apps. The data was split pretty evenly three ways and all between 1-4 hours. The next two questions dealt with when they group surveyed started using social media. I asked these questions because a lot of the points I want to discuss later in my paper have to deal with age and whether younger generations are suffering when it comes to communication. More than half the people surveyed said that they wished that they had waited to get social media until they were older. Some said that it is not appropriate for younger kids and that it is just toxic in general. Something that I really like that a couple people mentioned was that in reality, social media at a young age is stupid and useless. A lot of people said they wish they would have enjoyed their childhood more and they would be more extroverted now if they had not been exposed that early. The last question of this section that I asked was if they thought social media has had a more positive or negative impact on them. Overall, the data was split but leaning slightly towards the more positive side. The positive answers mostly dealt with being able to talk to stay in contact with people and meeting new friends. The negative answers all related to mental health and feeling bad about themselves. A lot of people said it is toxic and very controlling and takes up too much of our time.

The next set of questions I asked had to do more with communication and interaction with and without social media. I asked questions like how they feel about social media and how it has impacted their communication, their mental health, and if it has made our lives easier. I decided to ask questions like these because I figured I would get a wide range of responses and a lot of people’s different opinions. I started off by asking if people are an introvert or an extrovert to get an idea of what the responses would be like, and 66% said somewhere in between the two. The response for the next question really shocked me because I received such a one-side response. I asked if they think social media has impacted their communication and the way they interact with others and 75% (18/24 people) said yes. This is the information that I was looking for along with the next two questions. The next question asked if they think social media has negatively impacted their mental health and 50% said yes. I also plan on using this as a research question to show that social media can affect our mental health and therefore affect the way we interact with and around other people. The last two questions are similar but the responses were both very good. Almost everyone answered yes to the question asking if social media has made our lives easier. Everyone that answered yes said they think so because it helps them talk to friends, stay in touch with people they do not see as much, and meet new people that they are comfortable talking to. The people that said no also made good points such as it takes over our lives and it is filled with too much hate and cancel culture. I agree with both sides and am very happy that people can feel a positive response especially when it comes to communicating with other people online. The last question I asked was used to wrap up the whole survey and topic. I asked if they think social media has made our generation’s communication improve or worsen. The data was pretty evenly split, and most people gave a positive and a negative. The people that said improve gave that answer because they said it broadens our communication and allows us to talk to people at a wider range. The people who said it has made it worse all said that it is ruining our face-to-face interaction and causing us to lose emotion. They said that some people do not even know how to have a proper in person conversation and that they are too dependent on their phones. Overall, I agree with both arguments that people made but I do think that the positives outweigh the negatives in most of these situations and questions.

Research Questions

The first question I want to ask has to deal with the overall social media and communication connection and has multiple other questions I would like to cover within it. The main question is: Is social media hindering the way we communicate with each other? I also want to touch on questions like: Is social media impacting us in a positive or negative way? Do the positives outweigh the negatives? The second set of research questions I have is: Is their more room for improvement when it comes to dealing with communication in the social media spectrum? How can we help improve our communication skills on social media and in real life? How is social media impacting younger generation’s communication versus older generation’s communication?

Research Question One

Social media and communication have a direct connection to each other and both have a strong impact on the outcome of the other. My first research question has to do with that. My questions center around how social media has impacted our communication, and whether or not it is positive or negative. First, I think it is important to note the changes and different characteristics that come into play when talking about this. Things like age and problems going on in our world can affect our social media usage and communication. While we connect to people on a deeper level when talking to the in person, social media has also given us a newer and more broad way of communicating. The article “How Social Media Affects Our Ability to Communicate” by Stacey Hanke, talks about different ways social media has impacted our communication. Social media has become so relevant in our day to day lives and Hanke describes it in a couple different ways. She describes it as information binging and the fear of missing out, social graces and conversational boredom. Within these, she explains how social media has become an excuse and escape to talk to people face to face. Hanke also talks about how even though it is limiting our in person communication, it can sometimes make communicating in general easier, by being able to talk to each other in just a few words (Hanke 1). In another article by Ryan J. Fuller titled “The Impact of Social Media Use on Our Social Skills”, he discusses similar topics to Hanke’s article but also brings up more positive attributes of social media. Fuller starts of his article by giving some statistics, stating that 75% of teens own cellphones and 25% of them using it for social media, and also says that they use 7.5 hours a day using it (Fuller 1). I am glad that this was brought up because it is important to know how much time is spent on social media, scrolling through feed. Next, Fuller starts to discuss some of the benefits of social media. He briefly explains how social media is beneficial because we are able to stay in touch with our friends and family, and share important parts of our lives with them. He also explains how it helps people reach out to new friends and provide themselves with more opportunities (Fuller 1). Overall, I really like that he mentioned these because it is important to keep in mind the vast majority of social media and communication. While some use it for more simpler purposes likes just keeping up to date with what is going on in the world, others use it to make new friends, find new job opportunities, and stay in touch with people. Another topic I find important when it comes to answering this research question is how Covid affected everything. With the pandemic, we were left inside with nothing to do but what was at our fingertips. This pandemic increased social media usage drastically. The article “Social Media Insights Into US Mental Health During the COVID-19 Pandemic: Longitudinal Analysis of Twitter Data” by Danny Valdez et al, shows extensive research into determining just how much social media usage in the United States increased during the pandemic. They did experiments and surveys to determine multiple responses to research questions and show how much we rely on social media to communicate with each other. During the pandemic, everyone spent more time on their social media and their phone in general, probably more than they would like to admit. The article helps give more insight into this claim. There is the idea that social media was meant as an addition to our lives. For some people, it has become an addiction and a new piece of their life. The article focuses on how social media could be a toxic place and have a negative effect on our mental health. The time period for this information focuses around the COVID-19 pandemic. Using data from Twitter, Valdez created a study to determine the mood of people during the pandemic and the usage throughout (Valdez et al 2). Collecting tweets with certain hashtags and during time periods, the goal was to determine how much the pandemic affected people’s moods, and how much they put out and shared on social media. They used hashtags, timeline data, and tweets from different periods such as the first lockdown, different stay at home orders, etc. Given the responses to the data, they were able to determine the increase in social media usage. We cannot determine if this had a positive or negative effect on the people who were using Twitter, but we can infer that social media is becoming a key part of our lives. Not being able to talk to people as much in person during the first few months of the pandemic greatly affected communication, in positive and negative ways. Communication over the phone increased due to the amount of free time that people had and were able to spend talking to others. Contrary to that, in person communication also decreased given that people were not really allowed to leave the house. The next article by Tayebi et al, “The Role of Information Systems in Communication Through Social Media” focuses a lot about how we have evolved over time with social media and communication. They start off by talking about how social networks are like social media societies. They explain it by resembling it to a human society, as it is filled with people communicating, regardless of time or place. They also exemplify other aspects such as emotional support, information, emotions (Tayebi 2). Social media is constantly looked at through such a negative light due to some of the major bad events that have taken place. While it can be difficult at times to look past the negatives, it is important to recognize and acknowledge the positives. The growth of scientific research would not be possible without the amount of information received from the media (Tayebi 3). Without social media and media in general, we would not be where we are today as a society. As mentioned earlier, it is so easy to get lost in the negative aspects of social media and discard the positive ones. Positive parts of social media such as widespread communication and unlimited access to information makes it all worth it. Staying on topic with positive aspects of social media and communication, social media in the workplace has also broken down barriers for communication. The article “A Guide to the Successful Use of Social Media in the Workplace” by Clark Boyd gives insight into how social media has improved the workplace, and ultimately communication and interaction as a whole. Companies can use social media as a form of branding and way to communicate their products (Boyd 4). Boyd states, “Harvard Business Review finds that 82% of employees believe social media improves work relationships. Left to their own devices, your teams will connect and communicate on social networks, both inside and outside the office.” This directly relates to the research question asking whether social media hinders our communication with each other. Social media also helps when it comes to dealing with complaints placed online. By seeing these through social media, it can help the company communicate either with the person or their company the concerns that are being stated (Boyd 9). Overall, it is safe to say that social media has directly affected communication throughout different aspects of our lives.

Research Question Two

My second set of research questions has a lot to do with the future and how we can improve. Questions such as: Is their more room for improvement when it comes to dealing with communication in the social media spectrum? How can we help improve our communication skills on social media and in real life? How is social media impacting younger generation’s communication versus older generation’s communication? The article “What is Literacy” by James Paul Gee talks a lot about the basics of communication. I find this an important article to talk about before I go into more detail with this second research question. Gee explains discourse as a socially accepted way of speaking, thinking, and acting (Gee 1). It is important to note this because social media has changed that discourse for us. We no longer communicate and interact the same way in which we use to therefore almost giving us a new discourse. Another thing Gee discusses is identity kits. Gee explains identity kits as “appropriate costumes and instructions on how to act and talk” (Gee 2). This relates to social media because there is a certain way we communicate online that we wouldn’t do in person. For example, we use emojis and abbreviations to communicate on social media or over text, but this is something we would not do when communicating face-to-face. There are also some basic well-known rules of social media that follow along the lines of an identity kit. Such as, for Instagram it is a common idea not to like people’s pictures from too long ago. When you say this aloud it sounds like it is not a big deal and silly almost, but for people that use social media it is something that makes sense. The next article is going to focus more on the question that has to do with room for improvement of communication. The article “The Positive Effect of Not Following Others on Social Media” by Francesca Valsesia, Davide Proserpio, and Joseph C. Nunes involves how we deal with social media and how we react to it. The article has a lot to do with pyramid schemes and marketing schemes on social media, simply due to follower count. Social media has a lot of power over us and the content we see. Influencers have too much impact on what we see every day and this overall effects our communication (Valsesia 1). Social media feeds us information at our fingertips, whether it be true or false. Valsesia is trying to get the point across that social media has no impact on our lives without the phone and therefore, having a smaller follower count is better for our communication and overall wellbeing in the first place. Leading into my next article, social media can have a huge impact on the younger generation. This leads into part of my second research question dealing with the younger generation and their communication. The article “The Impact of Social Media on Youth Mental Health: Challenges and Opportunities” by Jacqueline Nesi shows how social media is a very complex brand of information and makes it complicated for everyone. Younger kids having access to it and multiple devices like computers and phones makes it that much more difficult. There are a lot of positives and negatives for younger kids having access to social media and the internet in general. It has an impact on their mental health and studies show it leads to signs of depression, body dysmorphia, eating disorders (Nesi 2). It can also affect their communication and outward identity due to things such as bullying, internet drama, and behavioral problems. While it does have serious negative risks, social media also can bring a lot of new positive ones. Things like creative ideas, humor and entertainment, and being able to explore their identity are all really great positives that social media gives us (Nesi 4). Most of them using it as a way to connect with friends and family and help them feel a sense of acceptance and belonging (Nesi 4). Similarly to this, social media has given a great outlet for kids and young adults to speak out on issues going on in the world. The article “Building Bridges: Exploring the Communication Trends and Perceived Sociopolitical Benefits of Adolescents Engaging in Online Social Justice Efforts” by Mariah Elsa Kornbluh goes into detail about the racial injustices in the world and how they are communicated through social media. Social media networks can help connect kids to different backgrounds and aspects of their lives (Kornbluh 1). Kornbluh expresses how a society only can flourish under civic engagement and being able to express ourselves, and social media is helping us do that. It is helping the younger generation prepare for the civic role that they will undergo (Kornbluh 2). Social media helps play a major role in participating in political movements and bringing awareness to topics (Kornbluh 3). This all is done by the younger generation and would not be possible without them. So, while it is easy to look at the negative parts of social media and how it effects the younger generation, it also brings great awareness to real life problems in our world. This last article I wanted to go over dealing with this research question has to do with the pandemic. The article “Responses to COVID-19 in Higher Education: Social Media Usage for Sustaining Formal Academic Communication in Developing Countries” by Abu Elnasr E. Sobaih, Ahmed M. Hasanein and Ahmed E. Abu Elnasr briefly talks about communication with social media in higher education systems. Education systems had to switch from in person learning and communication to online learning, which was a struggle for everyone. Throughout the time that this took place, results showed that social media had a positive effect on students dealing with this (Sobaih 1). Students used social media to build a community and help support each other through this rough time. Through these results, proper usage of social media can be shown as a positive result for a new era of learning (Sobaih 1). This is just one more reason why social media can help us improve our future.

After answering my research questions, it has become clear to me that while social media does have negative aspects, the positive aspects outweigh them. Between the articles and my own research, I have enough evidence to prove that social media does effect communication, but in a more positive way. The way we act and present ourselves is heavily influenced by social media and communication between generations are different and can be seen that way. It is important to note the accomplishments we have made as a society with social media and the media in general. It has helped connect families, provide support groups, and provide entertainment in desperate times. Our communication has changed because of social media but has changed and helped us for the better in the long run. Keeping social media a positive place and staying away from the toxic people on it will only help us grow and learn new things about ourselves.

Works Cited

Boyd, Clark. “A Guide to Using Social Media in the Workplace in 2021.”  The Blueprint , The Blueprint, 13 May 2020, www.fool.com/the-blueprint/social-media-in-the-workplace/.

https://www.fool.com/the-blueprint/social-media-in-the-workplace/

D, Valdez, et al. “Social Media Insights Into US Mental Health During the Covid-19 Pandemic: Longitudinal Analysis of Twitter Data.”  Journal of Medical Internet Research  , vol. 22, no. 12, 14 Dec. 2020, pp. 1438–8871.

http://eds.b.ebscohost.com.proxy.ulib.csuohio.edu:2050/eds/detail/detail? vid=8&sid=ff59b04c-b868-44cd-b864-4538e112a2ea%40sessionmgr103&bdata=JnNpdGU9ZWRzLWxpdmUmc2NvcGU9c2l0ZQ%3d%3d#AN=33284783&db=mnh

J, Nesi. “The Impact of Social Media on Youth Health: Challenges and Opportunities.”  North Carolina Medical Journal , vol. 81, no. 2, 2020, pp. 116–121.

http://eds.b.ebscohost.com.proxy.ulib.csuohio.edu:2050/eds/detail/detail?vid=10&sid=ff59b04c-b868-44cd-b864-4538e112a2ea%40sessionmgr103&bdata=JnNpdGU9ZWRzLWxpdmUmc2NvcGU9c2l0ZQ%3d%3d#AN=32132255&db=mnh

Gee, James Paul. “What is literacy.”  Negotiating academic literacies: Teaching and learning  across languages and cultures  (1998): 51-59.

https://academic.jamespaulgee.com/pdfs/Gee%20What%20is%20Literacy.pdf

Hanke, Stacey. “How Social Media Affects Our Ability to Communicate.”  Thrive Global , 13  Sept. 2018, thriveglobal.com/stories/how-social-media-affects-our-ability-to-communicate/.

https://thriveglobal.com/stories/how-social-media-affects-our-ability-to-communicate/

http://eds.a.ebscohost.com.proxy.ulib.csuohio.edu:2050/eds/pdfviewer/pdfviewer?vid=4&sid=467b825c-34f8-4e47-95df-e5b2b61bbaf4%40sessionmgr4006

Kornbluh, Mariah Elsa. “Building Bridges.”  Youth & Society , vol. 51, no. 8, 2017, pp. 1104–1126., doi:10.1177/0044118×17723656.

https://journals-sagepub-com.proxy.ulib.csuohio.edu/doi/pdf/10.1177/0044118X17723656

Retchin, Sarah, et al. “The Impact of Social Media Use on Social Skills.”  New York Behavioral Health , 1 Dec. 2020, newyorkbehavioralhealth.com/the-impact-of-social-media-use-on-social-skills/.

https://newyorkbehavioralhealth.com/the-impact-of-social-media-use-on-social-skills/

Sobaih, Abu Elnasr E., et al. “Responses to COVID-19 in Higher Education: Social Media Usage for Sustaining Formal Academic Communication in Developing Countries.”  MDPI , Multidisciplinary Digital Publishing Institute, 12 Aug. 2020, www.mdpi.com/2071-1050/12/16/6520/htm.

https://www.mdpi.com/2071-1050/12/16/6520/htm

Tayeb, Seyed Mohammad, et al. “The Role of Information Systems in Communication through Social Media.”  International Journal of Data and Network Science , vol. 3, no. 3, 2019, pp. 245–268., doi:10.5267/j.ijdns.2019.2.002.

http://www.growingscience.com/ijds/Vol3/ijdns_2019_15.pdf

Valsesia, Francesca, et al. “The Positive Effect of Not Following Others on Social Media .”  Journal of Marketing Research  , vol. 57, no. 6, Dec. 2020, pp. 1152–1168.

https://www.francescavalsesia.com/uploads/1/0/5/1/105151509/the_positive_effect_of_not_following_others_on_social_media.pdf

Understanding Literacy in Our Lives by Lindsey Matier is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License , except where otherwise noted.

Share This Book

Feb 15, 2023

6 Example Essays on Social Media | Advantages, Effects, and Outlines

Got an essay assignment about the effects of social media we got you covered check out our examples and outlines below.

Social media has become one of our society's most prominent ways of communication and information sharing in a very short time. It has changed how we communicate and has given us a platform to express our views and opinions and connect with others. It keeps us informed about the world around us. Social media platforms such as Facebook, Twitter, Instagram, and LinkedIn have brought individuals from all over the world together, breaking down geographical borders and fostering a genuinely global community.

However, social media comes with its difficulties. With the rise of misinformation, cyberbullying, and privacy problems, it's critical to utilize these platforms properly and be aware of the risks. Students in the academic world are frequently assigned essays about the impact of social media on numerous elements of our lives, such as relationships, politics, and culture. These essays necessitate a thorough comprehension of the subject matter, critical thinking, and the ability to synthesize and convey information clearly and succinctly.

But where do you begin? It can be challenging to know where to start with so much information available. Jenni.ai comes in handy here. Jenni.ai is an AI application built exclusively for students to help them write essays more quickly and easily. Jenni.ai provides students with inspiration and assistance on how to approach their essays with its enormous database of sample essays on a variety of themes, including social media. Jenni.ai is the solution you've been looking for if you're experiencing writer's block or need assistance getting started.

So, whether you're a student looking to better your essay writing skills or want to remain up to date on the latest social media advancements, Jenni.ai is here to help. Jenni.ai is the ideal tool for helping you write your finest essay ever, thanks to its simple design, an extensive database of example essays, and cutting-edge AI technology. So, why delay? Sign up for a free trial of Jenni.ai today and begin exploring the worlds of social networking and essay writing!

Want to learn how to write an argumentative essay? Check out these inspiring examples!

We will provide various examples of social media essays so you may get a feel for the genre.

6 Examples of Social Media Essays

Here are 6 examples of Social Media Essays:

The Impact of Social Media on Relationships and Communication

Introduction:.

The way we share information and build relationships has evolved as a direct result of the prevalence of social media in our daily lives. The influence of social media on interpersonal connections and conversation is a hot topic. Although social media has many positive effects, such as bringing people together regardless of physical proximity and making communication quicker and more accessible, it also has a dark side that can affect interpersonal connections and dialogue.

Positive Effects:

Connecting People Across Distances

One of social media's most significant benefits is its ability to connect individuals across long distances. People can use social media platforms to interact and stay in touch with friends and family far away. People can now maintain intimate relationships with those they care about, even when physically separated.

Improved Communication Speed and Efficiency

Additionally, the proliferation of social media sites has accelerated and simplified communication. Thanks to instant messaging, users can have short, timely conversations rather than lengthy ones via email. Furthermore, social media facilitates group communication, such as with classmates or employees, by providing a unified forum for such activities.

Negative Effects:

Decreased Face-to-Face Communication

The decline in in-person interaction is one of social media's most pernicious consequences on interpersonal connections and dialogue. People's reliance on digital communication over in-person contact has increased along with the popularity of social media. Face-to-face interaction has suffered as a result, which has adverse effects on interpersonal relationships and the development of social skills.

Decreased Emotional Intimacy

Another adverse effect of social media on relationships and communication is decreased emotional intimacy. Digital communication lacks the nonverbal cues and facial expressions critical in building emotional connections with others. This can make it more difficult for people to develop close and meaningful relationships, leading to increased loneliness and isolation.

Increased Conflict and Miscommunication

Finally, social media can also lead to increased conflict and miscommunication. The anonymity and distance provided by digital communication can lead to misunderstandings and hurtful comments that might not have been made face-to-face. Additionally, social media can provide a platform for cyberbullying , which can have severe consequences for the victim's mental health and well-being.

Conclusion:

In conclusion, the impact of social media on relationships and communication is a complex issue with both positive and negative effects. While social media platforms offer many benefits, such as connecting people across distances and enabling faster and more accessible communication, they also have a dark side that can negatively affect relationships and communication. It is up to individuals to use social media responsibly and to prioritize in-person communication in their relationships and interactions with others.

The Role of Social Media in the Spread of Misinformation and Fake News

Social media has revolutionized the way information is shared and disseminated. However, the ease and speed at which data can be spread on social media also make it a powerful tool for spreading misinformation and fake news. Misinformation and fake news can seriously affect public opinion, influence political decisions, and even cause harm to individuals and communities.

The Pervasiveness of Misinformation and Fake News on Social Media

Misinformation and fake news are prevalent on social media platforms, where they can spread quickly and reach a large audience. This is partly due to the way social media algorithms work, which prioritizes content likely to generate engagement, such as sensational or controversial stories. As a result, false information can spread rapidly and be widely shared before it is fact-checked or debunked.

The Influence of Social Media on Public Opinion

Social media can significantly impact public opinion, as people are likelier to believe the information they see shared by their friends and followers. This can lead to a self-reinforcing cycle, where misinformation and fake news are spread and reinforced, even in the face of evidence to the contrary.

The Challenge of Correcting Misinformation and Fake News

Correcting misinformation and fake news on social media can be a challenging task. This is partly due to the speed at which false information can spread and the difficulty of reaching the same audience exposed to the wrong information in the first place. Additionally, some individuals may be resistant to accepting correction, primarily if the incorrect information supports their beliefs or biases.

In conclusion, the function of social media in disseminating misinformation and fake news is complex and urgent. While social media has revolutionized the sharing of information, it has also made it simpler for false information to propagate and be widely believed. Individuals must be accountable for the information they share and consume, and social media firms must take measures to prevent the spread of disinformation and fake news on their platforms.

The Effects of Social Media on Mental Health and Well-Being

Social media has become an integral part of modern life, with billions of people around the world using platforms like Facebook, Instagram, and Twitter to stay connected with others and access information. However, while social media has many benefits, it can also negatively affect mental health and well-being.

Comparison and Low Self-Esteem

One of the key ways that social media can affect mental health is by promoting feelings of comparison and low self-esteem. People often present a curated version of their lives on social media, highlighting their successes and hiding their struggles. This can lead others to compare themselves unfavorably, leading to feelings of inadequacy and low self-esteem.

Cyberbullying and Online Harassment

Another way that social media can negatively impact mental health is through cyberbullying and online harassment. Social media provides a platform for anonymous individuals to harass and abuse others, leading to feelings of anxiety, fear, and depression.

Social Isolation

Despite its name, social media can also contribute to feelings of isolation. At the same time, people may have many online friends but need more meaningful in-person connections and support. This can lead to feelings of loneliness and depression.

Addiction and Overuse

Finally, social media can be addictive, leading to overuse and negatively impacting mental health and well-being. People may spend hours each day scrolling through their feeds, neglecting other important areas of their lives, such as work, family, and self-care.

In sum, social media has positive and negative consequences on one's psychological and emotional well-being. Realizing this, and taking measures like reducing one's social media use, reaching out to loved ones for help, and prioritizing one's well-being, are crucial. In addition, it's vital that social media giants take ownership of their platforms and actively encourage excellent mental health and well-being.

The Use of Social Media in Political Activism and Social Movements

Social media has recently become increasingly crucial in political action and social movements. Platforms such as Twitter, Facebook, and Instagram have given people new ways to express themselves, organize protests, and raise awareness about social and political issues.

Raising Awareness and Mobilizing Action

One of the most important uses of social media in political activity and social movements has been to raise awareness about important issues and mobilize action. Hashtags such as #MeToo and #BlackLivesMatter, for example, have brought attention to sexual harassment and racial injustice, respectively. Similarly, social media has been used to organize protests and other political actions, allowing people to band together and express themselves on a bigger scale.

Connecting with like-minded individuals

A second method in that social media has been utilized in political activity and social movements is to unite like-minded individuals. Through social media, individuals can join online groups, share knowledge and resources, and work with others to accomplish shared objectives. This has been especially significant for geographically scattered individuals or those without access to traditional means of political organizing.

Challenges and Limitations

As a vehicle for political action and social movements, social media has faced many obstacles and restrictions despite its many advantages. For instance, the propagation of misinformation and fake news on social media can impede attempts to disseminate accurate and reliable information. In addition, social media corporations have been condemned for censorship and insufficient protection of user rights.

In conclusion, social media has emerged as a potent instrument for political activism and social movements, giving voice to previously unheard communities and galvanizing support for change. Social media presents many opportunities for communication and collaboration. Still, users and institutions must be conscious of the risks and limitations of these tools to promote their responsible and productive usage.

The Potential Privacy Concerns Raised by Social Media Use and Data Collection Practices

With billions of users each day on sites like Facebook, Twitter, and Instagram, social media has ingrained itself into every aspect of our lives. While these platforms offer a straightforward method to communicate with others and exchange information, they also raise significant concerns over data collecting and privacy. This article will examine the possible privacy issues posed by social media use and data-gathering techniques.

Data Collection and Sharing

The gathering and sharing of personal data are significant privacy issues brought up by social media use. Social networking sites gather user data, including details about their relationships, hobbies, and routines. This information is made available to third-party businesses for various uses, such as marketing and advertising. This can lead to serious concerns about who has access to and uses our personal information.

Lack of Control Over Personal Information

The absence of user control over personal information is a significant privacy issue brought up by social media usage. Social media makes it challenging to limit who has access to and how data is utilized once it has been posted. Sensitive information may end up being extensively disseminated and may be used maliciously as a result.

Personalized Marketing

Social media companies utilize the information they gather about users to target them with adverts relevant to their interests and usage patterns. Although this could be useful, it might also cause consumers to worry about their privacy since they might feel that their personal information is being used without their permission. Furthermore, there are issues with the integrity of the data being used to target users and the possibility of prejudice based on individual traits.

Government Surveillance

Using social media might spark worries about government surveillance. There are significant concerns regarding privacy and free expression when governments in some nations utilize social media platforms to follow and monitor residents.

In conclusion, social media use raises significant concerns regarding data collecting and privacy. While these platforms make it easy to interact with people and exchange information, they also gather a lot of personal information, which raises questions about who may access it and how it will be used. Users should be aware of these privacy issues and take precautions to safeguard their personal information, such as exercising caution when choosing what details to disclose on social media and keeping their information sharing with other firms to a minimum.

The Ethical and Privacy Concerns Surrounding Social Media Use And Data Collection

Our use of social media to communicate with loved ones, acquire information, and even conduct business has become a crucial part of our everyday lives. The extensive use of social media does, however, raise some ethical and privacy issues that must be resolved. The influence of social media use and data collecting on user rights, the accountability of social media businesses, and the need for improved regulation are all topics that will be covered in this article.

Effect on Individual Privacy:

Social networking sites gather tons of personal data from their users, including delicate information like search history, location data, and even health data. Each user's detailed profile may be created with this data and sold to advertising or used for other reasons. Concerns regarding the privacy of personal information might arise because social media businesses can use this data to target users with customized adverts.

Additionally, individuals might need to know how much their personal information is being gathered and exploited. Data breaches or the unauthorized sharing of personal information with other parties may result in instances where sensitive information is exposed. Users should be aware of the privacy rules of social media firms and take precautions to secure their data.

Responsibility of Social Media Companies:

Social media firms should ensure that they responsibly and ethically gather and use user information. This entails establishing strong security measures to safeguard sensitive information and ensuring users are informed of what information is being collected and how it is used.

Many social media businesses, nevertheless, have come under fire for not upholding these obligations. For instance, the Cambridge Analytica incident highlighted how Facebook users' personal information was exploited for political objectives without their knowledge. This demonstrates the necessity of social media corporations being held responsible for their deeds and ensuring that they are safeguarding the security and privacy of their users.

Better Regulation Is Needed

There is a need for tighter regulation in this field, given the effect, social media has on individual privacy as well as the obligations of social media firms. The creation of laws and regulations that ensure social media companies are gathering and using user information ethically and responsibly, as well as making sure users are aware of their rights and have the ability to control the information that is being collected about them, are all part of this.

Additionally, legislation should ensure that social media businesses are held responsible for their behavior, for example, by levying fines for data breaches or the unauthorized use of personal data. This will provide social media businesses with a significant incentive to prioritize their users' privacy and security and ensure they are upholding their obligations.

In conclusion, social media has fundamentally changed how we engage and communicate with one another, but this increased convenience also raises several ethical and privacy issues. Essential concerns that need to be addressed include the effect of social media on individual privacy, the accountability of social media businesses, and the requirement for greater regulation to safeguard user rights. We can make everyone's online experience safer and more secure by looking more closely at these issues.

In conclusion, social media is a complex and multifaceted topic that has recently captured the world's attention. With its ever-growing influence on our lives, it's no surprise that it has become a popular subject for students to explore in their writing. Whether you are writing an argumentative essay on the impact of social media on privacy, a persuasive essay on the role of social media in politics, or a descriptive essay on the changes social media has brought to the way we communicate, there are countless angles to approach this subject.

However, writing a comprehensive and well-researched essay on social media can be daunting. It requires a thorough understanding of the topic and the ability to articulate your ideas clearly and concisely. This is where Jenni.ai comes in. Our AI-powered tool is designed to help students like you save time and energy and focus on what truly matters - your education. With Jenni.ai , you'll have access to a wealth of examples and receive personalized writing suggestions and feedback.

Whether you're a student who's just starting your writing journey or looking to perfect your craft, Jenni.ai has everything you need to succeed. Our tool provides you with the necessary resources to write with confidence and clarity, no matter your experience level. You'll be able to experiment with different styles, explore new ideas , and refine your writing skills.

So why waste your time and energy struggling to write an essay on your own when you can have Jenni.ai by your side? Sign up for our free trial today and experience the difference for yourself! With Jenni.ai, you'll have the resources you need to write confidently, clearly, and creatively. Get started today and see just how easy and efficient writing can be!

Try Jenni for free today

Create your first piece of content with Jenni today and never look back

Theories and Practice on Policy Communication

• First Online: 09 November 2019

Cite this chapter

• Basskaran Nair 4 ,
• Saltanat Janenova 4 &
• Balzhan Serikbayeva 5  

169 Accesses

This chapter discusses the leading theories on communications that are attuned to and utilised by developed nations for a market-driven economy. Many of these theories are based on European, British and American literature where practice and performance are closely linked to global standards of communication. It intertwines these theories with local examples. Practitioners can apply these theories to the execution of their public policies.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

• Available as EPUB and PDF
• Read on any device
• Instant download
• Own it forever
• Durable hardcover edition
• Dispatched in 3 to 5 business days
• Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Axelrod, R. (1984). The Evolution of Cooperation . New York: Basic Books.

Google Scholar  

Bales, S. N. (2002). Framing Public Issues . Washington, DC: Frameworks Institute. http://www.frameworksinstitute.org/assets/files/PDF/FramingPublicIssuesfinal.pdf . Accessed 22 August 2019.

Bandura, A. (1986). Social Foundation of Thought and Action . Englewood Cliffs, NJ: Prentice Hall.

Buchanan, J., & Tullock, G. (1962). The Calculus of Consent . Ann Arbor: University of Michigan Press.

Book   Google Scholar  

Burkhanov, A. (2017). Kazakhstan’s national identity—Building policy: Soviet legacy, state efforts, and societal reactions. Cornell International Law Journal, 50 (1), Article 1. https://scholarship.law.cornell.edu/cgi/viewcontent.cgi?article=1893&context=cilj . Accessed 22 August 2019.

Cobb, R. W., & Elder, C. D. (1972). Participation in American Politics: The Dynamics of Agenda Setting . Baltimore, MD: John Hopkins University Press.

Cohen, B. (1963). The Press and Foreign Policy . Princeton: Princeton University Press.

Dye, T. R. (2008). Understanding Public Policy (11th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.

Ferguson, Sh. D. (2000). Researching the Public Opinion Environment: Theories and Methods . Thousand Oaks, CA: Sage.

Festinger, L. (1957). A Theory of Cognitive Dissonance . Stanford, CA: Stanford University Press.

Ikramova, U., & McConnell, K. (1999). Women’s NGOs in Central Asia’s evolving societies. In M. H. Ruffin & D. Waugh (Eds.), Civil Society in Central Asia . Seattle: University of Washington Press.

John, P. (2011). Making Public Policy . London: Routledge.

Kazakhstan News Gazette. (2018, April 12). Year of Rukhani Zhangyru: Spiritual Modernization United Kazakhstanis . https://kazakhstannewsgazette.com/year-of-rukhani-zhangyru-spiritual-modernization-united-kazakhstanis/ . Accessed 22 August 2019.

Koenig, L. W. (1986). An Introduction to Public Policy . Englewood Cliffs, NJ: Prentice‐Hall.

Kotler, Ph., & Zaltman, G. (1971). Social marketing: An approach to planned social change. Journal of Marketing, 35, 3–12.

Article   Google Scholar  

Kuzhabekova, A., Janenova, S., & Almukhambetova, A. (2018). Analyzing the experiences of female leaders in civil service in Kazakhstan: Trapped between economic pressure to earn and traditional family role expectations. International Journal of Public Administration, 41 (15), 1290–1301.

Larana, E., Johnston, H., & Gusfield, J. (1994). New Social Movements: From Ideology to Identity . Philadelphia: Temple University Press.

Lindblom, Ch. (1959). The science of muddling through. Public Administration Review, 19 (2), 79–88.

Lippmann, W. (1992). Public Opinion . New York: Free Press and Simon & Schuster.

McCombs, M. (2004). Setting the Agenda: The Mass Media and Public Opinion . Cambridge, UK: Polity Press.

McGuire, W. J. (1974). Persuasion, resistance, and attitude change. In I. Sola & W. Schramm (Eds.), Handbook of Communications . Chicago: Rand McNally.

Nair, B. (2014, July 12). Governance in wired world . Business Times , p. 26.

Neubeck, K. J., Neubeck, M. A., & Glasberg, D. S. (2007). Social Problems (5th ed.). New York: McGraw-Hill.

Newsom, D., Turk, J. V., & Kruckeburg, D. (2000). This Is PR: The Realities of Public Relations . Belmont, CA: Wadsworth.

Spitzer, R. J. (1993). Media and Public Policy . New York: Praeger Publishers.

Stroud, N. J., & Muddiman, A. (2013). The American media system today: Is the public fragmenting? In T. N. Ridout (Ed.), New Directions in Media and Politics . New York: Routledge.

Thaler, R. H., & Sunstein, C. R. (2009). Nudge: Improving Decisions About Health, Wealth and Happiness . London: Penguin Books.

Truman, D. (1954). The Government Process . New York: Knopf.

Download references

Author information

Authors and affiliations.

Nazarbayev University, Nur-Sultan, Kazakhstan

Basskaran Nair & Saltanat Janenova

GSPP, Nur-Sultan, Kazakhstan

Balzhan Serikbayeva

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Basskaran Nair .

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Author(s)

About this chapter

Nair, B., Janenova, S., Serikbayeva, B. (2020). Theories and Practice on Policy Communication. In: A Primer on Policy Communication in Kazakhstan. Palgrave Pivot, Singapore. https://doi.org/10.1007/978-981-15-0610-9_2

Download citation

DOI : https://doi.org/10.1007/978-981-15-0610-9_2

Published : 09 November 2019

Publisher Name : Palgrave Pivot, Singapore

Print ISBN : 978-981-15-0609-3

Online ISBN : 978-981-15-0610-9

eBook Packages : Political Science and International Studies Political Science and International Studies (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

• Search Menu
• Browse content in Arts and Humanities
• Browse content in Architecture
• Theory of Architecture
• Browse content in History
• History of Education
• Regional and National History
• Browse content in Philosophy
• Feminist Philosophy
• Philosophy of Language
• Browse content in Religion
• Religious Studies
• Browse content in Society and Culture
• Cultural Studies
• Ethical Issues and Debates
• Technology and Society
• Browse content in Law
• Comparative Law
• Criminal Law
• Environment and Energy Law
• Human Rights and Immigration
• Browse content in International Law
• Public International Law
• Legal System and Practice
• Medical and Healthcare Law
• Browse content in Medicine and Health
• Browse content in Public Health and Epidemiology
• Public Health
• Browse content in Science and Mathematics
• Browse content in Earth Sciences and Geography
• Environmental Geography
• Urban Geography
• Environmental Science
• Browse content in Psychology
• Social Psychology
• Browse content in Social Sciences
• Browse content in Anthropology
• Anthropology of Religion
• Browse content in Business and Management
• Business Ethics
• Business History
• Corporate Social Responsibility
• Human Resource Management
• Industry Studies
• Information and Communication Technologies
• Knowledge Management
• Criminology and Criminal Justice
• Browse content in Economics
• Behavioural Economics and Neuroeconomics
• Economic Systems
• Economic History
• Economic Development and Growth
• Financial Markets
• History of Economic Thought
• Public Economics
• Browse content in Education
• Educational Strategies and Policy
• Higher and Further Education
• Philosophy and Theory of Education
• Browse content in Human Geography
• Political Geography
• Browse content in Politics
• Asian Politics
• Comparative Politics
• Conflict Politics
• Environmental Politics
• European Union
• Indian Politics
• International Relations
• Middle Eastern Politics
• Political Sociology
• Political Economy
• Political Theory
• Public Policy
• Russian Politics
• Security Studies
• UK Politics
• US Politics
• Browse content in Regional and Area Studies
• Asian Studies
• Browse content in Social Work
• Care of the Elderly
• Child and Adolescent Social Work
• Couple and Family Social Work
• Developmental and Physical Disabilities Social Work
• Direct Practice and Clinical Social Work
• Human Behaviour and the Social Environment
• International and Global Issues in Social Work
• Mental and Behavioural Health
• Social Justice and Human Rights
• Social Policy and Advocacy
• Social Work and Crime and Justice
• Social Work Macro Practice
• Social Work Practice Settings
• Social Work Research and Evidence-based Practice
• Welfare and Benefit Systems
• Browse content in Sociology
• Childhood Studies
• Community Development
• Comparative and Historical Sociology
• Economic Sociology
• Gender and Sexuality
• Gerontology and Ageing
• Health, Illness, and Medicine
• Marriage and the Family
• Migration Studies
• Occupations, Professions, and Work
• Organizations
• Population and Demography
• Race and Ethnicity
• Social Theory
• Social Movements and Social Change
• Social Research and Statistics
• Social Stratification, Inequality, and Mobility
• Sociology of Religion
• Sociology of Education
• Urban and Rural Studies
• Reviews and Awards
• Journals on Oxford Academic
• Books on Oxford Academic

• < Previous

Introduction 1 Close

• Published: October 2018
• Cite Icon Cite
• Permissions Icon Permissions

Sociology, and the allied field of social policy, were latecomers to British universities. Their modern disciplinary identities were only established in the three decades after the Second World War, replacing the earlier, less methodologically prescriptive traditions of social enquiry that had flourished in Britain in the first half of the twentieth century. This ascent to academic respectability also had a major impact on post-war public life. Technical experts on ‘society’ began to play a more celebrated role in public policy debates and cultural commentary. 2 Close Amid the galaxy of outstanding British social scientists of the 1950s and 1960s, Richard Titmuss (1907-73) has a good claim to be the most influential.

Although eventually a beneficiary of the more technocratic and credentialed world of British higher education after the War, Titmuss had in fact left school without any qualifications at the age of 14 and spent the early years of his career working for an insurance company. But he was also a prodigious autodidact and accompanied his paid work with a growing side-line as a writer on social questions. His burgeoning reputation in this field led him to be commissioned to write the official history of the social services during the Second World War. The resulting book, Problems of Social Policy (1950), was well-received and it was on the strength of this work that he was appointed Professor of Social Administration at the LSE in 1950. From this platform Titmuss laid the foundations of the study of social policy as a separate academic field and produced numerous significant publications on key welfare issues, notably on the distribution of income and on healthcare. Titmuss’s influence extended beyond academia to broader public debates in Britain in the 1950s and 1960s about social inequality. He was close to the Labour Party and was a respected informal advisor to senior Labour politicians. The 1964-70 Labour government drew extensively on policies that he and his associates – particularly Peter Townsend and Brian Abel-Smith – had developed. Essays on ‘the Welfare State’ was published before that, in 1958, and collected together Titmuss’s key articles and lectures from his first eight years at the LSE. It was a work that triumphantly demonstrated the strengths of Titmuss’s approach to the study of social policy, incisively opening up swathes of new intellectual territory into which subsequent generations of experts and policy-makers would pour for decades afterwards.

Signed in as

Institutional accounts.

• GoogleCrawler [DO NOT DELETE]
• Google Scholar Indexing

Personal account

• Sign in with email/username & password
• Get email alerts
• Save searches
• Purchase content
• Activate your purchase/trial code

Institutional access

• Sign in with a library card Sign in with username/password Recommend to your librarian
• Institutional account management
• Get help with access

Access to content on Oxford Academic is often provided through institutional subscriptions and purchases. If you are a member of an institution with an active account, you may be able to access content in one of the following ways:

IP based access

Typically, access is provided across an institutional network to a range of IP addresses. This authentication occurs automatically, and it is not possible to sign out of an IP authenticated account.

Sign in through your institution

Choose this option to get remote access when outside your institution. Shibboleth/Open Athens technology is used to provide single sign-on between your institution’s website and Oxford Academic.

• Click Sign in through your institution.
• Select your institution from the list provided, which will take you to your institution's website to sign in.
• When on the institution site, please use the credentials provided by your institution. Do not use an Oxford Academic personal account.
• Following successful sign in, you will be returned to Oxford Academic.

If your institution is not listed or you cannot sign in to your institution’s website, please contact your librarian or administrator.

Sign in with a library card

Enter your library card number to sign in. If you cannot sign in, please contact your librarian.

Society Members

Society member access to a journal is achieved in one of the following ways:

Sign in through society site

Many societies offer single sign-on between the society website and Oxford Academic. If you see ‘Sign in through society site’ in the sign in pane within a journal:

• Click Sign in through society site.
• When on the society site, please use the credentials provided by that society. Do not use an Oxford Academic personal account.

If you do not have a society account or have forgotten your username or password, please contact your society.

Sign in using a personal account

Some societies use Oxford Academic personal accounts to provide access to their members. See below.

A personal account can be used to get email alerts, save searches, purchase content, and activate subscriptions.

Some societies use Oxford Academic personal accounts to provide access to their members.

Viewing your signed in accounts

Click the account icon in the top right to:

• View your signed in personal account and access account management features.
• View the institutional accounts that are providing access.

Signed in but can't access content

Oxford Academic is home to a wide variety of products. The institutional subscription may not cover the content that you are trying to access. If you believe you should have access to that content, please contact your librarian.

For librarians and administrators, your personal account also provides access to institutional account management. Here you will find options to view and activate subscriptions, manage institutional settings and access options, access usage statistics, and more.

Our books are available by subscription or purchase to libraries and institutions.

• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Rights and permissions
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

Essay on Importance of Communication for Students and Children

500+ words essay on importance of communication:.

Communication is one of the important tools that aid us to connect with people. Either you are a student or a working professional, good communication is something that will connect you far ahead. Proper communication can help you to solve a number of issues and resolve problems. This is the reason that one must know how to communicate well. The skills of communication essential to be developed so that you are able to interact with people. And able to share your thoughts and reach out to them. All this needs the correct guidance and self-analysis as well.

Meaning of Communication

The word communication is basically a process of interaction with the people and their environment . Through such type of interactions, two or more individuals influence the ideas, beliefs, and attitudes of each other.

Such interactions happen through the exchange of information through words, gestures, signs, symbols, and expressions. In organizations, communication is an endless process of giving and receiving information and to build social relationships.

Importance of Communication

Communication is not merely essential but the need of the hour. It allows you to get the trust of the people and at the same time carry better opportunities before you. Some important points are as follows –

Help to Build Relationships 

No matter either you are studying or working, communication can aid you to build a relationship with the people. If you are studying you communicate with classmates and teachers to build a relationship with them. Likewise in offices and organizations too, you make relationships with the staff, your boss and other people around.

Improve the Working Environment 

There are a number of issues which can be handled through the right and effective communication. Even planning needs communication both written as well as verbal. Hence it is essential to be good in them so as to fill in the communication gap.

Foster strong team

Communication helps to build a strong team environment in the office and other places. Any work which requires to be done in a team. It is only possible if the head communicates everything well and in the right direction.

Find the right solutions

Through communication, anyone can find solutions to even serious problems. When we talk, we get ideas from people that aid us to solve the issues. This is where communication comes into play. Powerful communication is the strength of any organization and can help it in many ways.

Earns more respect

If your communication skills are admirable, people will love and give you respect. If there is any problem, you will be the first person to be contacted. Thus it will increase your importance. Hence you can say that communications skills can make a big change to your reputation in society.

Get the huge list of more than 500 Essay Topics and Ideas

Don’t Go Overboard With Your Point

The conversation is about to express your thoughts. And to let the other person know what you feel. It is not mean to prove that your point is correct and the other person is wrong. Don’t Overboard other With Your Point.

Watch Your Words

Before you say something to Watch Your Words. At times, out of anger or anxiousness, we say somethings that we must not say. Whenever you are in a professional meeting or in some formal place, where there is a necessity of communicating about your product or work then it is advised to practice the same beforehand

Communication is the greatest importance. It is important to sharing out one’s thoughts and feelings to live a fuller and happier life. The more we communicate the less we suffer and the better we feel about everything around. However, it is all the more necessary to learn the art of effective communication to put across ones point well.

Customize your course in 30 seconds

Which class are you in.

• Travelling Essay
• Picnic Essay
• Our Country Essay
• My Parents Essay
• Essay on Favourite Personality
• Essay on Memorable Day of My Life
• Essay on Knowledge is Power
• Essay on Gurpurab
• Essay on My Favourite Season
• Essay on Types of Sports

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Download the App

Contemporary Issues in Social Policy Essay

Strength and welfare of a society depend largely on the extent to which government and social institutions care for their most vulnerable members. Under certain circumstances, people with a learning disability, particularly those who experience severe complications are considered the most vulnerable focus group of the British society.

There is convincing evidence that people with disabilities are less likely to receive sufficient support and care regardless the Disability Discrimination Act and other legislatures that have been established in the country. The highlighted problems reveal serious pitfalls in delivering equal treatment, sustaining a high quality of care practice, and problems with funding plans that have specifically been worked out for disabled children and adults.

In addition to these problems, society experiences such problems as income inequality and poverty contributing to extremely low standards of living. As a result, failure to adhere to legislative act supporting the quality standards of public health cases, as well as inability of the government to cope with the societal problems provides a strong framework for discussion.

The emergent problems need to be addressed from the perspective of treating the most vulnerable members of society, as well as define to what extent the unemployment rates and low-income rates influence the accomplishment of social polices in Britain.

Development of Social Policy and Welfare in Contemporary Britain

In the context of the above-highlighted problems, the public care and social policies in Britain are under the primary focus. Evidence shows that many hospitals lack sufficient care and training programs that would make health care available for regardless of social status and physical abilities.

However, the contemporary situation fails to meet the established goals due to the insufficient funding, and lack of consistent action plan, and absence of specialized orientation for treating old people, as well as adults with learning disabilities. Despite the establishment of many social programs, such as Valuing People, much concern with social welfare in the country remains. According to the Valuing People program, health care professionals, as well as public officer, should follow three main principles.

First, they should assist the most vulnerable members of society in leading an independent life. Second, they should respect people’s rights, regardless of their social position and income. Finally, they should provide people with choice in terms of the type of care they need to get (The Secretary of State for Health by Command of Her Majesty, 2001).

For certain reasons, some of the hospitals, particularly the establishment located in Cornwall failed to provide sufficient care and treatment to people with learning disabilities (Healthcare Commission, 2006). The investigation has also revealed the case of maltreatment and abuse practiced by poorly trained professionals.

Such a situation has been caused by serious pitfalls in human resource management on the part of the top leaders of the hospital. In addition, lack of plans of care does not provide professionals with accurate data concerning which tools and instruments should be used to cope with people with specific needs (Healthcare Commission, 2006). Moreover, lack of records, as well as full mismatch with Valuing People program enabled the investigators to introduce solid measure to prevent the problem in future.

The root of the problem goes deeply into the historic background of social policy formation. Specifically, the committee on Social Insurance and Allied Services (SIAS) introduced a survey on the analysis of existing social schemes tackling the problems of workmen’s compensation, social security, social insurance, and provisional rates (Beveridge, 1942).

At this point, Workmen Compensation Act of 1897 and Unemployment Act of 1934 need to be reconsidered with regard to the result obtained from the survey (Beveridge, 1942). Specific attention was also given to the analysis of medical treatment development, welfare of children, cash benefits for funerals and maternity, and voluntary provisions for a range of contingencies.

The above-presented historical background provides a better picture of necessary improvements, changes, and programs that need to be implemented in the future. Careful reconsideration of healthcare polices and public welfare strategies are indispensible to increasing the quality of living standards.

The Policy Environment in Which Decision Making in Care Practice Takes Place

Introducing new policy environments changes health care vision on the welfare of the public sector. Hence, because public influence on governmental polices in Britain is minimal, the policy environment is largely controlled by the political reforms introduced in the British Parliament (Kavanagh et al., 2006, p. 3).

Within these context, the emergence of pressure group have provided a key platform by means of which civil society is able to participate in political discourse and influence decision making. The so-called liberal democracy prevailed in the British society creates a number of contradictions.

First, interest groups offer a channel of political communication between society and government. Second, the groups can be connected with sectionalism that does not contribute to the general welfare of society (Kavanagh et al., 2006). Despite the presented controversy, the presence of pressure groups signifies the inability of the government to meet the needs and concerns of various social groups. Therefore, the emerged confrontation influences significantly the overall decision making in the sphere of health care practice.

Regardless of the current pitfalls, the presence of various interest groups allows the government tracks the changes within the society and introduces reforms that would improve the overall situation. This is of particular concern to health care practice where active engagement of insider groups into decision-making makes the government take these interventions while making policies in a particular field (Kavanagh et al., 2006, p. 8).

Hence, insider groups constitute an important resource for good information, as well as a basis for good administrative cooperation between health care field and the British government. In addition, because pressure groups operate at national and local levels, the authorities often rely on their concerns and try to identify the issues that can solve the problem.

The policy environment created as mixture of governmental and public initiatives is quite complicated due to the presence of various aspects, such as public objectives, functions and roles, and the extent of political participation at a particular level (Kavanagh et al. 2006).

Apart of positive benefits of the emergence of pressure groups, as well as governmental participation, the policy decisions often produce a number of problems. Specifically, a narrow-focused orientation on increasing the income of the citizens does not provide the country with sufficient level of living.

The Link between Legislative Development and Social Policy and Their Impact on Care Practice

To introduce improvement in administrative field and public sectors, the UK government has a complex system of stage of parliamentary bill. These stages are imperative for considering and evaluating the purposefulness of a specific policy (House of Commons Information Office, 2008).

Because the House of Commons is a democratically elected wing of the Parliament of the United Kingdom, “the purpose of pre-legislative scrutiny is…to make better law by improving the scrutiny of bills and drawing the wider public more effectively into the Parliamentary process” (House of Commons Information Office 2008, p. 3). The process of Bill presentation and acceptance also takes places in the House of Lords (2008).

Both procedures are pivotal because they have a potent impact on the development of legislature in the sphere of care practice as well. The scrutiny of the parliament bill adoption has also been applied to the consideration of the Health and Social Care Bill that aims to protect future of the National Health Service in the United Kingdom and cope with the challenges it experiences to avoid the current problems and surpass the consequences of future crisis (Department of Health 2011).

In addition, the bill seeks to introduce innovations, develop a patient-centred approach and establish a new focus on health care in the public sector. As it can be seen, a systematic and multistage examination of specific principles creates favourable conditions for issuing bills in care practice field.

On the one hand, the complex procedures implemented in the UK Parliament introduce transparency and consistency in adopting decisions in various areas of public health care. On the other hand, the government often pursues personal interests in passing specific legal provisions. Besides, the legal assistance has undergone the change, which makes it challengeable for advice agencies and solicitors to deliver free services.

In this respect, there is a strong necessity to improve and promote access to civil legal assistance. Despite certain measures were taken to solve the problem, the sectors still needs substantial improvements to ensure wider accessibility of the civil legal services (p. 16). Due to the fact that the legal aid service has been practiced for 60 years, constant changes are indispensible for sustaining a favourable development of health care services, as well as advancing the quality standards in medical institutions.

With regard to the above-highlighted problems, the government should present a new vision for reforming public services and creating a consistent framework of high-quality social care that would meet the requirements of the current century. The shits in communities and populations imply that society become much more diverse and, therefore, the existing services should take that into the deepest consideration the demands of the contemporary people.

The Role of the Theoretical Approaches to Welfare in Determining the Shape of Care in the 21 st Century

The social welfare of society can be achieved through the establishment of efficient theoretical approaches that would provide a framework for action plans. The development of social theories takes it roots in the nineteenth century, when Marx made signification contributions to exploring political and economic situations leading to poverty, high unemployment, and social inequalities (Carling, 1991).

According to Marx, the inequality lies in the extraction of the surplus value from the product produced by the employees. In fact, the employer is entitled for the creating the value and, therefore, the surplus-value they get is theft unless it is distributed among the employees.

Therefore, capitalistic system does not contribute to social equality and welfare. In order to reach the freedom and equality, there should be an equal distribution of function among the people where labour power should be given especial importance. Looking at the future development, capitalism does not provide efficient approaches contributing to the welfare of the twenty-first century’s society.

Within the perspectives of the modern development, the functionalism theory can be developed. Greater awareness of social roles that each person should take can strengthen the social structure and provide a consistent framework for building a highly functional society. Considering culturally and social diverse environments is indispensible for defining which roles and functions should each social layer perform to bring in welfare to the twenty-first century’s community in the United Kingdom.

The current problems can be solved by addressing this theoretical approach because it can significantly enhance the work incentives and make the overall social system much simpler (The Secretary of State for Work and Pensions 2010). In addition, increasing costs of governmental support has a negative impact on maintaining welfare and sustaining an appropriate level of care in the public sector.

There are many reasons why functionalism can be effectively applied to improve the above-highlighted problems. To begin with, defining the accurate roles of each social stratum can introduce social order and transparency in making decision and increasing the overall quality of care in the majority of UK hospitals.

With regard to the problem of insufficient funding and lack of education in Cornwall hospitals, the establishment of functionalism theory with the division of labour principle at the core can help the managers understand the problems and take greater control of the existing shortcoming, including maltreatment, abusive attitude to disabled individuals, inappropriate education.

Introducing the so-called collective consciousness and an individual-centred approach to treating patient can strike the balance between personal interest and the interest of the entire community.

The collective behaviour can create shared value consensus that would satisfy all the parties involved into social welfare creation. Health care professionals will gain a greater awareness of the importance of responsibilities they should take in front of the patients with learning disabilities.

A properly arranged social system will make the individuals believe that their duties are accomplished for the sake of their personal interests. At the same time, their contributions will be valuable for the gradual development of the UK society. In addition, members of society should be able to adapt to the various environments, attain to a commonly shared goal, integrate into the goals accomplishment and follow the generally established values.

The quality of health care delivery services in the United Kingdom fails to meet high-quality standards established in the world. This is of particular concern to the hospitals dealing with patients with learning disabilities. The group of patients should be able to receive an equal access to public care, which means that should receive qualified and specialized help from the health care professionals that should be much more advanced than those provided for people with ability to lead an independent life.

The major problem lies in insufficient funding, absence of appropriate recording system, lack of education and training programs for the staff, and absence of action plans. In order to solve these problems, specific legal regulations, as well as theoretical approach should be introduced. At this point, functionalism framework can significantly promote the social welfare in the 21 st century.

Reference List

Beveridge, W. 1942, Social Insurance and Allied Services , His Majesty Stationery Office, London.

Carling, A. H. 1991, Social Division , Oxford University Press, Oxford.

Citizens, Advice Service 2009, Social Policy Impact Report 2009, Citizens Advice Bureau, pp. 1-15.

Department of Health 2011, Overview of Health Care Bill, pp. 1-2.

Healthcare Commission 2006, Joint Investigation into Services for People with Learning Disabilities at Cornwall Partnership NHS Trust , Commission for Healthcare Adult and Inspection. pp. 1-17.

House of Commons Information Office 2008, Parliamentary Stages of a Governmental Bill, Factsheet L1 Legislation Series. pp. 3-14.

House of Lords 2008, Bills and How They Become Law, House of Lords Briefing , pp. 2-6.

Kavanagh, D., Richards, D., Geddes, A, and Smith, M 2006, “Pressure Groups and Policy Networks”, In D Kavanagh, D Richards, A Geddes, and M Smith, British Politics . Oxford University Press, UK.

The Secretary of State for Health by Command of Her Majesty 2000, Valuing People: A New Strategy for Learning Disability for the 21 st Century. Department of Health, Britain, pp. 1-149.

The Secretary of State for Work and Pensions 2010, 21 st Century Welfare, Department for Work and Pensions , pp. 1-52.

• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2020, January 17). Contemporary Issues in Social Policy. https://ivypanda.com/essays/contemporary-issues-in-social-policy-essay/

"Contemporary Issues in Social Policy." IvyPanda , 17 Jan. 2020, ivypanda.com/essays/contemporary-issues-in-social-policy-essay/.

IvyPanda . (2020) 'Contemporary Issues in Social Policy'. 17 January.

IvyPanda . 2020. "Contemporary Issues in Social Policy." January 17, 2020. https://ivypanda.com/essays/contemporary-issues-in-social-policy-essay/.

1. IvyPanda . "Contemporary Issues in Social Policy." January 17, 2020. https://ivypanda.com/essays/contemporary-issues-in-social-policy-essay/.

Bibliography

IvyPanda . "Contemporary Issues in Social Policy." January 17, 2020. https://ivypanda.com/essays/contemporary-issues-in-social-policy-essay/.

• Pitfalls and Dangers of Lack of Staffing and Training
• Extreme Negotiations: Benefits and Pitfalls
• Plea Bargain: Benefits and Pitfalls
• Conditions That Can Give Rise to the Use of Power or Political Behavior in Organization
• Eligibility Rules Used by Social Welfare Groups
• Corporate Social Responsibility: Socialist and Capitalist Perspective
• Policy Network Models and Governance
• Is Majority Rule Democratic?

We use cookies to enhance our website for you. Proceed if you agree to this policy or learn more about it.

• Essay Database >
• Essay Examples >
• Essays Topics >
• Essay on Communication

Social Media Communication Essay

Type of paper: Essay

Topic: Communication , Sociology , Company , Social Media , Media , Community , Activism , Facebook

Published: 03/14/2020

ORDER PAPER LIKE THIS

Social media has been identified as the means for people to interact, create, share, and/or exchange information and ideas in virtual communities and networks. Social media is different from traditional or industrial media. It has become a means of advertisement in such a way that it holds the qualities of permanence, cost, quality, frequency and scope (Katona and Miklos 142). In the 2013, there are social media platforms which became popular including as Twitter, Facebook, LinkedIn, YouTube, Flickr, Tumblr, Instagram, and others. In fact, the total time spent on social media in the U.S. on PC and mobile devices increased significantly for the past years (Katona and Miklos 142). Social media has become the place for activism of adult and youth alike. It will encourage violence within the community because of the reforms they want to institute in the community. Social media has also become a medium of communication to air the grievances of the public by allowing them to make cyber protests. Social media has negative effect since it triggers activism and violence. Digital activism enables the public to communicate their disgust, frustration and annoyance when they see wrongdoings committed by the government or persons in authority (Amin 64). Some of the big protest movements are initiated by social media which results to digital activism that may place a community under siege. There are also come companies who use social media such as Facebook for business purposes. It has become the reason for most of the companies to connect and establish ties with people they know. Studies show that at least 71 percent of the U.S. companies have used (Katona and Miklos 142). On the other hand, 57 percent of these companies used a brand or a vendor. Some of the companies used advertisements and sponsored post to promote their products and services. This will enable the company to know their market reach depending on the number of likes seen as the viewers clicked their pages (Katona and Miklos 142). Many had seen that there has been an increased in the number of companies that had used the social media to create a positive image (Kuvykaite & Piligrimiene 305). However, the nature of social media communication can also pose a possible risk for image of the company if the information is not assessed appropriately. It is important to note that the companies stand to lose their positive image immediately by wrong publicity. It is disappointing that social media can ruin the corporate image and good that took a long time to establish. By closely scrutiny, it can be inferred that communication in social media is more complicated than the other traditional means of communication. Social media can influence consumer response and intercommunication within a particular industry. Hence, there is a high probability for company to control and manage the content of communication that they want to impart to the public. The success of social media communication on image formation is dependent on the information disseminated in social media. Thus, there should be proper management of new technologies and communication channels in order to develop trust and relationship created through social media. In a world that is governed by globalization and competition in the market is growing rapidly and the a person or a company‘s image can be influenced by social media. In effect, social media becomes an important tool to help companies survive in competitive environment. For companies, social media can help ensure the success of the company by improving its performance. Social media can help establish public relations to develop positive image through internet technologies. For companies, they use new channels of communication such as Facebook and Twitter, to enhance and maintain sustain positive image to promote customer trust. For individuals, such as political candidates, they use social media by positive opinion by manipulating their image using Facebook. Social media can also opens opportunities for persons and companies to use various means for positive image development and attracting media attention.

Works Cited:

Amin, Ramtin. “The Empire Strikes Back: Social Media Uprisings and the Future of Cyber Activism:. Kennedy School Review, 10, (2010): 64-66. Katona, Zsolt, and Miklos Sarvary. "Maersk Line: B2B Social Media-It’s Communication, Not Marketing.” California Management Review 56.3 (2014): 142-156. Kuvykaite, R., & Piligrimiene, Z. (2013). Communication in Social Media for the Company’s Image Formation. Economics & Management, 18(2), 305-317 Zailskaite-Jakste, Ligita, and Rita Kuvykaite. "Implementation Of Communication In Social Media By Promoting Studies At Higher Education Institutions." Engineering Economics 23.2 (2012): 174-188.

Cite this page

Share with friends using:

Removal Request

Finished papers: 1015

This paper is created by writer with

ID 281077246

If you want your paper to be:

Well-researched, fact-checked, and accurate

Original, fresh, based on current data

Eloquently written and immaculately formatted

275 words = 1 page double-spaced

Get your papers done by pros!

Other Pages

Flexibility term papers, cotton term papers, tour term papers, wear term papers, fatty term papers, slope term papers, margin term papers, merit term papers, pitcher college essays, hebrews 10 term paper examples, example of terrorism research paper, free essay on more information about affiliation research grants conflict of interest and how, free essay on in group we shrink by carol tavris, the state of union address movie review, free revelations of the americas essay example, sample research paper on wittes economic policy for russia, good example of the obesity epidemic in the united states essay, social inequality essays examples, essay on life liberty and the pursuit of happiness, good example of retail strategy for belk womens department research paper, free essay on the case of murray vs philadelphia eagles, free case study about under armour, the four principal components and concepts of the existential theory are death freedom isolation and meaninglessness of life research paper, jia zhangke research paper samples, purview essays, australian state essays, convertibility essays, alex ross essays, derf essays, goblins essays, first love essays, edward vii essays, juvenile criminals essays, effective deterrent essays, github essays, capitan essays, touchscreen essays, incoherent essays, zwick essays, indecent essays, african descent essays, gross margin essays, breck essays.

Password recovery email has been sent to [email protected]

Use your new password to log in

You are not register!

By clicking Register, you agree to our Terms of Service and that you have read our Privacy Policy .

Now you can download documents directly to your device!

Check your email! An email with your password has already been sent to you! Now you can download documents directly to your device.

or Use the QR code to Save this Paper to Your Phone

The sample is NOT original!

Short on a deadline?

Don't waste time. Get help with 11% off using code - GETWOWED

No, thanks! I'm fine with missing my deadline

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• My Account Login
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 08 May 2024

Estimating and modeling spontaneous mobility changes during the COVID-19 pandemic without stay-at-home orders

• Baining Zhao   ORCID: orcid.org/0000-0002-9653-3316 1 , 2   na1 ,
• Xuzhe Wang 1   na1 ,
• Tianyu Zhang 1 ,
• Rongye Shi   ORCID: orcid.org/0000-0003-4298-9358 3 ,
• Fengli Xu 4 ,
• Fanhang Man 1 ,
• Erbing Chen 5 ,
• Yang Li   ORCID: orcid.org/0000-0002-2053-6393 1 ,
• Yong Li   ORCID: orcid.org/0000-0001-5617-1659 4 ,
• Tao Sun   ORCID: orcid.org/0000-0003-2570-543X 2 &
• Xinlei Chen 1 , 2 , 6  

Humanities and Social Sciences Communications volume  11 , Article number:  591 ( 2024 ) Cite this article

134 Accesses

1 Altmetric

Metrics details

• Health humanities
• Information systems and information technology
• Science, technology and society
• Social anthropology

Comprehending the complex interplay among urban mobility, human behavior, and the COVID-19 pandemic could deliver vital perspectives to steer forthcoming public health endeavors. In late 2022, China lifted its "Zero-COVID" policy and rapidly abandoned nearly all interventions. It provides a unique opportunity to observe spontaneous mobility changes without government restriction throughout such a pandemic with high infection. Based on 148 million travel data from the public bus, subway, and taxi systems in Shenzhen, China, our analysis reveals discernible spatial discrepancies within mobility patterns. This phenomenon can be ascribed to the heterogeneous responses of mobility behavior tailored to specific purposes and travel modes in reaction to the pandemic. Considering both the physiological effects of virus infection and subjective willingness to travel, a dynamic model is proposed and capable of fitting fine-grained urban mobility. The analysis and model can interpret mobility data and underlying population behavior to inform policymakers when evaluating public health strategies against future large-scale infectious diseases.

Similar content being viewed by others

COVID-19 is linked to changes in the time–space dimension of human mobility

Quantifying human mobility behaviour changes during the COVID-19 outbreak in the United States

Practical geospatial and sociodemographic predictors of human mobility

Introduction.

As one of the most infectious pandemics, COVID-19 has resulted in a devastating toll of over 7 million lives lost and an estimated $8 trillion in economic damage (Clark et al., 2020 ; Taskinsoy, 2020 ; Weiss et al., 2020 ; Zhang et al., 2022 ). Research on the COVID-19 pandemic and its impact holds the potential to offer valuable insights for addressing unforeseen large-scale, highly infectious diseases in the future (Chen et al., 2022 ; Menkir et al., 2021 ; Sibley et al., 2020 ).

Due to rapid urbanization, more than 55% of the global population resides in urban areas which serve as a hotbed for infectious diseases due to dense population (United Nations, 2018 ). It is imminent to study the response and recuperation of urban mobility during the influence of COVID-19 (Arellana et al., 2020 ; Atkinson-Clement and Pigalle, 2021 ; Gkiotsalitis and Cats, 2021 ; Levin et al., 2021 ) to facilitate understanding of the spread of the pandemics (Chang et al., 2021 ; Wei et al., 2021 ). Simultaneously, the change in urban mobility profoundly influences economic dynamics and mental health outcomes, among other societal aspects (Wang et al., 2021 , 2022 ). Existing research has primarily investigated how governments proactively implement mandatory or advisory stay-at-home orders to change people’s mobility behavior and contain virus transmission (Martínez and Short, 2021 ; Shen et al., 2020 ; Tirachini and Cats, 2020 ; Wang et al., 2022 ; Zhang, 2021 ). However, there is limited knowledge regarding the spontaneous change in citizens’ mobility behavior during the COVID-19 pandemic. The spontaneous change consists of individuals’ voluntary adaption of their travel behavior concerning the infection rate of disease and perceived threats, without the restriction of government policies (Han et al., 2021 ). In the case of COVID-19, the self-driven behavior involves engaging in home-based care while infected, limiting travel to essential trips only, avoiding crowded places, shifting on-site work to remote work to reduce potential exposure to the virus, etc. (Balmford et al., 2020 ; Tisdell, 2020 ). A better understanding of these spontaneous mobility changes can provide valuable insights into the necessary mobility behavior of the citizens and in turn facilitate the design of urban mobility-related policies during pandemics.

In this paper, we investigate spontaneous mobility changes without stay-at-home orders throughout a highly infectious pandemic, from its emergence through large-scale proliferation to eventual stabilization. The mobility changes are manifested as fluctuations in the number of individuals traveling, which are microscopically composed of varying purpose-specific mobility behavior. We aim to answer the following research questions:

Q1: At the temporal and spatial scale, how does urban mobility evolve in response to a pandemic’s lifecycle?

Q2: Given distinct regions corresponding to varied functional zones and data pertaining to various modes of transportation within a city, what is the underlying mobility behavior, and how do they react to the pandemic?

Q3: How to establish a dynamic model to deduce the spatio-temporal mobility changes of the entire city based on the behavior of different travel purposes and modes?

To answer these questions, we utilize origin-destination (OD) mobility data involving 148 million occurrences before and after the cancellation of the "Zero-COVID" policy in Shenzhen subway, bus, and taxi systems, China, as shown in Fig. 1 a, b. The OD data logs the time and quantity of individuals traveling from areas proximal to the origin to regions near the destination in the urban public transport system. Such data encapsulates the spatio-temporal mobility of urban populations, focusing solely on the volume of individuals moving between two regions, while avoiding the disclosure of individual-specific information. This ensures a high degree of privacy preservation.

a Trips in the Shenzhen bus, subway, and taxi systems from December 8, 2022, to January 6, 2023. China adhered to a rigorous "Zero-COVID" policy and implemented strong non-pharmaceutical measures from the beginning of the COVID-19 outbreak until early Dec. 2022. As a result, over 99% of the population in China had never been infected by any variant of SARS-CoV-2. Even though China heavily distributed the COVID-19 vaccine, the vaccine merely prevents people from serious symptoms without comprehensive immunity due to the fast variability of COVID-19. On December 7, 2022, the "Zero-COVID" policy, along with the travel restrictions, was rescinded. The travel volume immediately returned to a level approximating normalcy. Thereafter, the spread of SARS-CoV-2 precipitated a substantial decrease in urban mobility. A progressive recovery would then succeed. To highlight the impact of the pandemic more effectively, we conducted normalization separately for weekdays, Saturdays, and Sundays, ultimately showcasing the trip rate. b The lines bridging the origin and destination maps symbolize the human movement between two corresponding regions. Due to the dense nature of OD data, we depict flows exceeding a daily average of 200 for clarity. Three time periods are highlighted: before the COVID-19 pandemic outbreak, during the peak impact, and during the gradual stabilization period. These periods correspond to three different levels of mobility.

The period before and after the cancellation of the "Zero-COVID" policy provides a unique opportunity to observe the spontaneous evolution of urban mobility without mobility-restricting measures, facing the pandemic spreading on a large scale in a short period. The "Zero-COVID" policy focuses on promptly identifying and containing localized outbreaks using advanced technologies such as big data analysis and nucleic acid screening (China CDC, 2022 ). Through strict quarantine measures, identification of infection sources, and high-risk group identification, China aims to swiftly end outbreaks with minimal societal and economic impacts. China’s implementation of the "Zero-COVID" policy has been highly successful. Despite its massive population of 1.4 billion people, the country has reported relatively low numbers of COVID-19 cases and deaths before the cancellation of the "Zero-COVID" policy. Impressively, China has recorded 1,655,477 cases (less than 0.2% of the population) and 13,524 deaths (less than 0.1%% of the population) (Burki, 2022 ). In late 2022, the Omicron variant of SARS-CoV-2 became predominant worldwide and it was known for its high transmissibility (basic reproduction number ≈ 9.5) and penetration rate. Yet, compared to previous variants, it also demonstrated a relatively lower infection fatality rate (<1%) (Liu et al., 2022 ). Under such a circumstance, on Dec. 7, 2022, China lifted the "Zero-COVID" policy along with the travel restrictions. However, the Omicron variant showed a high rate of immune escape from vaccines which merely prevented people from serious symptoms without delivering comprehensive immunity. Therefore, the lift of the policy led to a rapid and massive spread of the Omicron variant of SARS-CoV-2. It is estimated that within a month, the proportion of infected individuals in cities sharply escalated from nearly zero to surpassing 70% (Leung et al., 2023 ). Besides, Shenzhen is a megacity with a permanent population exceeding 17 million individuals and a population density reaching 8,800 individuals per square kilometer (Shenzhen Government, 2023 ). The research on mobility behavior during the pandemic in Shenzhen is representative and informative.

An analytic framework and a dynamic model are proposed with the following key designs. In terms of spatial distribution, mobility in central business districts (CBDs) and their adjacent areas is significantly impacted. The mobility changes can be quantitatively represented by a time series and evaluated by features about the decline and recovery of trips. To explore the spatial disparities, four mobility patterns are found among the mobility changes corresponding to various OD pairs through the K -means++ clustering algorithm (answering Q1 ). We overlay an urban land use (ULU) map on origins and destinations, thus elucidating the intent behind mobility behavior, such as commuting, recreation, schooling, and more. The differences in mobility changes among subways, buses, and taxis are also discussed (answering Q2 ). Inspired by this, we try to devise a dynamic model of changes in passenger flow, predicated on the impact of COVID-19 on different mobility behavior. We model distinct mobility behavior by combining physical infection and the willingness influence, which subsequently deduces the effects of ULU embodying travel intentions. As a result, we can infer the mobility within the geographic regions housing these lands with different uses (answering Q3 ). Our model allows us to not only fit observed changes in trips but also to conduct detailed analysis at a granularity of less than 500m. We believe the proposed insight and model could be leveraged to provide public health officials with a holistic recommendation as they decide on mobility-related policies under similar pandemic conditions.

Spatial distributions of urban mobility level

The spatial distribution of the decline and recovery in mobility levels can be approximately observed based on Fig. 2 . This observation is made at the granularity of a 1km × 1km grid level. For subways, buses, and taxis in a grid, the initial travel volumes for departures and arrivals are aggregated and compared with the passenger flow from the pre-pandemic period to assess the level of mobility. It is observed that the mobility in CBDs was more significantly impacted by COVID-19 compared to areas farther from the CBDs.

a The departure and arrival passenger flows for each region were aggregated and then divided by their pre-pandemic values to ascertain the mobility levels in each area. Notably, Day 15 was observed to have one of the most significant reductions in passenger flow. Day 7 marked a period of decline, whereas Days 21 and 28 were identified as recovery phases. Furthermore, a disparity in the impact on mobility levels was observed between the CBD and its adjacent areas compared to regions located further from the CBD. b The spontaneous mobility changes consist of the reduction, trough, and recovery stages. These three stages are quantitatively measured and a set of corresponding features are formed, as listed in Table 1 .

CBDs are typically characterized by high density, both in terms of population and infrastructure. This density leads to more congested public spaces and transportation systems. During the pandemic, such conditions increased the risk of virus transmission, leading to more stringent movement restrictions and a greater reluctance among the public to travel in these areas. Furthermore, this may be related to the attributes of land use, which will be discussed in the section “Population behavior behind mobility patterns”.

Identifying mobility patterns

To investigate mobility changes at a finer granularity (street level), bus and subway data were processed based on station information and passenger flow, while taxi data were analyzed by urban areas (see Methods for a detailed description of the data). The OD data derived from the public bus, subway, and taxi systems in Shenzhen include both spatial and temporal information on urban mobility. In the bus system, bus routes establish connectivity between two distinct urban regions corresponding to the origin station and destination station, and passenger flow is influenced by the gradual spread of the epidemic, resulting in temporal variations. This principle is similarly applicable to subway and taxi systems.

Following the large-scale outbreak of the COVID-19 pandemic (post-December 7, 2022), the mobility for almost all OD pairs swiftly plummeted from pre-pandemic levels to a markedly low volume, eventually showing signs of gradual recovery, as shown in Fig. 2 . This change in trips can be delineated via a temporal variation curve for each OD pair, as shown in Fig. 3 a. We normalize each time series of OD passenger flow using historical data (i.e., typical passenger flow levels before November 25, 2022) and subject the data to preprocessing.

a This time series chart shows the changes in OD trip rate during the period from December 8, 2022, to January 6, 2023. The clustering algorithm identifies four distinct groups with different mobility patterns of decline and recovery. The middle line in each plot represents the average trip rate for each cluster. b Through the spatial attractiveness of ULU, we can infer prominent mobility behavior under each cluster. The changes in mobility are related to mobility behavior between different origins and destinations. c indicates the total impact on the overall passenger volumes of subway, bus, and taxi services.

To quantify the initial decline and subsequent recovery in mobility during the COVID-19 pandemic (Fig. 2 ), we establish a set of features to assess how trips evolve, as listed in Table 1 . The concept of resilience is about how the system responds to disturbances (Qian et al., 2022 ; Schwarz, 2018 ; Standish et al., 2014 ; Tabatabaei et al., 2018 ): does it withstand the shock and remain unchanged, does it adapt and transform into a new state, or does it collapse (Forzieri et al., 2022 ; Kumpfer, 2002 ; Zhao et al., 2021 ). Inspired by this, we concentrate on the resilience of mobility to the pandemic—a characteristic of mobility decreasing and rapidly recovering from large-scale infectious diseases. Features are devised to quantitatively measure the three stages—reduction, trough, and recovery— capturing the mobility changes throughout the COVID-19 pandemic.

Once the features of changes in mobility for OD pairs are extracted, the mobility trends for different modes of transportation can be uniformly analyzed. Based on the aforementioned properties, we employ unsupervised machine learning technology, i.e. K-means++ clustering algorithm (Arthur and Vassilvitskii, 2006 ), to discern the similarities and disparities in passenger flow among various OD pairs (see Methods for detail). As depicted in Fig. 3 a, the K -means++ algorithm categorizes the OD trip trends into four distinct patterns.

The OD mobility in Cluster 1 exhibits the most notable decline, with a total magnitude of impact of -13.47(%  ⋅  days), as presented in Table 1 . In the initial phase of the COVID-19 pandemic, a precipitous decrease in passenger flow was observed, with the average value plummeting to less than 50% by the 12th day. While the subsequent descent in passenger flow exhibited a slower pace, a persistently low level or downward trend was maintained. Certain OD passenger flows reached a standstill till the conclusion of the statistical period. This suggests a high degree of travel flexibility within this cluster, or significant aversion to infection, implying that this demographic endeavors to avoid travel throughout the pandemic. Furthermore, the resurgence of travel intent tends to be protracted, with a near-zero recovery rate in the short term.

Cluster 2 exhibits a pronounced U-shape curve with a comparable decline rate, as shown in Fig. 3 a. The nadir of OD passenger flow appeared around the 15th day, hovering at approximately 50%. Thereafter, the travel volume exhibited a gradual resurgence, culminating in an average travel volume recovery of 82.2%. The graphical representation illustrated a significantly slower pace of travel recovery in the pandemic’s later stages, in contrast to the rapid decline observed during the initial phases. The mobility behavior characterizing Cluster 2 aligns with the epidemiological patterns of infection and recovery. During the initial phase of the COVID-19 pandemic, citizens refrained from traveling, either due to active infection or as a preventive measure against contagion. However, as the infection peaked and those infected began to recover, travel volume followed a trajectory of gradual recovery. This pattern is representative of the majority of mobility behavior.

The mobility change curve for Cluster 3 also exhibits a U-shaped pattern; however, its recovery speed significantly outpaces that of Cluster 2. Following the onset of the pandemic, there was a rapid decrease in passenger flow, averaging approximately a 48.8% reduction. Contrasting with the sluggish recovery observed in Cluster 2, the resurgence pace in Cluster 3 mirrors the speed of passenger flow decline experienced during the reduction stage. Consequently, by the conclusion of the observation period, the OD mobility level essentially reverted to its pre-epidemic benchmark. In the end, the mobility behavior corresponding to this cluster rapidly surmounted the impacts of the COVID-19 pandemic, facilitating, and even accelerating, the return to pre-epidemic levels.

The mobility patterns of OD bus stations in Cluster 4 are minimally impacted by the COVID-19 pandemic, with the total impact merely a third of that experienced by Cluster 1. The corresponding curve demonstrated minor fluctuations, with a peak declining amplitude of 38.2%. Across the tripartite stages of mobility alteration (reduction, trough, recovery), the pandemic’s impact on passenger volume was relatively insubstantial. This suggests that within this cluster, the corresponding origins and destinations exhibit robust travel demand, complemented by relatively inflexible mobility behavior.

Population behavior behind mobility patterns

The two crucial facets of population behavior are where to go and how to get there. Consequently, we delve into discussions on insights regarding travel purposes and modes during the processes of mobility decline and recovery.

Travel purpose

Mobility between two areas is aggregated from population behavior with various travel purposes. Lands with specific uses around each origin and destination have the potential to reveal the land-use characteristics of the places where passengers most visit (Chang et al., 2021 ; Sun et al., 2007 ). Therefore, the integration of OD data and the ULU map has the potential to reveal the travel purposes behind OD trips. For example, if a passenger departs from a station (or pick-up point of a taxi trip) surrounded by lands with the category of residential and alights at lands with the category of the company at night o’clock, it can be inferred that the passenger is most likely commuting to work. Therefore, the ULU categories clearly indicate people’s travel purposes.

More generally, we establish a ULU feature vector to represent the probability of departure or arrival at each nearby urban land category (Xing et al., 2020 ). We have collected nine common types of ULU information from Gaode Maps and Baidu Maps, including the residential, company, commercial service, transport hub, college, school, hospital, cultural/sport, and park/scenery. The OD pairs are mapped to OD land pairs according to the corresponding ULU feature vectors (see Methods for details). We further delineate the primary ULU visited and the key travel objectives within each cluster, as presented in Fig. 3 b.

Travel behavior to schools is mainly classified within Cluster 1 and Cluster 2. Following COVID-19’s onset, passenger flow experienced a precipitous decline, with no short-term recovery trend in sight. Firstly, the health of minors is often perceived as more vulnerable, prompting parents to exercise added caution in safeguarding their children from the COVID-19 virus (She et al., 2020 ). Parents likely prefer to refrain from sending their children to school, owing to concerns regarding viral transmission. Subsequently, as infection rates escalated, schools transitioned to online or remote learning modalities (Betthäuser et al., 2023 ). This shift obviated the need for students, teachers, and staff to commute daily, thereby perpetuating the decrease in passenger flow. The passenger volume at school-associated stations was anticipated to remain low until the pandemic stabilizes. As for colleges, numerous students were sent home ahead of the lifting of the "Zero-COVID" policy, resulting in a significant decline in passenger flow.

The majority of trips from residential, company lands to park/scenery, cultural/sports lands are concentrated within clusters with a more significant impact on mobility. Analyzing travel sentiments reveals that these journeys are deemed non-essential, and the inclination to embark on them significantly diminishes following the COVID-19 pandemic outbreak (Han et al., 2021 ). Given the increased risk of COVID-19 infection, older populations exhibit heightened caution when using buses and visiting crowded locations, resulting in highly affected resilience. After contracting COVID-19 (Wang et al., 2022 ), individuals experience compromised systemic and pulmonary functions (Mulcahey et al., 2021 ), leading to a reluctance to engage in strenuous physical activity at sports halls for a short duration. Consequently, there was a diminished recovery in passenger flow to sports halls. Although there is no lockdown ban, citizens avoid visiting by bus and maintain social distance.

Intriguingly, despite the overarching epidemic conditions, passenger counts for the transport serving major transport hubs such as airports, train stations, and ferry terminals exhibit comparatively minor reductions and rapid recovery, mainly aligning with the trends of Cluster 3 and Cluster 4. The relative stability of these numbers can largely be attributed to the inherent nature of long-distance travel across cities. Unlike short-range transit, which may be supplanted by walking, cycling, or personal vehicles, alternatives for long-haul journeys are notably limited, thereby maintaining a baseline demand for buses servicing these transport hubs even during the height of the COVID-19 pandemic.

The alterations in mobility patterns concerning residential—hospital are primarily observed within clusters characterized by a comparatively modest reduction in mobility. The OD trips exhibited fluctuation, characterized by a decline and a gradual recovery within 30 days. In the initial stages, individuals with chronic conditions are advised to minimize hospital visits to lower their exposure risk to COVID-19. However, the pandemic triggered an escalation in healthcare demand, as numerous individuals sought medical care, testing, and treatment (Birkmeyer et al., 2020 ; Peiffer-Smadja et al., 2020 ). The heightened need for infectious disease services counterbalanced the reduction in visits to other outpatient clinics in hospitals, thereby contributing to the recovery of passenger flow towards hospitals. Moreover, the surge in hospital visits necessitated that healthcare workers and other essential personnel continue to commute to and from hospitals, irrespective of the pandemic situation. Their unwavering travel patterns help maintain a basic level of passenger flow of over 40% trip rate.

The behavior disparity between residential—company exhibits various patterns, with a significant proportion found in various clusters. Overall, they are relatively less affected, serving as primary driving factors for urban mobility recovery. It is possibly related to the various industries of companies. On one hand, the characteristics of various industries significantly influence the patterns of public transport use. Employees in industries of internet technology and electric communication demonstrate greater adaptability to remote work during the COVID-19 pandemic. Due to the pressing demand for healthcare products, drugs, and research, stations located near pharmaceutical companies are expected to experience a faster recovery in passenger flow. The nature of manufacturing and factory work typically requires on-site participation, rendering remote work impractical. On the other hand, income levels within industries also influence travel patterns. Employees in the internet and telecommunications industries tend to have higher incomes and have more flexibility in choosing their transport modes, potentially opting for private vehicles over public transport during the COVID-19 pandemic to reduce exposure risks. In contrast, lower-income employees might be more reliant on public transportation to commute to their workplaces, maintaining the demand for buses. The income disparity between industries further contributes to the observed differences in passenger flow trends during the pandemic.

Travel behavior involving commercial service falls within Cluster 3 and Cluster 4. The effect on the service industry essentially aligns with the proportion of the population infected with the virus. Following about three years of "Zero-COVID" policy, the associated panic has largely dissipated. As people yearn to return to normalcy and recreational activities, customer flow at shopping malls stages a swift recovery in later phases. This observation appears comprehensible, given the fundamental nature of work activities as a pivotal component of economic endeavors. With the resumption of work activities, a consequential surge in demands for commercial activities is anticipated (Ma et al., 2023 ).

Overall, during the COVID-19 pandemic, commuting, commercial, and healthcare demand constituted the largest components of urban mobility. It is crucial to maintain and promptly restore the supply of buses among communities, transport hubs, hospitals, and companies. Integrating ULU data with OD data enables governments and transport operators to thoroughly analyze and elucidate shifts in mobility behavior during the pandemic. This comprehensive approach offers a solid foundation for well-informed policy development and implementation against unforeseen pandemics with high infection and low case fatality rates.

Travel mode

The total impact for the overall passenger volumes of subway, bus, and taxi services are computed, as presented in Fig. 3 c. The data indicates varying degrees of reduction in passenger flow, with subways (−9.42%  ⋅  days) experiencing the most significant decline, followed by buses (−8.63%  ⋅  days), and taxis (−7.90%  ⋅  days). This trend can be primarily attributed to the perceived risk of COVID-19 transmission in different transportation environments and the adaptive responses of urban populations to the pandemic.

Subways, typically characterized by high passenger densities and closed environments, represent the apex of perceived transmission risk. The significant drop in subway usage can be attributed to people’s avoidance of crowded spaces and potential virus hotspots. Furthermore, the role of subways as connectors of various urban hubs rendered them particularly vulnerable to reduced usage as individuals sought to minimize travel and potential exposure to the virus. In contrast, buses, while also experiencing a notable decline in passenger flow, were marginally less impacted than subways. This difference might be due to the varied nature of bus routes, some of which cater to essential travel less feasible via other means, and the slightly lower passenger densities compared to subways.

Taxis, offering more individualized and controlled travel environments, demonstrated the least reduction in passenger flow. This trend suggests a public preference for modes of transportation perceived as safer and less conducive to virus spread. Nonetheless, the overall decline in taxi usage reflects broader patterns of reduced mobility, driven by lockdown measures, the shift to remote work, and heightened public health awareness. Economic factors also played a role, as the financial impacts of the pandemic might have influenced individuals’ transportation choices, with taxis being a costlier option compared to public transit. Collectively, these observations underscore the multifaceted impact of the COVID-19 pandemic on urban transportation, shaped by an interplay of health, lifestyle, and economic considerations.

Dynamic model of urban mobility

We develop a dynamic OD mobility model to quantitatively simulate the fine-grained impact of the COVID-19 pandemic on OD passenger flow. The mobility changes can be ascribed to the rate of COVID-19 infection and the willingness to travel. We employ an epidemic transmission model, the susceptible-infectious-removed (SIR) model (Cooper et al., 2020 ; Keeling and Eames, 2005 ), to simulate the rise in infection cases in the city. In general, people who got afflicted with COVID-19 would recuperate at home for days, leading to a plunge in overall city-wide human mobility flow. However, some patients might choose to crowd into the hospitals which leads to an unusual increase in travel needs between urban lands. Therefore, we establish the willingness factors to represent the emotional effects of the COVID-19 pandemic on various types of mobility behavior. The disparity in OD passenger flow under high spatial granularity can be elucidated by the travel purposes and travel modes (refer to Methods for details).

Specifically, people in the dynamic OD model have three distinct states: usual (U), infectious (I), and recovered (R). (Fig. 4 ). In contrast to the classic SIR model, the usual state in the proposed model refers to passengers who remained uninfected by the COVID-19 pandemic, with unaffected travel willingness. Infectious passengers encompass those who have contracted the COVID-19 virus or have been driven by panic to abstain from bus travel. Over time, if affected passengers decide to resume travel, they transition out of the recovered state. The model has merely four free parameters that scale: (1) transmission rate of the COVID-19 pandemic, (2) recovery rate of the COVID-19 pandemic, (3) willingness factors for transmission, and (4) willingness factors for recovery; all four parameters persist as constants over time. The first two parameters, which are determined by the infectivity and virulence of the epidemic itself, remain consistent for all OD pairs. The latter two categories of parameters are determined by travel purposes, which can be inferred by the ULU surrounding OD bus stations.

The model inputs encompass the pandemic’s transmission and recovery rates, as well as people’s willingness factors. Furthermore, the model requires the ULU information near bus stations and the pre-outbreak historical passenger flow levels. In the model, the passengers have usual (U), infectious (I), and recovered (R) states. We fit models to all OD pairs in bus, subway, and taxi systems, which shows full model fits of four clusters corresponding to Fig. 3 . The blue line represents the model predictions. As the trips of the OD passenger flow tend to have great variability, we also show the smoothed average (line of a different color from blue). Shaded regions denote the 2.5th and 97.5th percentiles across stochastic realizations. The green dashed line represents the predictions without considering willingness factors (In this case, the predictions for all clusters are consistent). We sample 100 parameter sets of willingness factors and perform stochastic realizations for each set.

The willingness factors serve as a measure of how much the panic mentality contributes to the decline and recovery in mobility. They reflect the degree of anxiety and perceived risk associated with traveling during the COVID-19 pandemic. For example, as the number of infectious passengers increases, other passengers perceive a higher risk of infection when taking the subway, which leads them to seek alternative means of transport or avoid traveling altogether. This corresponds to a negative willingness factor. Conversely, the surge in public health demand during the COVID-19 pandemic leads to positive willingness factors associated with medically related travel. The willingness factors collectively illustrate the various responses of different travel purposes to the COVID-19 pandemic.

Our model precisely matches the observed trips of OD pairs in Shenzhen from December 8, 2022, to January 6, 2023, as shown in Fig. 4 . As shown in Supplementary Fig. 1 , the average values of willingness factors correspond to the analysis of mobility patterns in section “Population behavior behind mobility patterns”. For example, the value of the willingness factor for recovery associated with the travel behavior of school-age children exhibits a significantly negative trend, indicating a slow recovery in their travel activities. This observation aligns well with the predominant mobility pattern observed within Cluster 1. The willingness factors for the recovery of “residential-transport hub” and “company-transport hub” are the two highest positive values, indicating that there are fewer viable alternatives for long-distance travel, and as a result, passenger flow is less affected. The distinctions between subway, bus, and taxi services can also be discerned from the willingness factors.

At a micro level, we can forecast the decline and recovery of mobility between any two stations or taxi-operating regions in Shenzhen. At a macro level, our model precisely captures the average mobility changes for the four clusters (Fig. 4 ). The dynamic OD model enables even a relatively straightforward UIR model to accurately fit observed passenger flow, despite mobility behavior during that period. This model offers valuable insights into urban mobility during potential future outbreaks of infectious diseases with high infection, empowering policymakers to deduce alterations in urban mobility and population behavior during the initial stages of an epidemic outbreak.

Although COVID-19 has become a familiar presence in our lives, it continues to pose a significant global threat. As we write this paper, the virus still claims a life every three minutes (WHO, 2023 ). The suffering and the painful lessons learned from the COVID-19 pandemic must not be in vain. The analytical framework and model proposed in this research contribute to the long-term management of the COVID-19 pandemic and offer tools to confront potential future viral epidemics.

Based on 148 million travel records, this paper examines the fine-grained spatio-temporal characteristics of urban mobility during the COVID-19 pandemic. It integrates these characteristics with an urban land use map to elucidate the heterogeneity in the decline and recovery of mobility. In terms of travel purposes, trips originating from schools and colleges experienced a sharp decline attributable to class suspensions and risk aversion to infections. Non-essential travel, such as visits to museums, sports halls, and parks, saw a substantial decrease and a gradual recovery. In contrast, travel associated with commuting, commercial services, and healthcare exhibited relatively modest declines and a quicker rebound. Regarding transportation modes, the most affected to least affected were subway, bus, and taxi. Subsequently, the UIR model with willingness factors is built, which comprehensively captures the influence of COVID-19 on travel willingness. In contrast to previous research that primarily focused on macro-level mobility analysis, we propose a set of indicators to measure the temporal variations in urban mobility and uncover the heterogeneity of mobility changes through clustering. There were rare urban mobility models at the block level encompassing various modes of transportation.

Our dataset has limitations as it does not encompass all modes of urban mobility. The model is also minimalist, neglecting regional differences in epidemic spread. Nevertheless, our findings are valuable for revealing spatial differences in the decline and recovery of travel. The proposed analytical framework and dynamic model are adaptable and extensible, which can be applied to different pandemics of other cities or other travel modes, capturing detailed aspects of real-world urban mobility.

This paper offers potential implications for future application and research aimed at formulating more targeted and effective public health policies and strategies. In the context of evaluating public health policies, our results can guide policymakers in assessing the opportunity cost of urban mobility limitations during large-scale pandemic outbreaks. The dynamic OD urban mobility model provides a benchmark for mobility changes without mobility-restricting intervention, enabling the precise evaluation of the impacts on the transmission rate and economic productivity. Additionally, in the realm of policy formulation, policy decisions can influence mobility behavior by altering travel willingness and emotions associated with different POI types, significantly affecting the outcome of the epidemic infection. Improved paid leave policy or income support can reduce mobility for essential staff during illness. Strengthening hygiene and disinfection, maintaining proper ventilation, and increasing routes between origins and destinations of high travel demand contribute to enhanced passenger safety. Lastly, in terms of future academic research, fusing large language models with computational, interactive agents seems to be a path to realistic simulations of human behavior (Xu et al., 2023 ). Based on emerging urban sensing technologies (e.g., drones (Wang et al., 2022 ) and crowdsensing (Chen et al., 2019 ; Guo et al., 2021 ; Xia et al., 2023 ), diverse and heterogeneous human behavioral data are continuously mined. Integrating travel behavior and large models to construct urban individual profiles for simulating travel behaviors appears to be a promising direction. Additionally, we will continue to investigate the changes and resilience of urban elements when faced with restricted urban mobility, including variations in air pollution (Chen et al., 2020 ), unmanned delivery (Chen et al., 2022 ), and emergency communication (Ren et al., 2023 ).

Origin-destination data of urban mobility

Thanks to the extensive deployment of mobile devices (Chen et al., 2020 ), the travel data are collected by a public transportation service company in Shenzhen. The original data, gathered by 663 bus routes, 16 subway routes, and 17,826 taxis during operation, total comprises approximately 148 million trips. Through advanced data collection (Li et al., 2022 ) and processing techniques (Chen et al., 2018 ), aggregated OD data can be obtained. The data collection period for bus-related information spans from October 15, 2022, to January 6, 2023, while that for subway and taxi data extends from November 1, 2022, to January 6, 2023. The OD data for buses has a shape of (3010, 3010, 98), where the first and second dimensions correspond to 3010 bus stations representing origins and destinations, respectively, and the third dimension represents dates. Similarly, the OD data for subways has a shape of (240, 240, 67) with a similar interpretation. The original data for taxis consists of individual trip records, including vehicle ID, pick-up time, drop-off time, pick–up latitude and longitude, and drop-off latitude and longitude, among other details. Based on the urban land use (ULU) map in Shenzhen (Fig. 5 a), which can be divided into 11362 regions (Gong et al., 2020 ), we perform individual matching of each order’s latitude and longitude to the corresponding urban region or the nearest urban region based on proximity to the region’s boundaries. The taxi OD data has been organized into a matrix of shapes (11362, 11362, 67). Among them, we specifically focused on the data of 56 million urban mobility records spanning from December 8, 2022, to January 6, 2023. The data preceding December 7, 2022, are utilized to calculate the baseline level of mobility and assist in obtaining the spatial attractiveness of ULU.

a This map shows urban land use categories in Shenzhen. b This figure illustrates the process of obtaining the urban land use feature vector.

This dataset, capturing changes in passenger flow before and after China’s cancellation of the "Zero-COVID" policy, is distinct from other datasets. China adhered to the "Zero-COVID" policy from 2020 to 2022, with over 99% of Chinese residents never having been infected with COVID-19. After the policy’s cancellation on December 7, 2022, Shenzhen experienced the entire process of large-scale COVID-19 spread without mobility-restricting intervention. As of July 23rd, 2023, China has implemented universal and free COVID-19 vaccination for its population. The reported vaccination rates are as follows (Xinhua, 2022 ): a cumulative coverage rate of 92.1% for the first dose, a completion rate of 89.7%, and an enhanced immunization rate of 71.7%. Among individuals aged 60 and above, the rates are 89.6% for at least one dose, 84.7% for completion, and 67.3% for enhanced immunization. The widespread administration of vaccines has significantly reduced the mortality rate, facilitating recovery from COVID-19. However, the transmissibility of SARS-CoV-2 remains high. This data offers insights into the effects of highly infectious and low-toxicity viruses on social mobility and public transport use under natural transmission conditions in the city. This information provides a baseline for assessing the potential impact of future infectious diseases on urban mobility in the absence of government intervention.

This dataset encompasses the entire process of trip changes, from the initial stage to a sharp decline, and ultimately to a gradual recovery during large-scale infectious disease outbreaks in cities. From November 1, 2022, to November 25, 2022, Shenzhen experienced fewer than 50 confirmed cases per day, and the daily lives of citizens remained largely unaffected (Shenzhen Government, 2022 ). The data from this period can be considered the baseline passenger volume for Shenzhen’s bus system. As the number of confirmed cases progressively increased, the government implemented stringent lockdown measures from November 25, 2022, to December 7, 2022, resulting in a substantial decrease in passenger flow. As of December 7, 2022, the cumulative number of confirmed cases constituted less than 0.1% of the total population. The official outbreak of the COVID-19 pandemic transpired after the cancellation of the "Zero-COVID" policy. The Omicron variant of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spread rapidly (Leung et al., 2023 ), which is evident in the drastic reduction of passenger flow within the public transport system. Subsequently, as a majority of urban residents became infected and recovered, the public transport passenger flow gradually rebounded, as depicted in Fig. 1 .

Assuming the passenger flow from origin i to destination j on day t is denoted as f i , j , t , the entire dataset can be denoted as

where M represents the sum of the number of bus stations, subway stations, and the total number of taxi-operating urban regions. T is the number of days.

Point of interest data and urban land use data

As online social media and mobile communication continue to flourish, location-based service (LBS) systems such as Google Places, Gaode Maps (China Satellite Maps), and Facebook are gaining popularity across various sectors (Han et al., 2021 ; Xing et al., 2020 ). These LBS systems enable users to search for points of interest (POIs) to access better services and share experiences from places they have visited (Chang et al., 2021 ). Generally, a POI is a specific location that individuals may find useful or intriguing. The term is commonly used to refer to commercial services, schools, subway stations, or other categories found in digital maps (Wang et al., 2019 ; Zhu et al., 2020 ).

A detailed land use map of Chinese cities can be derived by combining POI data with 10-meter satellite imagery, OpenStreetMap, nighttime lights, and Tencent social big data (Gong et al., 2020 ). This map serves as a valuable resource for inferring travel intentions. As the data is from 2018, we complement the newly developed land use attributes based on the latest available POI data. The data sources include Gaode Maps and Baidu Maps, which are leading providers of digital map content, navigation, and location service solutions in China. Important urban land use categories for travel are selected, including residential, company, commercial service, transport hub, college, school, hospital, cultural/sport, and park/scenery.

OD data preprocessing

For each OD pair ( i ,  j ) in bus, subway, and taxi systems, the time series of mobility can be represented as:

Since the passenger flow at a single OD pair exhibits randomness, f i , j experiences significant fluctuations over time, as shown in Supplementary Fig. 2 . To better illustrate the impact of COVID-19 on public transport, we apply Kalman filtering to smooth the data.

Kalman filtering is a recursive algorithm utilized for estimating the state of a dynamic system by combining noisy measurements with a mathematical model of the system (Chui et al., 2017 ). When applied to noisy time series data, the Kalman filter can provide a smoothed version of the data by recursively estimating the underlying state of the system that generated the data. The filter accounts for uncertainties in both the measurements and the system’s model, rendering it particularly effective at reducing noise while preserving the true signal. Upon applying Kalman filtering, the resulting sequence for the OD station pair ( i ,  j ) is denoted as:

The mobility between different OD pairs exhibits significant variation, with a maximum difference spanning several orders of magnitude. To uncover the general patterns impact of large-scale infectious diseases on passenger flow, it is essential to perform normalization on the time series \({{{{{\bf{f}}}}}^{{\prime} }}_{i,j}\) . Specifically, for any OD pair ( i ,  j ), the passenger flow on December 8, 2022 is considered a normal level unaffected by the epidemic. The normalized time series is calculated by:

Identifying mobility patterns via clustering algorithm

Measuring of mobility changes.

The features of changes in mobility are developed to facilitate description and subsequent clustering analysis. Drawing inspiration from the resilience in the fields of biology and engineering (Schwarz, 2018 ; Standish et al., 2014 ; Tabatabaei et al., 2018 ), we develop a set of features to comprehensively quantify the decline and recovery patterns of travel before, during, and after a large-scale infectious disease outbreak. These features can characterize the temporal changes in the response of mobility behavior to the pandemic, which reflects the interplay between human behavior, public policy, and the built environment, and provides insights into the dynamics of urban mobility under extraordinary circumstances.

Firstly, for any OD time series \({{{{{\bf{f}}}}}^{{\prime}{\prime}}}_{i,j}\) , we can identify several basic features, including the maximum value R top before the passenger flow decreases, the minimum value R bottom of passenger flow, the final passenger flow level R end , and their corresponding time moments t 0 , t 1 , t 2 , t 3 . The definition and significance of the features are as follows.

Declining speed Γ: The speed at which trips decrease serves as a measure of how quickly urban populations adapt their mobility behavior in response to public health crises. It is calculated by:

Declining amplitude Λ: The maximum amplitude of the decrease in trips directly mirrors the influence of the epidemic on mobility behavior.

Trough duration Π: The third indicator of mobility changes we define pertains to the trough duration of mobility. This indicator captures the persistence of the reduced trips during the COVID-19 pandemic, reflecting the extent to which the usual travels are disrupted.

Recovery speed Θ: When infected individuals gradually recover and the overall panic levels subside, this metric captures the speed at which urban mobility bounces back to normalcy. The determination of the indicator is achieved through:

Recovery amplitude ϒ: The amplitude of mobility recovery highlights the degree to which trips rebound after the initial impact of the pandemic. As infected citizens gradually recover and no longer reject traveling, this indicator provides insights into the interplay between public health measures, human behavior, and the ability of societies to regain their normal functioning after experiencing a large-scale infectious disease outbreak.

Total impact Δ: We finally introduce a comprehensive metric that captures the decline and recovery process of the pandemic’s impact on mobility. This total impact indicator effectively represents the degree to which the pandemic affects mobility, calculated as the area between the curve and the usual level of trips using the formula:

A lower value indicates a more substantial influence of the epidemic on mobility.

Therefore, we extract features of mobility change from each OD array ( i ,  j ):

K-means++ clustering

In order to identify change patterns of OD mobility during the pandemic, we perform the K-means++ clustering algorithm on the OD dataset. K -means++ clustering is an unsupervised machine learning algorithm used for partitioning a given dataset into a specified number ( K ) of clusters.

K-means++ clustering aims to minimize the within-cluster sum of squared (WCSS) distances from the data points to their respective cluster centroids by iteratively updating the centroids and assigning the data points. Specifically, the input of the K-means++ clustering algorithm is

where each OD time series is represented by the features.

The algorithm first needs to initialize the cluster center. A centroid c 1 is randomly chosen from X . For each x i , j   ∈   X , compute the squared distance to its nearest centroid:

where \({{{\mathcal{C}}}}={\left\{{c}_{p}\right\}}_{p = 1,...,k-1}\) represents the set of centroids chosen so far. Then, the next centroid c k is determined from the data points with probability proportional to \(D{({x}_{i})}^{2}\) :

Repeat the two steps until we have taken K centers altogether.

After selecting the K initial centroids, the algorithm proceeds with the standard iterative updates of centroids. Step 1 is assigning x i , j to the closest centroid using ( 13 ). Suppose that at iteration n , the dataset is divided into K clusters:

In step 2, calculate new centroid positions for each cluster by minimizing the WCSS distance:

The two steps are repeated until convergence is reached, i.e., when the assignment of samples to clusters no longer changes.

The elbow method is utilized to determine the optimal number of clusters, denoted as K , in K-means clustering (Bholowalia and Kumar, 2014 ). The elbow method involves plotting the variance explained or the sum of squared distances (SSE) of the data points to their cluster centroids against different values of K . The elbow method suggests selecting the value of K at the “elbow" or bend in the SSE plot (Supplementary Fig. 3a) . This point represents the optimal trade-off between minimizing SSE and avoiding excessive complexity in the clustering model.

After establishing a preliminary range for K using the elbow method, Silhouette Analysis is employed to finalize the value of K (Lleti et al., 2004 ). For each K -value, silhouette coefficient is computed for each data point, which measures the similarity between each data point and the cluster to which it is assigned, with values ranging from –1 to 1. Subsequently, the average silhouette coefficient is calculated for all data points at each K -value. This average value serves as an indicator of cluster cohesion and separation, with higher values indicating better clustering results. As depicted in Supplementary Fig. 3b , the analysis determined that the optimal value for K  = 4.

Finally, we derive K clusters of OD mobility, as shown in Fig. 3 a. The labels for each OD pair have been obtained. Furthermore, the OD pairs can be associated with spatial information, which leads to subsequent analysis.

Spatial attractiveness of urban land use

Our goal is to illuminate the variation in mobility for OD pairs during a widespread infectious disease outbreak. Solely analyzing the geographical locations of the starting and ending stations of travel, however, is inadequate. As a result, we endeavor to extract travel purposes by integrating the urban land use map.

Lands in close proximity to bus stations, subway stations, and taxi pick-up and drop-off points provide insight into passengers’ potential travel motivation. For example, when a bus station is located in a residual land, there is a high probability that passengers disembarking at this station intend to commute to their residences. Accordingly, a ULU feature vector is established for each origin and destination, which is represented as:

where r  = 1, 2, . . . ,  R represents urban land use categories. s i , r is defined as the spatial attractiveness of land use category r to the citizens boarding or disembarking from the station or urban regions i ( i  = 1, 2, . . . ,  M ).

For the bus stations and subway stations, a common method (Xing et al., 2020 ) is to use the number of lands to determine s i , r :

where z i , r denotes the number of lands belonging to category r within a certain distance range of the stations. Taking into account the varying importance of stations, the distance range for bus stations is set at 200 meters, while the distance range for subway stations is set at 500 meters, as presented in Fig. 5 b.

As mentioned in “Origin-destination data of urban mobility”, the taxi OD data represents the mobility between 11,362 distinct regions within the city. These urban regions have been delineated with nearly homogeneous land use attributes. Therefore, s i , r can be derived directly:

For an urban region with the land use attribute r , the s i , r value corresponds to 1, while the remaining values of the land feature vector are set to 0.

As each urban land use category can be mapped to an activity, identifying the most probable land use category corresponds to determining the most likely activity to be undertaken. To further analyze the mobility behavior from the origin i to the destination j , we compute the travel transition matrix:

The elements in the matrix S i , j represent the likelihood of passengers transitioning from a land use category at the origin station (region) to a ULU category at the destination station (region). For instance, s i ,1 s j ,2 in the bus system corresponds to passengers departing from land use category 1 near bus station i and arriving at land use category 2 near bus station j . From this, we can infer the passengers’ mobility behavior and purposes. The travel transition matrix for the subway and taxi follows the same principle.

We combine the K clusters of change patterns with the spatial attractiveness of urban land use. For each cluster, we sum the corresponding S i , j values to obtain a total transition matrix. Considering daily frequency changes in passenger flow, the passenger flow from station i to station j is approximately equal to the passenger flow from station j to station i on a daily timescale. Based on the symmetry of OD passenger flow, we add the lower and upper triangular parts of the total transition matrix. Finally, we identify the top values in the matrix that correspond to the primary OD land pairs and their corresponding proportions of mobility behavior within each cluster.

Dynamic OD mobility model

We develop a dynamic model to capture the fine-grained urban mobility changes. The decline in trips can be attributed to two factors: physiological infection with viruses and emotional influence. Inspired by the classic epidemic transmission model SIR (Cooper et al., 2020 ; Keeling and Eames, 2005 ), an OD passenger flow model is proposed, comprehensively considering the spread of the COVID-19 pandemic and the willingness to travel via public transportation, as shown in Fig. 4 .

The model incorporates pandemic transmission and recovery rates, willingness factors of mobility behavior, urban land use map, and pre-pandemic OD passenger flow as input. It outputs the changes in trips for every OD pair during the COVID-19 pandemic with high spatial resolution.

Physiological infection module

Prior to the outbreak of large-scale infectious diseases, we assume that the daily number of population from station i to station j is W i , j . Following the epidemic outbreak, all passengers can be categorized into three states: usual (U), infected (I) by the virus or panic, and recovered (R). The relationship among passengers associated with the three states is shown in the subsequent ( 22 ).

Specifically, initially uninfected people may become infected with the virus through contact with infected individuals. Eventually, the infected people transition to a recovery state. For each citizen, the probability state transition process of physiological infection is as follows:

where U i , j , t ,  I i , j , t ,  R i , j , t denote passenger numbers of usual state, infected state, and recovery state, respectively on the t day. β and γ represent the transmission and recovery rates, respectively, which are inherent attributes of the pandemic spread.

Willingness influence module

During the pandemic, urban residents tend to reduce their travel activities to mitigate the risk of infection. This tendency is particularly pronounced when there is a higher reported or perceived number of infections and potential cases through news outlets, social media, or personal observations, leading to increased levels of anxiety. During the recovery phase, mobility gradually rebounds over time, and confidence in travel is steadily restored. Furthermore, the decline and recovery of different travel behaviors exhibit heterogeneity. From the clustering results in the section “Population behavior behind mobility patterns”, it is evident that the decrease and recovery of trips are associated with travel purposes.

Willingness factors are proposed to model the willingness influence. It is assumed that the willingness factors of travel from ULU type r to ULU type v follow normal distributions:

where r ,  v  = 1, 2, , . . . ,  R . \({\mu }_{r,v},{\sigma }_{r,v}^{2}\) and \({{\mu }^{{\prime} }}_{r,v},{\sigma }_{r,v}^{{\prime} 2}\) are distribution parameters (mean and variance) of willingness factors for transmission and recovery, respectively.

In terms of travel mode choices, individuals tend to opt for modes associated with lower exposure to viral risks. Consequently, the willingness factor for transmission and recovery in the context of subway, bus, and taxi are denoted as \({\mu }_{q},{{\mu }^{{\prime} }}_{q}(q=1,2,3)\) .

The probability state transition process of emotional influence on travel is as follows:

Put it together

For an OD pair ( i ,  j ) of transport mode q , the travel transition matrix S i , j decomposes travel for a given OD pair into combinations of various trip purposes. Consequently, it enables the synthesis of willingness factors, thereby generating the willingness factor for that specific OD pair. The willingness factors can be estimated by linearly stacking the sampled willingness factors for travel purposes:

The mobility change for OD pair ( i ,  j ) can be determined through the following equation.

The time series of the passenger flow from origin i to destination j is:

W i , j is determined by the trips of OD ( i ,  j ) prior to the pandemic outbreak. α i , j and \({\alpha }_{i,j}^{{\prime} }\) denote willingness factors for transmission and recovery, respectively. These willingness factors depend on the ULU feature arrays of the origin i and the destination j , accounting for the diversity of OD passenger flow. The initial values of U i , j , t ,  I i , j , t ,  R i , j , t are determined by the initial infection rate in the city.

By obtaining the changes in passenger flow at all OD pairs, we can also derive the overall change in trips under the impact of the pandemic. This model characterizes the response of an entire city’s public transport system to the pandemic and the shifts in mobility behavior.

Model validation

The alterations in actual OD passenger flow within the Shenzhen bus, subway, and taxi systems during the COVID-19 pandemic serve to verify the effectiveness of the dynamic model. Research (Cai et al., 2022 ; Leung et al., 2023 ; Ren et al., 2022 ; Ribeiro Xavier et al., 2022 ) indicates that the transmission rate of SARS-CoV-2 ranges from approximately 0.2 to 0.4, while the recovery rate spans from approximately 0.05 to 0.15. These values are determined by the inherent transmission characteristics of COVID-19. This paper primarily concentrates on modeling the variations in mobility for different OD stations. Without loss of generality, we employ β  = 0.3,  γ  = 0.1.

When calculating the willingness factor for recovery, we consider only the main ULU pairs depicted in Fig 3 b. Consequently, the parameter estimation target constitutes the mean values and standard deviations of willingness factors for OD ULU pairs. Based on the maximum likelihood estimation (MLE) method, we find the optimal parameter values of the willingness factor distributions, enabling the predictive changes in the mobility model to approximate the actual trend. Specifically, we employed the grid search method to identify the values of \({\alpha }_{r,v},{{\alpha }^{{\prime} }}_{r,v}\) that correspond to the minimum root mean squared error (RMSE) between the true trips and predicted trips. These values of \({\alpha }_{r,v},{{\alpha }^{{\prime} }}_{r,v}\) are treated as sampling data for the distribution of willingness factors to be estimated. Subsequently, we determined the parameter values that maximize the probability of sampling data occurrence, resulting in the optimal mean values and standard deviations of the empirical factors.

The optimization results show that the values of willingness factors for transmission are similar for all OD station pairs, whereas the willingness factors for recovery primarily contribute to the diversity observed in mobility behavior, as displayed in Supplementary Fig. 1 . Under the optimal parameters, we sample and derive 100 sets of dynamic model parameters. The predictive trips in Shenzhen during the COVID-19 pandemic are shown in Fig. 4 .

Besides, an analysis is conducted to evaluate the robustness of the UIR model. The values of β and γ vary by approximately ± 30% and observe the model performance while keeping other parameters constant. This analysis focused on calculating the average root mean square error (RMSE) between the actual and predicted values of passenger flow across various OD pairs in the city. The RMSE for each trip series provided a daily prediction error. As shown in Supplementary Fig. 4 , our findings indicate that for every 1% deviation in β , the prediction error increased by approximately 0.61%, and for every 1% deviation in γ , the prediction error increased by around 0.77%. These results suggest a certain level of sensitivity of the model to these parameters.

Data availability

Urban land use categories are available at https://data-starcloud.pcl.ac.cn/zh . Points of interest (POIs) data can be openly accessed via the APIs of Gaode Maps ( https://lbs.amap.com/ ) and Baidu Maps ( https://lbsyun.baidu.com/ ). Raw origin-destination (OD) data of buses and taxis are available with the permission of Shenzhen Bus Group Co., Ltd. ( https://en.szbus.com.cn/ ) upon request for academic cooperation. The de-identified data are available at https://github.com/trainandtest666/OD-Data-in-Public-Transport-System.git .

Arellana J, Márquez L, Cantillo V (2020) COVID-19 outbreak in Colombia: an analysis of its impacts on transport systems. J Adv Transp 2020:1–16. https://doi.org/10.1155/2020/8867316

Article   Google Scholar  

Arthur D, Vassilvitskii S (2007) k-means++: the advantages of careful seeding. Soda 7:1027–1035

MathSciNet   Google Scholar  

Atkinson-Clement C, Pigalle E (2021) What can we learn from COVID-19 pandemic’s impact on human behaviour? The case of France’s lockdown. Humanit Soc Sci Commun 8(1):81. https://doi.org/10.1057/s41599-021-00749-2

Balmford B, Annan JD, Hargreaves JC, Altoè M, Bateman IJ (2020) Cross-country comparisons of COVID-19: policy, politics and the price of life. Environ Resour Econ 76:525–551. https://doi.org/10.1007/s10640-020-00466-5

Betthäuser BA, Bach-Mortensen AM, Engzell P (2023) A systematic review and meta-analysis of the evidence on learning during the COVID-19 pandemic. Nat Hum Behav 7:375–385. https://doi.org/10.1038/s41562-022-01506-4

Article   PubMed   Google Scholar  

Bholowalia P, Kumar A (2014) EBK-means: a clustering technique based on elbow method and k-means in WSN. Int J Comput Appl 105(9):17–24

Google Scholar  

Birkmeyer JD, Barnato A, Birkmeyer N, Bessler R, Skinner J (2020) The impact of the COVID-19 pandemic on hospital admissions in the United States: study examines trends in US hospital admissions during the COVID-19 pandemic. Health Affairs 39(11):2010–2017. https://doi.org/10.1377/hlthaff.2020.00980

Burki T (2022) Dynamic zero COVID policy in the fight against COVID. Lancet Respir Med 10(6):e58–e59. https://doi.org/10.1016/S2213-2600(22)00142-4

Article   CAS   PubMed   PubMed Central   Google Scholar  

Cai J, Deng X, Yang J et al. (2022) Modeling transmission of SARS-CoV-2 omicron in China. Nat Med 28(7):1468–1475. https://doi.org/10.1038/s41591-022-01855-7

Chang S, Pierson E, Koh P, Gerardin J, Redbird B, Grusky D, Leskovec J (2021) Mobility network models of COVID-19 explain inequities and inform reopening. Nature 589(7840):82–87. https://doi.org/10.1038/s41586-020-2923-3

Article   ADS   CAS   PubMed   Google Scholar  

Chen L, Xu F, Han Z, Tang K, Hui P, Evans J, Li Y (2022) Strategic COVID-19 vaccine distribution can simultaneously elevate social utility and equity. Nat Hum Behav 6:1503–1514. https://doi.org/10.1038/s41562-022-01429-0

Chen X, Wang H, Li Z et al. (2022) DeliverSense: efficient delivery drone scheduling for crowdsensing with deep reinforcement learning. In: UbiComp/ISWC’22 Adjunct, Cambridge, United Kingdom, 11–15 September, pp. 403–408

Chen X, Xu S, Fu H, Joe-Wong C, Zhang L, Noh H.Y., Zhang P (2019) Asc: Actuation system for city-wide crowdsensing with ride-sharing vehicular platform. In: Proceedings of the Fourth Workshop on International Science of Smart City Operations and Platforms Engineering, Montreal, Quebec, Canada, 15 April, pp. 19–24

Chen X, Xu S, Han J et al. (2020) Pas: prediction-based actuation system for city-scale ridesharing vehicular mobile crowdsensing. IEEE Internet Things J 7(5):3719–3734. https://doi.org/10.1109/JIOT.2020.2968375

Chen X, Xu S, Liu X, Xu X, Noh HY, Zhang L, Zhang P (2020) Adaptive hybrid model-enabled sensing system (HMSS) for mobile fine-grained air pollution estimation. IEEE Trans Mobile Comput 21(6):1927–1944. https://doi.org/10.1109/TMC.2020.3034270

Chen X, Xu X, Liu X, Pan S, He J, Noh HY, Zhang L, Zhang P (2018) Pga: Physics guided and adaptive approach for mobile fine-grained air pollution estimation. In: Proceedings of the 2018 ACM International Joint Conference and 2018 International Symposium on Pervasive and Ubiquitous Computing and Wearable Computers, Singapore, 08–12 October, pp. 1321–1330

China Center for Disease Control and Prevention (2022) China Daily Perspectives: the dynamic COVID-zero strategy in China. https://doi.org/10.46234/ccdcw2022.015 . Accessed 24 May, 2023

Chui CK, Chen G et al. (2017) Kalman filtering. Springer

Clark A, Jit M, Warren-Gash C et al. (2020) Global, regional, and national estimates of the population at increased risk of severe covid-19 due to underlying health conditions in 2020: a modelling study. Lancet Glob Health 8(8):e1003–e1017. https://doi.org/10.1016/S2214-109X(20)30264-3

Cooper I, Mondal A, Antonopoulos CG (2020) A SIR model assumption for the spread of COVID-19 in different communities. Chaos Soliton Fract 139:110057. https://doi.org/10.1016/j.chaos.2020.110057

Article   MathSciNet   Google Scholar  

Forzieri G, Dakos V, McDowell NG, Ramdane A, Cescatti A (2022) Emerging signals of declining forest resilience under climate change. Nature 608(7923):534–539. https://doi.org/10.1038/s41586-022-04959-9

Gkiotsalitis K, Cats O (2021) Public transport planning adaption under the COVID-19 pandemic crisis: literature review of research needs and directions. Transp Rev 41:374–392. https://doi.org/10.1080/01441647.2020.1857886

Gong P, Chen B, Li X et al. (2020) Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018. Sci Bull 65(3):182–187. https://doi.org/10.1016/j.scib.2019.12.007

Guo B, Wang S, Ding Y, Wang G, He S, Zhang D, He T (2021) Concurrent order dispatch for instant delivery with time-constrained actor-critic reinforcement learning. In: 2021 IEEE Real-Time Systems Symposium (RTSS), pp. 176–187

Han Z, Fu H, Xu F, Tu Z, Yu Y, Hui P, Li Y (2021) Who will survive and revive undergoing the epidemic: Analyses about POI visit behavior in Wuhan via check-in records. In: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, NY, USA, vol. 5, pp. 1–20

Keeling MJ, Eames KT (2005) Networks and epidemic models. J R Soc Interface 2(4):295–307. https://doi.org/10.1098/rsif.2005.0051

Article   PubMed   PubMed Central   Google Scholar  

Kumpfer KL (2002) Factors and processes contributing to resilience: the resilience framework. Resilience and development: positive life adaptations. 179–224. https://doi.org/10.1007/0-306-47167-1_9

Leung K, Lau EH, Wong CK, Leung GM, Wu JT (2023) Estimating the transmission dynamics of SARS-CoV-2 Omicron BF. 7 in Beijing after the adjustment of zero-COVID policy in November-December 2022. Nat Med 29:579–582. https://doi.org/10.1038/s41591-023-02212-y

Article   CAS   PubMed   Google Scholar  

Levin R, Chao DL, Wenger EA, Proctor JL (2021) Insights into population behavior during the COVID-19 pandemic from cell phone mobility data and manifold learning. Nat Comput Sci 1(9):588–597. https://doi.org/10.1038/s43588-021-00125-9

Li Z, Man F, Chen X, Zhao B, Wu C, Chen X (2022) TRACT: Towards large-scale crowdsensing with high-efficiency swarm path planning. In: UbiComp/ISWC’22 Adjunct, Cambridge, United Kingdom, 11–15 Sept, pp. 409–414. https://doi.org/10.1145/2994551.2996714

Liu Y, Yu Y, Zhao Y, He D (2022) Reduction in the infection fatality rate of Omicron variant compared with previous variants in South Africa. Int J Infect Dis 120:146–149. https://doi.org/10.1016/j.ijid.2022.04.029

Lletı R, Ortiz MC, Sarabia LA, Sánchez MS (2004) Selecting variables for k-means cluster analysis by using a genetic algorithm that optimises the silhouettes. Anal Chim Acta 515(1):87–100

Ma S, Li S, Zhang J (2023) Spatial and deep learning analyses of urban recovery from the impacts of COVID-19. Sci Rep 13(1):2447. https://doi.org/10.1038/s41598-023-29189-5

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Martínez L, Short J (2021) The pandemic city: urban issues in the time of COVID-19. Sustainability 13(6):3295. https://doi.org/10.3390/su13063295

Menkir TF, Chin T, Hay JA et al. (2021) Estimating internationally imported cases during the early COVID-19 pandemic. Nat Commun 12(1):311. https://doi.org/10.1038/s41467-020-20219-8

Mulcahey MK, Gianakos AL, Mercurio A, Rodeo S, Sutton KM (2021) Sports medicine considerations during the COVID-19 pandemic. Am J Sports Med 49(2):512–521. https://doi.org/10.1177/0363546520975186

Peiffer-Smadja N, Lucet J, Bendjelloul G et al. (2020) Challenges and issues about organizing a hospital to respond to the COVID-19 outbreak: experience from a French reference centre. Clin Microbiol Infect 26(6):669–672. https://doi.org/10.1016/j.cmi.2020.04.002

Qian T, Chen X, Xin Y, Tang W, Wang L (2022) Resilient decentralized optimization of chance constrained electricity-gas systems over lossy communication networks. Energy 239:122158. https://doi.org/10.1016/j.energy.2021.122158

Ren J, Xu Y, Li Z, Hong C, Zhang XP, Chen X (2023) Scheduling UAV swarm with attention-based graph reinforcement learning for ground-to-air heterogeneous data communication. In: UbiComp/ISWC’23 Adjunct, Cancun, Quintana Roo, Mexico, 08–12 October, pp. 670–675. https://doi.org/10.1145/3594739.3612905 (2023)

Ren SY, Wang WB, Gao RD, Zhou AM (2022) Omicron variant (B. 1.1. 529) of SARS-CoV-2: mutation, infectivity, transmission, and vaccine resistance. World J Clin Cases 10(1):1

Ribeiro XC, Sachetto OR, da FVV et al. (2022) Characterisation of Omicron variant during COVID-19 pandemic and the impact of vaccination, transmission rate, mortality, and reinfection in South Africa, Germany, and Brazil. BioTech 11(2):12. https://doi.org/10.3390/biotech11020012

Article   CAS   Google Scholar  

Schwarz S (2018) Resilience in psychology: a critical analysis of the concept. Theory Psychol 28(4):528–541. https://doi.org/10.1177/0959354318783584

She J, Liu L, Liu W (2020) COVID-19 epidemic: disease characteristics in children. J Med Virol 92(7):747–754. https://doi.org/10.1002/jmv.25807

Shen J, Duan H, Zhang B et al. (2020) Prevention and control of COVID-19 in public transportation: experience from China. Environ Pollut 266(2):115291. https://doi.org/10.1016/j.envpol.2020.115291

Shenzhen Government (2023) Notice of the Shenzhen municipal people’s government on issuing the overall plan and three year action plan for park city construction in Shenzhen (2022–2024). http://www.sz.gov.cn/zfgb/2023/gb1272/content/post_10389162.html . Accessed 3 Aug 2023

Shenzhen Open Data Platform (2022) COVID-19 in Shenzhen—statistics of daily confirmed cases. https://opendata.sz.gov.cn . Accessed 12 Aug 2023

Sibley C, Greaves L, Satherley N et al. (2020) Effects of the COVID-19 pandemic and nationwide lockdown on trust, attitudes toward government, and well-being. Am Psychol 75(5):618–630. https://doi.org/10.1037/amp0000662

Standish RJ, Hobbs R, Mayfield M et al. (2014) Resilience in ecology: Abstraction, distraction, or where the action is? Biol Conserv 177:43–51. https://doi.org/10.1016/j.biocon.2014.06.008

Sun H, Forsythe W, Waters N (2007) Modeling urban land use change and urban sprawl: Calgary, Alberta, Canada. Netw Spat Econ 7:353–376. https://doi.org/10.1007/s11067-007-9030-y

Tabatabaei NM, Ravadanegh SN, Bizon N (2018) Power systems resilience. Springer

Taskinsoy J (2020) COVID-19: Is the great outbreak a sign of what the future has stowed for the human race? Available at SSRN 3597434. https://doi.org/10.2139/ssrn.3597434

Tirachini A, Cats O (2020) COVID-19 and public transportation: current assessment, prospects, and research needs. J Public Transp 22(1):1–21. https://doi.org/10.5038/2375-0901.22.1.1

Tisdell CA (2020) Economic, social and political issues raised by the COVID-19 pandemic. Econ Anal Policy 68:17–28. https://doi.org/10.1016/j.eap.2020.08.002

United Nations (2018) 68% of the world population projected to live in urban areas by 2050, says UN. https://www.un.org/development/desa/en/news/population/2018-revision-of-world-urbanization-prospects.html . Accessed 20 Aug 2022

Wang D, Tayarani M, He BY, Gao J, Chow JYJ, Gao HO, Ozbay K (2021) Mobility in post-pandemic economic reopening under social distancing guidelines: Congestion, emissions, and contact exposure in public transit. Transp Res Part A Policy Pract 153:151–170. https://doi.org/10.1016/j.tra.2021.09.005

Wang H, Chen X, Cheng Y et al. (2022) H-SwarmLoc: efficient scheduling for localization of heterogeneous MAV swarm with deep reinforcement learning. In: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems, Boston, Massachusetts, US, November, pp. 1148–1154

Wang J, Huang J, Yang H, Levinson D (2022) Resilience and recovery of public transport use during COVID-19. npj Urban Sustain 2(1):18. https://doi.org/10.1038/s42949-022-00061-1

Wang S, He T, Zhang D et al. (2019) Towards efficient sharing: a usage balancing mechanism for bike sharing systems. In: WWW’19, San Francisco, CA, USA, 13-17 May, pp. 2011–2021

Wei Y, Wang J, Song W, Xiu C, Ma L, Pei T (2021) Spread of COVID-19 in China: analysis from a city-based epidemic and mobility model. Cities 110:103010. https://doi.org/10.1016/j.cities.2020.103010

Weiss M, Schwarzenberg A, Nelson R, Sutter K, Sutherland M (2020) Global economic effects of COVID-19. Congressional Research Service. pp. 21–35

World Health Organization (WHO) (2023) WHO Director-General’s opening remarks at the media briefing—5 May 2023. https://www.who.int/director-general/speeches/detail/who-director-general-s-opening-remarks-at-the-media-briefing---5-may-2023 . Accessed 22 Jun 2023

Xia K, Lin L, Wang S, Wang H, Zhang D, He T (2023) A predict-then-optimize couriers allocation framework for emergency last-mile logistics. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Long Beach, CA, USA, 6–10 August, pp. 5237–5248. https://doi.org/10.1145/3580305.3599766

Xing Y, Wang K, Lu JJ (2020) Exploring travel patterns and trip purposes of dockless bike-sharing by analyzing massive bike-sharing data in Shanghai, China. J Transp Geogr 87:102787. https://doi.org/10.1016/j.jtrangeo.2020.102787

Xinhua News Agency (2022) The overall coverage rate of COVID-19 vaccine in China. http://www.news.cn/politics/2022-07/23/c_1128857426.htm . Accessed 11 Jul 2023

Xu F, Zhang J, Gao C, Feng J, Li Y (2023) Urban generative intelligence (UGI): a foundational platform for agents in embodied city environment. https://doi.org/10.48550/arXiv.2312.11813

Zhang A, Zhang K, Li W, Wang Y, Li Y, Zhang L (2022) Optimising self-organised volunteer efforts in response to the COVID-19 pandemic. Humanit Soc Sci Commun 9(1):134. https://doi.org/10.1057/s41599-022-01127-2

Zhang J (2021) People’s responses to the COVID-19 pandemic during its early stages and factors affecting those responses. Humanit Soc Sci Commun 8:37. https://doi.org/10.1057/s41599-021-00720-1

Zhao P, Cao Z, Zeng DD, Gu C, Wang Z, Xiang Y, Qadrdan M, Chen X, Yan X, Li S (2021) Cyber-resilient multi-energy management for complex systems. IEEE Trans Industr Inform 18(3):2144–2159. https://doi.org/10.1109/TII.2021.3097760

Zhu X, Wang S, Guo B et al. (2020) SParking: A win-win data-driven contract parking sharing system. In: UbiComp/ISWC ’20 Adjunct, Virtual Event, Mexico, 12–16 September, pp. 596–604

Download references

Acknowledgements

This paper was supported by the National Key R&D Program of China No. 2022YFC3300703, the Natural Science Foundation of China under Grant No. 62371269. Guangdong Innovative and Entrepreneurial Research Team Program No. 2021ZT09L197, Shenzhen 2022 Stabilization Support Program No. WDZC20220811103500001, Tsinghua Shenzhen International Graduate School Cross-disciplinary Research and Innovation Fund Research Plan No. JC20220011, the Project from Science and Technology Innovation Committee of Shenzhen (Grant No. KCXST20221021111201002), the Major Key Project of PCL (Peng Cheng Laboratory) under Grants PCL2023A09, and Meituan.

Author information

These authors contributed equally: Baining Zhao, Xuzhe Wang.

Authors and Affiliations

Shenzhen International Graduate School, Tsinghua University, Shenzhen, China

Baining Zhao, Xuzhe Wang, Tianyu Zhang, Fanhang Man, Yang Li & Xinlei Chen

Peng Cheng Laboratory, Shenzhen, China

Baining Zhao, Tao Sun & Xinlei Chen

Institute of artificial intelligence, Beihang University, Beijing, China

Department of Electronic Engineering, Tsinghua University, Beijing, China

Fengli Xu & Yong Li

Department of Mathematics, Shenzhen University, Shenzhen, China

Erbing Chen

RISC-V International Open Source Laboratory, Shenzhen, China

Xinlei Chen

You can also search for this author in PubMed   Google Scholar

Contributions

Fellow researchers and authors contributed as follows: Conceptualization: BZ, RS, YL, & XC; methodology: XW, BZ, & TS; formal analysis: RS, FX, & XC; writing/original draft preparation: BZ, XW, FM, EC, & TZ; writing/review and editing: TS, XW, XC, BZ, & YL; BZ, XW, & TS contributed equally to this work and share first authorship. Correspondence to XC.

Corresponding author

Correspondence to Xinlei Chen .

Ethics declarations

Competing interests.

The authors declare no competing interests.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

Additional information.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary figs, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Zhao, B., Wang, X., Zhang, T. et al. Estimating and modeling spontaneous mobility changes during the COVID-19 pandemic without stay-at-home orders. Humanit Soc Sci Commun 11 , 591 (2024). https://doi.org/10.1057/s41599-024-03068-4

Download citation

Received : 30 August 2023

Accepted : 17 April 2024

Published : 08 May 2024

DOI : https://doi.org/10.1057/s41599-024-03068-4

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Computer & Communication Industry Association

Computer and Communications Industry Association

• Competition
• Content Moderation
• Cybersecurity
• Emerging Tech
• European Union
• Innovation Policy
• Internet Governance
• Research Center
• Case Studies
• About Research Center
• Briefs and Written Submissions
• Comments, Letters, And Testimony
• White Papers & Reports
• Board of Directors & Executives
• Press Releases
• Digital Economy

CCIA Publishes White Papers Detailing the History and Benefits of Digital Trade

Washington – The Computer & Communications Industry Association published two White Papers detailing the history of cross-border data flow commitments and highlighting the benefits of digital trade in response to common myths about their harms.  The two papers, titled “ USTR’s Revisionist History on Data and Trade Agreements ” and “ Responding to the Myths Holding Back U.S. Action on Digital Trade ,” respond to quotes from recent testimony of the U.S. Trade Representative (USTR) in oversight hearings before the House Ways & Means Committee and the Senate Finance Committee .  

The first paper, “USTR’s Revisionist History on Data and Trade Agreements,” provides a historical perspective on the United States’ longstanding record of support for cross-border data flows, going back to 1980.  The second paper, “Responding to the Myths Holding Back U.S. Action on Digital Trade,” highlights the importance of digital trade to U.S. priorities and responds to the arguments of those who believe digital trade rules should not currently be pursued or enforced on the global stage.

The Computer & Communications Industry Association has advocated for digital trade rules that strengthen the global economy for over 50 years.

The following can be attributed to CCIA Vice President of Digital Trade Jonathan McHale:

“Strong digital trade rules reflect the broader interests of the United States in support of its economy, workers, national security, and democratic priorities on the global stage.  Historically, the U.S. Trade Representative has led the way alongside close international partners to negotiate and enforce commitments that connect like-minded allies and protect the flow of information and enhance mutually-beneficial commercial activity.”  

“In recent months, we have seen one unsupported argument after another justifying the recent U.S. trade policy shift, the abandonment of robust rules that support cross-border data flows, prohibit unjust data localization and discriminatory treatment of digital products, and protect companies’ source code from compelled disclosure.  These justifications are reliant on myths that reflect neither the history nor the evidence in support of such rules, ignoring a wide range of economic and societal benefits.  We must remember the origins of U.S. leadership in this area, as valid today as in the past—and the history of cross-border data flow commitments date back 40 years.  U.S. trade policy leaders should redirect digital priorities back on track at a moment when digital protectionism and authoritarian approaches to governance are gaining ground.”

We've detected unusual activity from your computer network

To continue, please click the box below to let us know you're not a robot.

Why did this happen?

Please make sure your browser supports JavaScript and cookies and that you are not blocking them from loading. For more information you can review our Terms of Service and Cookie Policy .

For inquiries related to this message please contact our support team and provide the reference ID below.