hate speech on social media pdf

• Search Menu
• Advance articles
• Featured Content
• Author Guidelines
• Open Access
• About The British Journal of Criminology
• About the Centre for Crime and Justice Studies
• Editorial Board
• Advertising and Corporate Services
• Journals Career Network
• Self-Archiving Policy
• Dispatch Dates
• Terms and Conditions
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

Introduction, prevalence of online hate speech on social media, theoretical framework, data and methods.

• < Previous

Hate in the Machine: Anti-Black and Anti-Muslim Social Media Posts as Predictors of Offline Racially and Religiously Aggravated Crime

• Article contents
• Figures & tables
• Supplementary Data

Matthew L Williams, Pete Burnap, Amir Javed, Han Liu, Sefa Ozalp, Hate in the Machine: Anti-Black and Anti-Muslim Social Media Posts as Predictors of Offline Racially and Religiously Aggravated Crime, The British Journal of Criminology , Volume 60, Issue 1, January 2020, Pages 93–117, https://doi.org/10.1093/bjc/azz049

• Permissions Icon Permissions

National governments now recognize online hate speech as a pernicious social problem. In the wake of political votes and terror attacks, hate incidents online and offline are known to peak in tandem. This article examines whether an association exists between both forms of hate, independent of ‘trigger’ events. Using Computational Criminology that draws on data science methods, we link police crime, census and Twitter data to establish a temporal and spatial association between online hate speech that targets race and religion, and offline racially and religiously aggravated crimes in London over an eight-month period. The findings renew our understanding of hate crime as a process, rather than as a discrete event, for the digital age.

Hate crimes have risen up the hierarchy of individual and social harms, following the revelation of record high police figures and policy responses from national and devolved governments. The highest number of hate crimes in history was recorded by the police in England and Wales in 2017/18. The 94,098 hate offences represented a 17 per cent increase on the previous year and a 123 per cent increase on 2012/13. Although the Crime Survey for England and Wales has recorded a consistent decrease in total hate crime victimization (combining race, religion, sexual orientation, disability and transgender), estimations for race and religion-based hate crimes in isolation show an increase from a 112,000 annual average (April 13–March 15) to a 117,000 annual average (April 15–March 17) ( ONS, 2017 ). This increase does not take into account the likely rise in hate victimization in the aftermath of the 2017 terror attacks in London and Manchester. Despite improvements in hate crime reporting and recording, the consensus is that a significant ‘dark figure’ remains. There continues a policy and practice need to improve the intelligence about hate crimes, and in particular to better understand the role community tensions and events play in patterns of perpetration. The HMICFRS (2018) inspection on police responses to hate crimes evidenced that forces remain largely ill-prepared to handle the dramatic increases in racially and religiously aggravated offences following events like the United Kingdom-European Union (UK-EU) referendum vote in 2016 and the terror attacks in 2017. Part of the issue is a significant reduction in Police Community Support Officers throughout England, and in particular London ( Greig-Midlane (2014) indicates a circa 50 per cent reduction since 2010). Fewer officers in neighbourhoods gathering information and intelligence on community relations reduces the capacity of forces to pre-empt and mitigate spates of inter-group violence, harassment and criminal damage.

Technology has been heralded as part of the solution by transforming analogue police practices into a set of complementary digital processes that are scalable and deliverable in near real time ( Williams et al. , 2013 ; Chan and Bennett Moses, 2017 ; Williams et al. , 2017a ). In tandem with offline hate crime, online hate speech posted on social media has become a pernicious social problem ( Williams et al. , 2019 ). Thirty years on from the Home Office (1989) publication ‘ The Response to Racial Attacks and Harassment ’ that saw race hate on the streets become priority for six central Whitehall departments, the police, Crown Prosecution Service (CPS) and courts ( Bowling, 1993 ), the government is now making similar moves to tackle online hate speech. The Home Secretary in 2016 established the National Online Hate Crime Hub, a Home Affairs Select Committee in 2017 established an inquiry into hate crime, including online victimization, and a review by the Law Commission was launched by the prime minister to address the inadequacies in legislation relating to online hate. Social media giants, such as Facebook and Twitter, have been questioned by national governments and the European Union over their policies that provided safe harbour to hate speech perpetrators. Previous research shows hate crimes offline and hate speech online are strongly correlated with events of significance, such as terror attacks, political votes and court cases ( Hanes and Machin, 2014 ; Williams and Burnap, 2016 ). It is therefore acceptable to assume that online and offline hate in the immediate wake of such events are highly correlated. However, what is unclear is if a more general pattern of correlation can be found independent of ‘trigger’ events. To test this hypothesis, we collected Twitter and police recorded hate crime data over an eight-month period in London and built a series of statistical models to identify whether a significant association exists. At the time of writing, no published work has shown such an association. Our models establish a general temporal and spatial association between online hate speech targeting race and religion and offline racially and religiously aggravated crimes independent of ‘trigger’ events . Our results have the potential to renew our understanding of hate crime as a process, rather than a discrete event ( Bowling, 1993 ), for the digital age.

Since its inception, the Internet has facilitated the propagation of extreme narratives often manifesting as hate speech targeting minority groups ( Williams, 2006 ; Perry and Olsson, 2009 ; Burnap and Williams, 2015 , 2016 ; Williams and Burnap, 2016 ; Williams et al. , 2019 ). Home Office (2018) data show that 1,605 hate crimes were flagged as online offences between 2017 and 2018, representing 2 per cent of all hate offences. This represents a 40 per cent increase compared to the previous year. Online race hate crime makes up the majority of all online hate offences (52 per cent), followed by sexual orientation (20 per cent), disability (13 per cent), religion (12 per cent) and transgender online hate crime (4 per cent). Crown Prosecution Service data show that in the year April 2017/18, there were 435 prosecutions related to online hate, a 13 per cent increase on the previous year ( CPS, 2018 ). These figures are a significant underestimate. 1 HMICFRS (2018) found that despite the Home Office introducing a requirement for police forces to flag cyber-enabled hate crime offences, uptake on this practice has been patchy and inconsistent, resulting in unreliable data on prevalence.

Hawdon et al. (2017) , using representative samples covering 15- to 30-year-olds in the United States, United Kingdom, Germany and Finland, found on average 43 per cent respondents had encountered hate material online (53 per cent for the United States and 39 per cent for the United Kingdom). Most hate material was encountered on social media, such as Twitter and Facebook. Ofcom (2018b) , also using a representative UK sample, found that near half of UK Internet users reported seeing hateful content online in the past year, with 16- to 34-year-olds most likely to report seeing this content (59 per cent for 16–24s and 62 per cent for 25–34s). Ofcom also found 45 per cent of 12- to 15-year-olds in 2017 reported encountering hateful content online, an increase on the 2016 figure of 34 per cent ( Ofcom, 2018a ; 2018c ).

Administrative and survey data only capture a snapshot of the online hate phenomenon. Data science methods pioneered within Computational Criminology (see Williams and Burnap, 2016 ; Williams et al. , 2017a ) facilitate a real-time view of hate speech perpetration in action, arguably generating a more complete picture. 2 In 2016 and 2017, the Brexit vote and a string of terror attacks were followed by significant and unprecedented increases in online hate speech (see Figures 1 and 2 ). Although the production of hate speech increased dramatically in the wake of all these events, statistical models showed it was least likely to be retweeted in volume and to survive for long periods of time, supporting a ‘half-life’ hypothesis. Where hate speech was retweeted, it emanated from a core group of like-minded individuals who seek out each other’s messages ( Williams and Burnap, 2016 ). Hate speech produced around the Brexit vote in particular was found to be largely driven by a small number of Twitter accounts. Around 50 per cent of anti-Muslim hate speech was produced by only 6 per cent users, many of whom were classified as politically anti-Islam ( Demos, 2017 ).

UK anti-black and anti-Muslim hate speech on Twitter around the Brexit vote

Global anti-Muslim hate speech on Twitter during 2017 (gaps relate to breaks in data collection)

The role of popular and politically organized racism in fostering terrestrial climates of intimidation and violence is well documented ( Bowling, 1993 ). The far right, and some popular right-wing politicians, have been pivotal in shifting the ‘Overton window’ of online political discussion further to the extremes ( Lehman, 2014 ), creating spaces where hate speech has become the norm. Early research shows the far right were quick to take to the Internet largely unhindered by law enforcement due to constitutional protections around free speech in the United States. The outcome has been the establishment of extreme spaces that provide a collective virtual identity to previously fragmented hateful individuals. These spaces have helped embolden domestic hate groups in many countries, including the United States, United Kingdom, Germany, the Netherlands, Italy and Sweden ( Perry and Olsson, 2009 ).

In late 2017, social media giants began introducing hate speech policies, bowing under pressure from the German government and the European Commission ( Williams et al. , 2019 ). Up to this point, Facebook, Instagram, YouTube and Twitter were accused of ‘shielding’ far right pages as they generated advertising income due to their high number of followers. The ‘Tommy Robinson’ Facebook page, with 1 million followers, held the same protections as media and government pages, despite having nine violations of the platform’s policy on hate speech, whereas typically only five were tolerated by the content review process ( Hern, 2018 ). The page was eventually removed in March 2019, a year after Twitter removed the account of Stephen Yaxley-Lennon (alias Tommy Robinson) from their platform.

Social media was implicated in the Christchurch, New Zealand extreme-right wing terror attack in March 2019. The terrorist was an avid user of social media, including Facebook and Twitter, but also more subversive platforms, such as 8chan. 8chan was the terrorist’s platform of choice when it came to publicizing his live Facebook video of the attack. His message opened by stating he was moving on from ‘shit-posting’—using social media to spread hatred of minority groups—to taking the dialogue offline, into action. He labelled his message a ‘real life effort post’—the migration of online hate speech to offline hate crime/terrorism ( Figure 3 ). The live Facebook video lasted for 17 minutes, with the first report to the platform being made after the 12th minute. The video was taken down within the hour, but it was too late to stop the widespread sharing. It was re-uploaded more than 2 million times on Facebook, YouTube, Instagram and Twitter and it remained easily accessible over 24 hours after the attack. Facebook, Twitter, but particularly 8chan, flooded with praise and support for the attack. Many of these posts were removed, but those on 8chan remain due to its lack of moderation.

Christchurch extreme right terror attacker’s post on 8chan, broadcasting the live Facebook video

In the days following the terror attack spikes in hate crimes were recorded across the United Kingdom. In Oxford, Swastikas with the words “sub 2 PewDiePie” were graffitied on a school wall. In in his video ahead of the massacre, the terrorist asked viewers to ‘subscribe to PewDiePie’. The social media star who earned $15.5 million in 2018 from his online activities has become known for his anti-Semitic comments and endorsements of white supremacist conspiracies ( Chokshi, 2019 ). In his uploaded 74-page manifesto, the terrorist also referenced Darren Osborne, the perpetrator of the Finsbury Park Mosque attack in 2017. Osborne is known to have been influenced by social media communications ahead of his attack. His phone and computers showed that he accessed the Twitter account of Stephen Yaxley-Lennon two days before the attack, who he only started following two weeks prior. The tweet from Robinson read ‘Where was the day of rage after the terrorist attacks. All I saw was lighting candles’. A direct Twitter message was also sent to Osborne by Jayda Fransen of Britain First ( Rawlinson, 2018 ). Other lone actor extreme right-wing terrorists, including Pavlo Lapshyn and Anders Breivik, are also known to have self-radicalized via the Internet ( Peddell et al. 2016 ).

Far right and popular right-wing activity on social media, unhindered for decades due to free-speech protections, has shaped the perception of many users regarding what language is acceptable online. Further enabled by the disinhibiting and deindividuating effects of Internet communications, and the ineffectiveness of the criminal justice system to keep up with the pace of technological developments ( Williams, 2006 ), social media abounds with online hate speech. Online controversies, such as Gamergate, the Bank of England Fry/Austen fiasco and the Mark Meechan scandal, among many others, demonstrate how easily users of social media take to antagonistic discourse ( Williams et al. , 2019 ). In recent times, these users have been given further licence by the divisive words of popular right-wing politicians wading into controversial debates, in the hopes of gaining support in elections and leadership contests. The offline consequences of this trend are yet to be fully understood, but it is worth reminding ourselves that those who routinely work with hate offenders agree that although not all people who are exposed to hate material go on to commit hate crimes on the streets, all hate crime criminals are likely to have been exposed to hate material at some stage ( Peddell et al. , 2016 ).

The study relates to conceptual work that examines the role of social media in political polarization ( Sunstein, 2017 ) and the disruption of ‘hierarchies of credibility’ ( Greer and McLaughlin, 2010 ). In the United States, online sources, including social media, now outpace traditional press outlets for news consumption ( Pew Research Centre, 2018 ). The pattern in the United Kingdom is broadly similar, with only TV news (79 per cent) leading over the Internet (64 per cent) for all adults, and the Internet, in particular social media taking first place for those aged 16–24 ( Ofcom, 2018b ). In the research on polarization, the general hypothesis tested is disinformation is amplified in partisan networks of like-minded social media users, where it goes largely unchallenged due to ranking algorithms filtering out any challenging posts. Sunstein (2017) argues that ‘echo chambers’ on social media reflecting increasingly extreme viewpoints are breeding grounds for ‘fake news’, far right and left conspiracy theories and hate speech. However, the evidence on the effect of social media on political polarization is mixed. Boxell et al. (2017) and Debois and Blank (2017) , both using offline survey data, found that social media had limited effect on polarization on respondents. Conversely, Brady et al. (2017) and Bail et al. (2018) , using online and offline data, found strong support for the hypothesis that social media create political echo chambers. Bail et al. found that republicans, and to a lesser extent democrats, were likely to become more entrenched in their original views when exposed to opposing views on Twitter, highlighting the resilience of echo chambers to destabilization. Brady et al. found that emotionally charged (e.g. hate) messages about moral issues (e.g. gay marriage) increased diffusion within echo chambers, but not between them, indicating this as a factor in increasing polarization between liberals and conservatives.

A recently exposed factor that is a likely candidate for increasing polarization around events is the growing use of fake accounts and bots to spread divisive messages. Preliminary evidence shows that these automated Twitter accounts were active in the UK-EU referendum campaign, and most influential on the leave side ( Howard and Kollanyi, 2016 ). Twitter accounts linked to the Russian Internet Research Agency (IRA) were also active in the Brexit debate following the vote. These accounts also spread fake news and promoted xenophobic messages in the aftermath of the 2017 UK terror attacks ( Crest, 2017 ). Accounts at the extreme-end of right-wing echo chambers were routinely targeted by the IRA to gain traction via retweets. Key political and far right figures have also been known to tap into these echo chambers to drum-up support for their campaigns. On Twitter, Donald Trump has referred to Mexican immigrants as ‘criminals and rapists’ and retweeted far right activists after Charlottesville, and Islamophobic tweets from the far right extremist group, Britain First. The leaders of Britain First, and the ex-leader of the English Defence League, all used social media to spread their divisive narrative before they were banned from most platforms between December 2017 and March 2019. These extremist agitators and others like them have used the rhetoric of invasion, threat and otherness in an attempt to increase polarization online, in the hope that it spills into the offline, in the form of votes, financial support and participation in rallies. Research by Hope Not Hate (2019) shows that at the time of the publication of their report, 5 of the 10 far-right social media activists with the biggest online reach in the world were British. The newest recruits to these ideologies (e.g. Generation Identity) are highly technically capable and believe social media to be essential to building a larger following.

Whatever the effect of social media on polarization, and how this may vary by individual-level factors, the role of events, bots and far right agitators, there remains limited experimental research that pertains to the key aim of this article: its impact on the behaviour of the public offline. Preliminary unpublished work suggests a link between online polarizing activity and offline hate crime ( Müller and Shwarz, 2018a , 2018b ). But what remains under-theorized is why social media has salience in this context that overrides the effect of other sources (TV, newspapers, radio) espousing arguably more mainstream viewpoints. Greer and Mclaughlin (2010) have written about the power of social media in the form of citizen journalism, demonstrating how the initially dominant police driven media narrative of ‘protestor violence’ in the reporting of the G20 demonstration was rapidly disrupted by technology-driven alternative narratives of ‘police violence’. They conclude “the citizen journalist provides a valuable additional source of real-time information that may challenge or confirm the institutional version of events” (2010: 1059). Increasingly, far right activists like Stephen Yaxley-Lennon are adopting citizen journalism as a tactic to polarize opinion. Notably, Lennon live-streamed himself on social media outside Leeds Crown Court hearing the Huddersfield grooming trials to hundreds of thousands of online viewers. His version of events was imbued with anti-Islam rhetoric, and the stunt almost derailed the trial. Such tactics take advantage of immediacy, manipulation, partisanship and a lack of accountability rarely found in mainstream media. Such affordances can provide a veil of authenticity and realism to stories, having the power to reframe their original casting by the ‘official’ establishment narrative, further enabled by dramatic delivery of ‘evidence’ of events as they occur. The ‘hacking’ of the information-communications marketplace enabled by social media disrupts the primacy of conventional media, allowing those who produce subversive “fake news” anti-establishment narratives to rise up the ‘hierarchy of credibility’. The impact of this phenomenon is likely considerable knowing over two-thirds of UK adults, and eight in ten 16- to 24-year-olds now use the Internet as their main source of news ( Ofcom, 2018b ).

The hypotheses test if online hate speech on Twitter, an indicator of right-wing polarization, can improve upon the estimations of offline hate crimes that use conventional predictors alone.

H1 : Conventional census regressors associated with hate crime in previous research will emerge as statistically significant.

‘Realistic’ threats are often associated with hate crimes (Stephan and Stephan, 2000 ; Roberts et al., 2013). These relate to resource threats, such as competition over jobs and welfare benefits. Espiritu (2004) shows how US census measures relating to economic context are statistically associated with hate crimes at the state level. In the United Kingdom, Ray et al. (2004) found that a sense of economic threat resulted in unacknowledged shame, which was experienced as rage directed toward the minority group perceived to be responsible for economic hardship. Demographic ecological factors, such as proportion of the population who are black or minority ethnic and age structure, have also been associated with hate crime ( Green, 1998 ; Nandi et al. , 2017 ; Williams and Tregidga, 2014 ; Ray et al. , 2004 ). In addition, educational attainment has been shown to relate to tolerance, even among those explicitly opposed to minority groups ( Bobo and Licari, 1989 ).

H2 : Online hate speech targeting race and religion will be positively associated with police recorded racially and religiously aggravated crimes in London.

Preliminary unpublished work focusing on the United States and Germany has showed that posts from right-wing politicians that target minority groups, deemed as evidence of extreme polarization, are statistically associated with variation in offline hate crimes recorded by the police. Müller and Shwarz (2018a) found an association between Trump’s tweets about Islam-related topics and anti-Muslim hate in US state counties. The same authors also found anti-refugee posts on the far-right Alternative für Deutschland’s Facebook page predicted offline-violent crime against immigrants in Germany ( Müller and Shwarz, 2018b ). This hypothesis tests for the first time if these associations are replicated in the United Kingdom’s largest metropolitan area.

H3 : Estimation models including the online hate speech regressor will increase the amount of offline hate crime variance explained in panel-models compared to models that include census variables alone.

Williams et al. (2017a) found that tweets mentioning terms related to the concept of ‘broken windows’ were statistically associated with police recorded crime (hate crime was not included) in London boroughs and improved upon the variance explained compared to census regressors alone. This hypothesis tests whether these results hold for the estimation of hate crimes.

The study adopted methods from Computational Criminology (see Williams et al. , 2017a for an overview). Data were linked from administrative, survey and social media sources to build our statistical models. Police recorded racially and religiously aggravated offences data were obtained from the Metropolitan Police Service for an eight-month period between August 2013 and August 2014. UK census variables from 2011 were derived from the Nomis web portal. London-based tweets were collected over the eight-month period using the Twitter streaming Application Programming Interface via the COSMOS software ( Burnap et al. , 2014 ). All sources were linked by month and Lower Layer Super Output Area (LSOA) in preparation for a longitudinal ecological analysis.

Dependent measures

Police recorded crime.

Police crime data were filtered to ensure that only race hate crimes related to anti-black/west/south Asian offences, and religious hate crimes related to anti-Islam/Muslim offences were included in the measures. In addition to total police recorded racially and religiously aggravated offences ( N = 6,572), data were broken down into three categories: racially and religiously aggravated violence against the person, criminal damage and harassment reflecting Part II of the Crime and Disorder Act 1998.

Independent measures

Social media regressors.

Twitter data were used to derive two measures. Count of Geo-coded Twitter posts —21.7 million posts were located within the 4720 London LSOAs over the study window as raw counts (Overall: mean 575; s.d. 1,566; min 0; max 75,788; Between: s.d. 1,451; min 0; max 53,345; Within: s.d. 589; min –23,108; max 28,178). Racial and Religious Online Hate Speech —the London geo-coded Twitter corpus was classified as ‘hateful’ or not (Overall: mean 8; s.d. 15.84; min 0; max 522; Between: s.d. 12.57; min 0; max 297; Within: s.d. 9.63; min –120; max 440). Working with computer scientists, a supervised machine learning classifier was built using the Weka tool to distinguish between ‘hateful’ Twitter posts with a focus on race (in this case anti-black/middle-eastern) and religion (in this case anti-Islam/Muslim), and more general non-‘hateful’ posts. A gold standard dataset of human-coded annotations was generated to train the machine classifier based on a sample of 2,000 tweets. In relation to each tweet, human coders were tasked with selecting from a ternary set of classes (‘yes’, ‘no’, and ‘undecided’) in response to the following question: ‘is this text offensive or antagonistic in terms of race, ethnicity or religion?’ Tweets that achieved 75 per cent agreement and above from four human coders were transposed into a machine learning training dataset (undecided tweets were dropped). Support Vector Machine with Bag of Words feature extraction emerged as most accurate machine learning model, with a precision of 0.89, a retrieval of 0.69 and an overall F-measure of 0.771, above the established threshold of 0.70 in the field of information retrieval ( van Rijsbergen, 1979 ). The final hate dataset consisted of 294,361 tweets, representing 1.4 per cent of total geo-coded tweets in the study window (consistent with previous research, see Williams and Burnap, 2016 ; Williams and Burnap, 2018 ). Our measure of online hate speech is not designed to correspond directly to online hate acts deemed as criminal in the UK law. The threshold for criminal hate speech is high, and legislation is complex (see CPS guidance and Williams et al. , 2019 ). Ours is a measure of online inter-group racial and/or religious tension, akin to offline community tensions that are routinely picked up by neighborhood policing teams. Not all manifestations of such tension are necessarily criminal, but they may be indicative of pending activity that may be criminal. Examples of hate speech tweets in our sample, include: ‘Told you immigration was a mistake. Send the #Muzzies home!’; ‘Integrate or fuck off. No Sharia law. #BurntheQuran’; and ‘Someone fucking knifed on my street! #niggersgohome’. 3

Census regressors

Four measures were derived from 2011 census data based on the literature that estimated hate crime using ecological factors (e.g. Green, 1998 ; Espiritu, 2004 ). These include proportion of population: (1) with no qualifications, (2) aged 16–24, (3) long-term unemployed, and (4) black and minority ethnic (BAME). 4

Methods of estimation

The estimation process began with a single-level model that collapsed the individual 8 months worth of police hate crime and Twitter data into one time period. Because of the skewed distribution of the data and the presence of over-dispersion, a negative binomial regression model was selected. These non-panel models provide a baseline against which to compare the second phase of modelling. To incorporate the temporal variability of police recorded crime and Twitter data, the second phase of modelling adopted a random- and fixed-effects regression framework. The first step was to test if this framework was an improvement upon the non-panel model that did not take into account time variability. The Breusch–Pagan Lagrange multiplier test revealed random-effects regression was favourable over single-level regression. Random effects modelling allows for the inclusion of time-variant (police and Twitter data) and time-invariant variables (census measures). Both types of variable were grouped into the 4720 LSOA areas that make up London. Using LSOA as the unit of analysis in the models allowed for an ‘ecological’ appraisal of the explanatory power of race and religious hate tweets for estimating police recorded racially and religiously aggravated offences ( Sampson, 2012 ). When the error term of an LSOA is correlated with the variables in the model, selection bias results from time-invariant unobservables, rendering random effects inconsistent. The alternative fixed-effects model that is based on within-borough variation removes such sources of bias by controlling for observed and unobserved ecological factors. Therefore, both random- and fixed-effects estimates are produced for all models. 5 A Poisson model was chosen over negative binomial, as the literature suggests the latter does not produce genuine fixed-effects (FE) estimations. 6 In addition, Poisson random-/fixed-effects (RE/FE) estimation with robust standard errors is recognized as the most reliable option in the presence of over-dispersion ( Wooldridge, 1999 ). There were no issues with multicollinearity in the final models.

Figures 4–7 show scatterplots with a fitted lined (95% confidence interval in grey) of the three types of racially and religiously aggravated offences (plus combined) by race and religious hate speech on Twitter over the whole eight-month period. Scatterplots indicated a positive relationship between the variables. Two LSOAs emerged as clear outliers (LSOA E01004736 and E01004763: see Figures 8–9 ) and required further inspection (not included in scatter plots). A jackknife resampling method was used to confirm if these LSOAs (and others) were influential points. This method fits a negative binomial model in 4,720 iterations while suppressing one observation at a time, allowing for the effect of each suppression on the model to be identified; in plain terms, it allows us to see how much each LSOA influences the estimations. Inspection of a scatterplot of dfbeta values (the amount that a particular parameter changes when an observation is suppressed) confirmed the above LSOAs as influential points, and in addition E01002444 (Hillingdon, in particular Heathrow Airport) and E01004733 (Westminster). The decision was made to build all models with and without outliers to identify any significant differences. The inclusion of all four outliers did change the magnitude of effects, standard errors and significance levels for some variables and model fit, so they were removed in the final models.

Hate tweets by R & R aggravated violence against the person

Hate tweets by R & R aggravated harassment

Hate tweets by R & R aggravated criminal damage

Hate tweets by R & R aggravated offences combined

Outlier LSOA E01004736

Outlier LSOA E01004763

Table 1 presents results from the negative binomial models for each type of racially and religiously aggravated crime category. These models do not take into account variation over time, so estimates should be considered as representing statistical associations covering the whole eight-month period of data collection, and a baseline against which to compare the panel models presented later. The majority of the census regressors emerge as significantly predictive of all racially and religiously aggravated crimes, broadly confirming previous hate crime research examining similar factors and partly supporting Hypothesis 1. Partly supporting Green (1998) and Nandi (2017) the proportion of the population that is BAME emerged as positively associated with all race and religious hate crimes, with the greatest effect emerging for racially or religiously aggravated violence against the person. Partly confirming work by Bobo and Licari (1989) models shows a positive relationship between the proportion of the population with no qualifications and racially and religiously aggravated violence, criminal damage and total hate crime, but the association only emerged as significant for criminal damage. Proportion of the population aged 16–24 only emerged as significant for criminal damage and total hate crimes, and the relationship was negative, partly contradicting previous work ( Ray et al. , 2004 ; Williams and Tregidga, 2014 ). Like Espiritu (2004) and Ray et al. (2004) , the models show that rates of long-term unemployment were positively associated with all race and religious hate crimes. Although this variable had the greatest effect in the models, we found an inverted U-shape curvilinear relationship (indicated by the significant quadratic term). Figure 10 graphs the relationship, showing as the proportion of the long-term unemployed population increases victimization increases to a mid-turning point of 3.56 per cent where victimization begins to decrease.

Negative binomial models (full 8-month period, N = 4,270)

Notes: Because of the presence of heteroskedasticity robust standard errors are presented. * p < 0.05; ** p < 0.01; *** p < 0.001. All models significant at the 0.0000 level.

Plot of curvilinear relationship between long term unemployment and racially and religiously aggravated crime.

This finding at first seems counter-intuitive, but a closer inspection of the relationship between the proportion of the population that is long-term unemployed and the proportion of the population that is BAME reveals a possible explanation. LSOAs with very high long-term unemployment and BAME populations overlap. Where this overlap is significant, we find relatively low rates of hate crime. For example, LSOA E01001838 in Hackney, in particular the Frampton Park Estate area has 6.1 per cent long-term unemployment, a 68 per cent BAME population and only 2 hate crimes, and LSOA E01003732 in Redbridge has 5.6 per cent long-term unemployment, a 76 per cent BAME population, and only 2 hate crimes. These counts of hate crime either are below or are only slightly above the mean for London (mean = 1.39, maximum = 390). We know from robust longitudinal analysis by Nandi et al. (2017) that minority groups living in very high majority white areas are significantly more likely to report experiencing racial harassment. This risk decreases in high multicultural areas where there is low support for far right groups, such as London. Simple regression (not shown here) where the BAME population proportion was included as the only regressor does show an inverted U-shape relationship with all hate crimes, with the risk of victimization decreasing when the proportion far outweighs the white population. However, this curve was smoothed out when other regressors were included in the models. This analysis therefore suggests that LSOAs with high rates of long-term unemployment but lower rates of hate crime are likely to be those with high proportions of BAME residents, some of whom will be long-term unemployed themselves but unlikely to be perpetrating hate crimes against the ingroup.

Supporting Hypotheses 2, all negative binomial models show online hate speech targeting race and religion is positively associated with all offline racially and religiously aggravated offences, including total hate crimes in London over an eight-month period. The magnitude of the effect is relatively even across offence category. When considering the effect of the Twitter regressors against census regressors, it must be borne in mind the unit of change needed with each regressor to affect the outcome. For example, a percentage change in the BAME population proportion in an LSOA is quite different from a change in the count of hate tweets in the same area. The latter is far more likely to vary to a much greater extent and far more rapidly (see later in this section). The associations identified in these non-panel models indicate a strong link between hateful Twitter posts and offline racially and religiously aggravated crimes in London. Yet, it is not possible with these initial models to state direction of association: We cannot say if online hate speech precedes rather than follows offline hate crime.

Table 2 presents results from RE/FE Poisson models that incorporate variation over space and time . RE/FE models have been used to indicate causal pathways in previous criminological research; however, we suggest such claims in this article would stretch the data beyond their limits. As we adopt an ecological framework, using LSOAs as our unit of analysis, and not individuals, we cannot state with confidence that area-level factors cause the outcome. There are likely sub-LSOA factors that account for causal pathways, but we were unable to observe these in this study design. Nevertheless, the results of the RE/FE models represent a significant improvement over the negative binomial estimations presented earlier and are suitable for subjecting these earlier findings to a more robust test. Indeed, FE models are the most robust test given they are based solely on within-LSOA variation, allowing for the elimination of potential sources of bias by controlling for observed and unobserved ecological characteristics ( Allison, 2009 ). In contrast, RE models only take into account the factors included as regressors. These models therefore allow us to determine if online hate speech precedes rather than follows offline hate crime.

Random and fixed-effects Poisson regression models

Notes: Table shows results of separate random and fixed effects models. To determine if RE or FE is preferred the Hausman test can be used. However, this has been shown to be inefficient, and we prefer not to rely on it for interpreting our models (see Troeger, 2008 ). Therefore, both RE and FE results should be considered together. Because of the presence of heteroskedasticity robust standard errors are presented. Adjusted R 2 for random effects models only. * p < 0.05; ** p < 0.01; *** p < 0.001. All models significant at the 0.0000 level.

The RE/FE modelling was conducted in three stages (Models A to C) to address Hypothesis 3—to assess the magnitude of the change in the variance explained in the outcomes when online hate speech is added as a regressor. Model A includes only the census regressors for the RE estimations, and for all hate crime categories, broadly similar patterns of association emerge compared to the non-panel models. The variance explained by the set of census regressors ranges between 2 per cent and 6 per cent. Such low adjusted R-square values are not unusual for time-invariant regressors in panel models ( Allison, 2009 ).

Models B and C were estimated with RE and FE and introduce the Twitter variables of online hate speech and total count of geo-coded tweets. Model B introduces online hate speech alone, and both RE and FE results show positive significant associations with all hate crime categories. The largest effect in the RE models emerges for harassment (IRR 1.004). For every unit increase in online hate speech a corresponding 0.004 per cent unit increase is observed in the dependent. Put in other terms, an increase of 100 hate tweets would correspond to a 0.4 per cent increase, and an increase of 1,000 tweets would correspond to a 4 per cent increase in racially or religiously aggravated harassment in a given month within a given LSOA. Given we know hate speech online increases dramatically in the aftermath of trigger events (Williams and Burnap, 2015), the first example of an increase of 100 hate tweets in an LSOA is not fanciful. The magnitude of the effect with harassment, compared to the other hate offences, is also expected, given hate-related public order offences, that include causing public fear, alarm and distress, also increased most dramatically in the aftermath the ‘trigger’ events alluded to above (accounting for 56 per cent of all hate crimes recorded by police in 2017/18 ( Home Office, 2018) ). The adjusted R-square statistic for Model B shows large increases in the variance explained in the dependents by the inclusion of online hate speech as a regressor, ranging between 13 per cent and 30 per cent. Interpretation of these large increases should be tempered given time-variant regressors can exert a significant effect in panel models ( Allison, 2009 ). Nonetheless, the significant associations in both RE and FE models and the improvement in the variance explained provide strong support for Hypotheses 2 and 3.

Model C RE and FE estimations control for total counts of geo-coded Tweets, therefore eradicating any variance explained by the hate speech regressor acting as a proxy for population density ( Malleson and Andresen, 2015 ). In all models, the direction of relationship and significance between online hate speech and hate crimes does not change, but the magnitude of the effect does decrease, indicating the regressor was likely also acting, albeit to a small extent, as proxy for population density. The FE models also include an interaction variable between the time-invariant regressor proportion of the population that is BAME and the time-variant regressor online hate speech. The interaction term was significant for all hate crime categories with the strongest effect emerging for racially and religiously aggravated violence against the person. Figure 11 presents a predicted probability plot combining both variables for the outcome of violent hate crime. In an LSOA with a 70 per cent BAME population with 300 hate tweets posted a month, the incidence rate of racially and religiously aggravated violence is predicted to be between 1.75 and 2. However, it must be borne in mind when interpreting these predictions, the skewed distribution of the sample. Just over 70 per cent of LSOAs have a BAME population of 50 per cent or less and 150 or less hate tweets per month, therefore the probability for offences in these areas is between 1 and 1.25 (lower-left dark blue region of the plot). This plot provides predictions based on the model estimates, meaning if in the future populations and hate tweets were to increase toward the upper end of the spectrums, these are the probabilities of observing the racially and religiously aggravated violence in London.

Predicted probability of R & R agg. violence by BAME population proportion and hate tweet count

Our results indicate a consistent positive association between Twitter hate speech targeting race and religion and offline racially and religiously aggravated offences in London. Previous published work indicated an association around events that acted as ‘triggers’ for on and offline hate acts. This study confirms this association is consistent in the presence and absence of events. The models allowed us to provide predictions of the incidence rate of offline offences by proportion of the population that is BAME and the count of online hate tweets. The incidence rate for near three-quarters of LSOAs within London when taking into account these and other factors in the models remains below 1.25. Were the number of hate tweets sent per month to increase dramatically in an area with a high BAME population, our predictions suggest much higher incidence rates. This is noteworthy, given what we know about the impact of ‘trigger’ events and hate speech, and indicates that the role of social media in the process of hate victimization is non-trivial.

Although we were not able to directly test the role of online polarization and far right influence on the prevalence of offline hate crimes, we are confident that our focus on online hate speech acted as a ‘signature’ measure of these two phenomena. Through the various mechanisms outlined in the theoretical work presented in this article, it is plausible to conclude that hate speech posted on social media, an indicator of extreme polarization, influences the frequency of offline hate crimes. However, it is unlikely that online hate speech is directly causal of offline hate crime in isolation. It is more likely the case that social media is only part of the formula, and that local level factors, such as the demographic make-up of neighbourhoods (e.g. black and minority ethnic population proportion, unemployment) and other ecological level factors play key roles, as they always have in estimating hate crime ( Green, 1998 ; Espiritu, 2004 ; Ray et al. , 2004 ). What this study contributes is a data and theory-driven understanding of the relative importance of online hate speech in this formula. If we are to explain hate crime as a process and not a discrete act, with victimization ranging from hate speech through to violent victimization, social media must form part of that understanding ( Bowling, 1993 ; Williams and Tregidga, 2014 ).

Our results provide an opportunity to renew Bowling’s (1993) call to see racism as a continuity of violence, threat and intimidation. We concur that hate crimes must be conceptualized as a process set in geographical, social, historical and political context. We would add that ‘technological’ context is now a key part of this conceptualization. The enduring quality of hate victimization, characterized by repeated or continuous insult, threat, or violence now extends into the online arena and can be linked to its offline manifestation. We argue that hate speech on social media extends ‘climates of unsafety’ experienced by minority groups that transcend individual instances of victimization ( Stanko, 1990 ). Online hate for many minorities is part and parcel of everyday life—as Pearson et al. (1989 : 135) state ‘A black person need never have been the actual victim of a racist attack, but will remain acutely aware that she or he belongs to a group that is threatened in this manner’. This is no less true in the digital age. Social media, through various mechanisms such as unfettered use by the far right, polarization, events, and psychological processes such as deindividuation, has been widely infected with a casual low-level intolerance of the racial Other .

Our study informs the ongoing debate on ‘predictive policing’ using big data and algorithms to find patterns at scale and speed, hitherto unrealizable in law enforcement ( Kaufmann et al. , 2019 ). Much of the criminological literature is critical. The process of pattern identification further embeds existing power dynamics and biases, sharpens the focus on the symptoms and not the causes of criminality, and supports pre-emptive governance by new technological sovereigns ( Chan and Bennett Moses, 2017 ). These valid concerns pertain mainly to predictive policing efforts that apply statistical models to data on crime patterns, offender histories, administrative records and demographic area profiles. These models and data formats tend to produce outcomes that reflect existing patterns and biases because of their historical nature. Our work mitigates some of the existing pitfalls in prediction efforts in three ways: (1) The data used in estimating patterns are not produced by the police, meaning they are immune from inherent biases normally present in the official data generation process; (2) social media data are collected in real-time, reducing the error introduced by ‘old’ data that are no longer reflective of the context; and (3) viewing minority groups as likely victims and not offenders, while not addressing the existing purported bias in ongoing predictive policing efforts, demonstrates how new forms of data and technology can be tailored to achieve alternative outcomes. However, the models reported in this article are not without their flaws, and ahead of their inclusion in real-life applications, we would warn that predictions alone do not necessarily lead to good policing on the streets. As in all statistics, there are degrees of error, and models are only a crude approximation of what might be unfolding on the ground. In particular, algorithmic classification of hate speech is not perfect, and precision, accuracy and recall decays as language shifts over time and space. Therefore, any practical implementation would require a resource-intensive process that ensured algorithms were updated and tested frequently to avoid unacceptable levels of false positives and negatives.

Finally, we consider the methodological implications of this study are as significant as those outlined by Bowling (1993) . Examining the contemporary hate victimization dynamic requires methods that are able to capture both time and space variations in both online and offline data. Increasing sources of data on hate is also important due to continued low rates of reporting. We demonstrated how administrative (police records), survey (census) and new forms of data (Twitter) can be linked to study hate in the digital age. Surveys, interviews and ethnographies should be complemented by these new technological methods of enquiry to enable a more complete examination of the social processes which give rise to contemporary hate crimes. In the digital age, computational criminology, drawing on dynamic data science methods, can be used to study the patterning of online hate speech victimization and associated offline victimization. However, before criminologists and practitioners incorporate social media into their ‘data diets’, awareness of potential forms of bias in these new forms of data is essential. Williams et al . (2017a) identified several sources of bias, including variations in the use of social media (e.g. Twitter being much more popular with younger people). This is particularly pertinent given the recent abandonment of Twitter by many far right users following a clamp-down on hate speech in Europe. A reduction in this type of user may see a corresponding decrease in hate tweets, as they flock to more underground platforms, such as 8chan, 4chan, Gab and Voat, that are currently more difficult to incorporate into research and practical applications. The data used in this study were collected at a time before the social media giants introduced strict hate speech policies. Nonetheless, we would expect hate speech to be displaced, and in time data science solutions will allow us to follow the hate wherever it goes.

The government publication of ‘The Response to Racial Attacks and Harassment’ in 1989 saw a sea-change in the way criminal justice agencies and eventually the public viewed hate crime the United Kingdom ( Home Office, 1989 ). In 2019, the government published its Online Harms White Paper that tries to achieve the same with online hate ( Cabinet Office, 2019 ). Over the past decade, online hate victims have failed to convince others that they are undeserved targets of harm that is sufficiently serious to warrant collective concern, due to insufficient empirical credibility and their subsequent unheard calls for recognition. This research shows that online hate victimization is part of a wider process of harm that can begin on social media and then migrate to the physical world. Qualitative work shows direct individual level links between online and offline hate victimization ( Awan and Zempi, 2017 ). Our study extends this to the ecological level at the scale of the UK’s largest metropolitan area. Despite this significant advancement, we were unable to examine sub-LSOA factors, meaning the individual level mechanisms responsible for the link between online and offline hate incidents remain to be established by more forensic and possibly qualitative work. The combination of the data science-driven results of this study and future qualitative work has the potential to address the reduced capacity of the police to gain intelligence on terrestrial community tensions that lead to hate crimes. Such a technological solution may even assist in the redressing of the bias reportedly present in ‘predictive policing’ efforts, by refocussing the algorithmic lens away from those historically targeted by police, onto those that perpetrate harms against minorities.

This work was supported by the Economic and Social Research Council grant: ‘Centre for Cyberhate Research and Policy: Real-Time Scalable Methods & Infrastructure for Modelling the Spread of Cyberhate on Social Media’ (grant number: ES/P010695/1) and the US Department of Justice National Institute for Justice grant: ‘Understanding Online Hate Speech as a Motivator for Hate Crime’ (grant number: 2016-MU-MU-0009)

Allison , D. P . ( 2009 ), Fixed Effects Regression Models . Sage .

Google Scholar

Google Preview

Awan , I. and Zempi , I . ( 2017 ), ‘I Will Blow Your Face Off’—Virtual and Physical World Anti-Muslim Hate Crime’, British Journal of Criminology , 57 : 362 – 80

Burnap , P. , Rana , O. , Williams , M. , Housley , W. , Edwards , A. , Morgan , J. , Sloan , L. and Conejero , J . ( 2014 ), ‘COSMOS: Towards an Integrated and Scalable Service for Analyzing Social Media on Demand’, IJPSDS , 30 : 80 – 100 .

Burnap , P. and Williams , M. L . ( 2015 ), ‘Cyber Hate Speech on Twitter: An Application of Machine Classification and Statistical Modeling for Policy and Decision Making’ , Policy & Internet. 7 : 223 – 42 .

———. ( 2016 ), ‘Us and Them: Identifying Cyber Hate on Twitter across Multiple Protected Characteristics’ . EPJ Data Science, 5 : 1 – 15

Bail , C. A. , Argyle , L. P. , Brown , T. W. , Bumpus , J. P. , Chen , H. , Hunzaker , M. B. F. , Lee , J. , Mann , M. , Merhout , F. and Volfovsky , A . ( 2018 ), ‘Exposure to Opposing Views on Social Media Can Increase Political Polarization’ PNAS , 115 : 9216 – 21 .

Bobo , L. and Licari , F. C . ( 1989 ), ‘Education and Political Tolerance: Testing The Effects of Cognitive Sophistication and Target Group Affect’ , Public Opinion Quarterly 53 : 285 – 308 .

Bowling , B . ( 1993 ), ‘Racial Harassment and The Process of Victimisation: Conceptual and Methodological Implications for The Local Crime Survey’ , British Journal of Criminology , 33 : 231 – 50 .

Boxell , L. , Gentzkow , M. , and Shapiro , J. M . ( 2017 ), ‘Greater Internet Use Is Not Associated With Faster Growth In Political Polarization Among Us Demographic Groups’ , PNAS , 114 : 10612 – 0617 .

Brady , W. J. , Wills , J. A. , Jost , J. T. , Tucker , J. A. and Van Bavel , J. J . ( 2017 ), ‘Emotion Shapes The Diffusion of Moralized Content in Social Networks’ , PNAS , 114 : 7313 – 18 .

Cabinet Office. ( 2019 ) Internet Safety White Paper . Cabinet Office

Chan , J. and Bennett Moses , L . ( 2017 ), ‘Making Sense of Big Data for Security’ , British Journal of Criminology , 57 : 299 – 319 .

Chokshi , N . ( 2019 ), PewDiePie in Spotlight After New Zealand Shooting . New York Times .

CPS. ( 2018 ), Hate Crime Report 2017–18 . Crown Prosecutions Service .

Crest ( 2017 ), Russian Influence and Interference Measures Following the 2017 UK Terrorist Attacks . Centre for Research and Evidence on Security Threats .

Debois , E. and Blank , G . ( 2017 ), ‘The Echo Chamber is Over-Stated: The Moderating Effect of Political Interest and Diverse Media’ , Information, Communication & Society , 21 : 729 – 45 .

Demos. ( 2017 ), Anti-Islamic Content on Twitter . Demos

Espiritu , A . ( 2004 ), ‘Racial Diversity and Hate Crime Incidents’ , The Social Science Journal , 41 : 197 – 208 .

Green , D. P. , Strolovitch , D. Z. and Wong , J. S . ( 1998 ), ‘Defended Neighbourhoods, Integration and Racially Motivated Crime’ , American Journal of Sociology , 104 : 372 – 403 .

Greer , C. and McLaughlin , E . ( 2010 ), ‘We Predict a Riot? Public Order Policing, New Media Environments and the Rise of the Citizen Journalist’ , British Journal of Criminology , 50 : 1041 – 059 .

Greig-Midlane , J . ( 2014 ), Changing the Beat? The Impact of Austerity on the Neighbourhood Policing Workforce . Cardiff University .

Hanes , E. and Machin , S . ( 2014 ), ‘Hate Crime in the Wake of Terror Attacks: Evidence from 7/7 and 9/11’ , Journal of Contemporary Criminal Justice , 30 : 247 – 67 .

Hawdon , J. , Oksanen , A. and Räsänen , P . ( 2017 ), ‘Exposure To Online Hate In Four Nations: A Cross-National Consideration’ , Deviant Behavior , 38 : 254 – 66 .

Hern , A . ( 2018 ), Facebook Protects Far-Right Activists Even After Rule Breaches . The Guardian .

HMICFRS. ( 2018 ), Understanding the Difference: The Initial Police Response to Hate Crime . Her Majesty’s Inspectorate of Constabulary and Fire and Rescue Service .

Home Office. ( 1989 ), The Response to Racial Attacks and Harassment: Guidance for the Statutory Agencies, Report of the Inter-Departmental Racial Attacks Group . Home Office .

———. ( 2018 ), Hate Crime, England and Wales 2017/18 . Home Office .

Hope Not Hate. ( 2019 ), State of Hate 2019 . Hope Not Hate .

Howard , P. N. and Kollanyi , B . ( 2016 ), Bots, #StringerIn, and #Brexit: Computational Propeganda during the UK-EU Referendum . Unpublished Research Note. Oxford University Press.

Kaufmann , M. , Egbert , S. and Leese , M . ( 2019 ), ‘Predictive Policing and the Politics of Patterns’ , British Journal of Criminology , 59 : 674 – 92 .

Lehman , J . ( 2014 ), A Brief Explanation of the Overton Window . Mackinac Center for Public Policy .

Malleson , N. and Andresen , M. A . ( 2015 ), ‘Spatio-temporal Crime Hotspots and The Ambient Population’ , Crime Science , 4 : 1 – 8 .

Müller , K. and Schwarz , C. ( 2018a ), Making America Hate Again? Twitter and Hate Crime Under Trump . Unpublished working paper. University of Warwick.

———. ( 2018b ), Fanning the Flames of Hate: Social Media and Hate Crime . Unpublished working paper. University of Warwick.

Nandi , A. , Luthra , R. , Saggar , S. and Benzeval , M . ( 2017 ), The Prevalence and Persistence of Ethnic and Racial Harassment and Its Impact on Health: A Longitudinal Analysis . University of Essex .

Ofcom. ( 2018a ), Children and Parents: Media Use and Attitudes . Ofcom

———. ( 2018b ), News Consumption in the UK: 2018 . Ofcom .

———. ( 2018c ), Adults’ Media Use and Attitudes Report . Ofcom

ONS. ( 2017 ), CSEW Estimates of Number of Race and Religion Related Hate Crime in England and Wales, 12 Months Averages, Year Ending March 2014 to Year Ending March 2017 . Office for National Statistics .

Pearson , G. , Sampson , A. , Blagg , H. , Stubbs , P. and Smith , D. J . ( 1989 ), ‘Policing Racism’, in R. Morgan and D. J. Smith , eds., Coming to Terms with Policing: Perspectives on Policy . Routledge .

Peddell , D. , Eyre , M. , McManus , M. and Bonworth , J . ( 2016 ), ‘Influences and Vulnerabilities in Radicalised Lone Actor Terrorists: UK Practitioner Perspectives’ , International Journal of Police Science and Management , 18 : 63 – 76 .

Perry , B. and Olsson , P . ( 2009 ), ‘Cyberhate: The Globalisation of Hate’ , Information & Communications Technology Law , 18 : 185 – 99 .

Pew Research Centre. ( 2018 ), Americans Still Prefer Watching to Reading the News . Pew Research Centre .

Rawlinson , K . ( 2018 ), Finsbury Park-accused Trawled for Far-right Groups Online, Court Told . The Guardian .

Ray , L. , Smith , D. and Wastell , L . ( 2004 ), ‘Shame, Rage and Racist Violence’ , British Journal of Criminology , 44 : 350 – 68 .

Roberts, C., Innes, M., Williams, M. L., Tregidga, J. and Gadd, D. (2013), Understanding Who Commits Hate Crimes and Why They Do It [Project Report]. Welsh Government.

van Rijsbergen , C. J . ( 1979 ), Information Retrieval (2nd ed.), Butterworth .

Sampson , R. J . ( 2012 ), Great American City: Chicago and the Enduring Neighborhood Effect . University of Chicago Press .

Stanko . ( 1990 ), Everyday Violence . Pandora .

Stephan , W. G. and Stephan , C. W . ( 2000 ), An Integrated Threat Theory of Prejudice . Lawrence Erlbaum Associates .

Sunstein , C. R . ( 2017 ), #Republic: Divided Democracy in the Age of Social Media . Princeton University Press .

Troeger, V. E. (2008), ‘Problematic Choices: Testing for Correlated Unit Specific Effects in Panel Data’, Presented at 25th Annual Summer Conference of the Society for Political Methodology, 9–12 July 2008.

Williams, M. L. (2006), Virtually Criminal: Crime, Deviance and Regulation Online . Routledge.

Williams , M. and Burnap , P . ( 2016 ), ‘Cyberhate on Social Media in the Aftermath of Woolwich: A Case Study in Computational Criminology and Big Data’ , British Journal of Criminology , 56 : 211 – 38 .

———. ( 2018 ), Antisemitic Content on Twitter . Community Security Trust .

Williams , M. and Tregidga , J . ( 2014 ), ‘Hate Crime Victimisation in Wales: Psychological and Physical Impacts Across Seven Hate Crime Victim-types’ , British Journal of Criminology , 54 : 946 – 67 .

Williams , M. L. , Burnap , P. and Sloan , L. ( 2017a ), ‘Crime Sensing With Big Data: The Affordances and Limitations of Using Open-source Communications to Estimate Crime Patterns’ , The British Journal of Criminology , 57 : 320 – 40.

———. ( 2017b ), ‘Towards an Ethical Framework for Publishing Twitter Data in Social Research: Taking into Account Users’ Views, Online Context and Algorithmic Estimation’ , Sociology , 51 : 1149 – 68 .

Williams, M. L., Eccles-Williams, H. and Piasecka, I. (2019), Hatred Behind the Screens: A Report on the Rise of Online Hate Speech . Mishcon de Reya.

Williams, M. L., Edwards, A. E., Housley, W., Burnap, P., Rana, O. F., Avis, N. J., Morgan, J. and Sloan, L. (2013), ‘Policing Cyber-Neighbourhoods: Tension Monitoring and Social Media Networks’, Policing and Society , 23: 461–81.

Wooldridge , J. M . ( 1999 ), ‘Distribution-Free Estimation of Some Nonlinear Panel Data Models’ , Journal of Econometrics , 90 : 77 – 97 .

For current CPS guidance on what constitutes an online hate offence see: https://www.cps.gov.uk/legal-guidance/social-media-guidelines-prosecuting-cases-involving-communications-sent-social-media .

Not all hate speech identified reaches the threshold for a criminal offence in England and Wales.

These are not actual tweets from the dataset but are instead constructed illustrations that maintain the original meaning of authentic posts while preserving the anonymity of tweeters (see Williams et al. 2017b for a fuller discussion of ethics of social media research).

Other census measures were excluded due to multicollinearity, including religion.

To determine if RE or FE is preferred, the Hausman test can be used. However, this has been shown to be inefficient, and we prefer not to rely on it for interpreting our models (see Troeger, 2008 ). Therefore, both RE and FE results should be considered together.

See https://www.statalist.org/forums/forum/general-stata-discussion/general/1323497-choosing-between-xtnbreg-fe-bootstrap-and-xtpoisson-fe-cluster-robust .

Email alerts

Citing articles via.

• Recommend to your Library

Affiliations

• Online ISSN 1464-3529
• Print ISSN 0007-0955
• Copyright © 2024 Centre for Crime and Justice Studies (formerly ISTD)
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• 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.

The Rule of Law in Cyberspace pp 283–307 Cite as

Hate Speech and Social Media

• Miguel Nogueira de Brito 6  
• First Online: 07 September 2022

647 Accesses

Part of the book series: Law, Governance and Technology Series ((LGTS,volume 49))

It has become usual to distinguish between a culture of dignity, with large expression in Europe, and a culture of freedom, dominant in the USA, as the two possible horizons to deal with hate speech issues. This differentiation, besides constituting an expedient that evades the increasing need for a strong affirmation of freedom of expression, is becoming largely irrelevant due to the development of a tendency for hate speech self-regulation by the private entities that own the platforms and infrastructures of social networks. Hate speech self-regulation tends to be shaped by an economic model and is potentially controlled by algorithms that escape democratic controls. Instead, the specific political value of freedom of expression should be highlighted as a more consistent basis in its confrontation with other constitutional rights and interests.

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

Buying options

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

Tax calculation will be finalised at checkout

Purchases are for personal use only

See Sunstein ( 2007 ), pp. 5–6, 116, 145 and Sunstein ( 2017 ), pp. 5–13, 122–124, 163.

This contrast was initially explored and deepened (and not only within the freedom of expression domain) by Whitman ( 2000 ), pp. 1279–1398 and Whitman ( 2004 ), pp. 1151–1221. Winfried Brugger reaches much the same conclusion when he claims that the allegation that the Holocaust never happened, for example, if spoken on the steps of the US Capitol would be protected under the First Amendment, whereas if spoken on the steps of the German Reichstag would lead to criminal prosecution. He says that under dominant American jurisprudence, free speech, including the right to utter hate messages, is a preferred right that usually outweighs countervailing interests in dignity, honor, civility, and equality; on the contrary, international law and most non-American modern legal systems assign greater protection to the dignity, honor, and equality interests of the targets of hate speech (Brugger 2002 , pp. 2–3).

See Post ( 2009 ), p. 127.

The type of speech, but also the violence resulting therein are foreseen in number one of article 240 of the Criminal Code, by punishing with sentence of imprisonment from 1 to 8 years whoever: “a) Founds or sets up an organisation, or develops activities of organised propaganda inciting or encouraging discrimination, hate or violence against a person or a group of persons on the grounds of their race, colour, ethnic or national origin, ancestry, religion, sex, sexual orientation, gender identity or physical or physic disability ; or b) Takes part in such an organisation or in its activities referred to in the previous paragraph or gives support thereto, including its financing”. The same mixture of inflamed speech and cause of damage is considered in number two of the same article, pursuant to which it is punished with sentence of imprisonment from 6 months to 5 years “Whoever, publicly, by any mean intended for dissemination, namely through apology, denial or gross banalisation of genocide or war crimes or crimes against peace and humanity: (a) provoke acts of violence against a person or group of persons because of race, colour, ethnic or national origin, ancestry, religion, sex, sexual orientation, gender identity or physical or physic disability; ( b) defame or injure person or group of persons because of race, colour, ethnic or national origin, ancestry, religion, sex, sexual orientation, gender identity or physical or physic disability ; (c) threatening person or group of persons because of race, colour, ethnic or national origin, ancestry, religion, sex, sexual orientation, gender identity or physical or physic disability; or ( d) Incite violence or hate against person or group of persons because of their race, colour, ethnic or national origin, ancestry, religion, sex, sexual orientation, gender identity or physical or physic disability ”.

See Post ( 2009 ), p. 128.

See Post ( 2009 ), p. 129.

See Waldron ( 2012 ), p. 11.

See Post ( 2009 ), p. 131, transcribing statements issued at a debate which took place in the British Parliament in 1930, where a law permitting to demonstrate religious dissidence, insofar as the “decencies of the controversy” were respected, was tried to be revoked.

See Post ( 2009 ), p. 135.

See Iglezakis ( 2017 ), p. 369 and Banks ( 2010 ), p. 236.

See Mounk ( 2018 ), pp. 16 and 146 ff.

See Mounk ( 2018 ), p. 149.

Cf. Vesting ( 2019 ), pp. 33 ff.

See Sunstein ( 2017 ), pp. 1, 5–7.

See Sunstein ( 2017 ), pp. 66–67.

See Sunstein ( 2017 ), p. 102.

See Bentley et al. ( 2011 ), pp. 68–69.

See Sunstein ( 2017 ), pp. 108 ff.; 117 ff., 122 ff.

See Sunstein ( 2017 ), p. 135.

See Balkin ( 2004 ), pp. 6–8.

See Morozov ( 2011 ), p. 14.

See Morozov ( 2011 ) , p. 201.

Morozov ( 2011 ), pp. 189 ff, speaks in this regard of a “slacktivism”.

See Morozov ( 2011 ), p. 196.

See Morozov ( 2011 ), pp. 200–201.

See Balkin ( 2004 ), p. 13.

See Rose ( 2016 ), p. 158.

See Post ( 2012 ), p. 25; in the same direction, see Post ( 2009 ), p. 137.

See Suk, pp. 144 ff.

See Heinze ( 2016 ), p. 7.

In this regard, see the influence of the works of Erving Goffman on the analysis of Whitman ( 2000 ), esp. pp. 1382–1383 and Post ( 2009 ), p. 129. Criticising, however, the manner how Post takes advantage of the thesis of Erving Goffman, see Whitman ( 2000 ), p. 1383, note 353 and Whitman ( 2004 ), p. 1168.

Pursuant to Schauer ( 2005 ), p. 56: “I try to identify and try to start explaining differences, leaving the normative evaluation of such differences to other persons or other times”.

See Schauer ( 2005 ), p. 34 and Iglezakis ( 2017 ), p. 372. The Additional Protocol to the Convention on Cybercrime is in force in Portugal since 2010, having been approved by the Resolution of the Assembly of the Republic no. 91/2009, ratified by the Decree from the President of the Republic no. 94/2009, both from 15 September and published at the Official Gazette of that same day.

See Iglezakis ( 2017 ), p. 373 and Banks ( 2010 ), p. 236.

See Whitman ( 2000 ), p. 1381.

See Whitman ( 2000 ), pp. 1395–1396.

See Whitman ( 2000 ), p. 1385.

See Whitman ( 2000 ), pp. 1285, 1387.

See Van Mill ( 2017 ), p. 46.

See Schauer ( 2005 ), p. 45.

See Schauer ( 2005 ), pp. 46, 47.

See Schauer ( 2005 ), pp. 48–49.

See Schauer ( 2005 ), p. 51.

See Jacobson and Schlink, p. 241.

An illustrative example is the definition of harassment included in the “Harassment Policy and Procedures” of Duke University, in the United States: “Harassment of any individual for any reason is not acceptable at Duke University. Harassment is a form of discrimination prohibited and may arise in exclusive situations of a certain interpersonal relationship or in actions rooted in an attitude towards a group. Sexual harassment is perhaps the most commonly understood form of harassment, but it is important to note that harassment on any demographic basis—including age, colour, disability, national origin, sex, gender identity, gender expression, race, religion, class, veteran status, institutional status or sexual orientation—also occurs and is expressly prohibited” (available at https://oie.duke.edu/sites/default/files/u32/Harassment%20Policy%20and%20Procedures%20Nov2%202018.pdf , last access on 3 November 2018). The problem of this definition is not only its wide nature, but rather, as noted by Jacobson and Schlink, pp. 234–235, the circumstance that its applicability occurs outside the judicial scope, with the inherent increase of discretion, within a scope where discovery of the truth is only one of the interests to be pursued. Another example of restriction of freedom of expression in universities is given by the so-called “ hate speech codes ”: Chemerinsky and Gillman ( 2018 ), pp. 97 ff.

See Sunstein ( 1995 ), p. 197.

See Jacobson and Schlink, pp. 238–239.

See Marx, https://www.marxists.org/archive/marx/works/1844/jewish-question/ , last access on November 19, 2020.

With the spread of global communication structures made possible by the Internet, the ‘private sphere’ of Internet communication acquires a public function and an obligation to the public. The public character of the sphere of Internet communication is its intrinsic normativity in relation to society and to human individuals. One of the consequences of this process is that global communication structures must be considered as obligated parties of fundamental rights (cf. Fischer-Lescano 2016 , p. 162).

See Balkin ( 2014 ), p. 2297.

See Balkin ( 2014 ), p. 2341.

See Klonick ( 2018 ), pp. 1601, 1669.

See these examples, and more, in Jeffrey (2008), available at https://www.nytimes.com/2008/11/30/magazine/30google-t.html ; Klonick ( 2018 ), pp. 1623–1624.

Section 230 of the Communications Decency Act immunizes providers of “interactive computer services” from liability arising from user-generated content: see Klonick ( 2018 ), p. 1603 ff.

See Klonick ( 2018 ), p. 1627.

See Balkin ( 2009 ), p. 427.

See Waldron ( 2012 ), pp. 88 and 165; for criticism, see Van Mill ( 2017 ), pp. 94 ff. As this last author notes, op. cit. , p. 98, “The reason why Waldron thinks an attack on dignity is wrong is presumably because it causes harm to the victim in some way. The real harm of hate speech, portrayed in the sorts of images he discusses, is that it involves threats to peace, safety, physical integrity, and psychological well-being. It has the capacity to incite violence and it leaves people intimidated vulnerable and afraid. Preventing hate speech is about protecting people from the effects of stirred up hatred, and only if an attack on dignity is equivalent to these sorts of infringements would it count as a reason for limiting speech”. If so, it is better to assume that freedom of expression is, a prima facie, right subject, as any other, to weighting in the confrontation with other rights and interests, instead of trying to oppose freedom of speech, as justification for the non-regulation of hate speech, and dignity, as justification for its regulation.

See Van Mill ( 2017 ), p. 105. A similar argument, in a certain extent, may be found in Alexander ( 2005 ), p. 185.

See Weinstein ( 2009 ), pp. 81–82; See also Sunstein ( 1995 ), pp. 11–14, 167–173.

See Weinstein ( 2009 ), p. 82.

See Heinze ( 2016 ), pp. 21–22.

Some withdraw thereof the impossibility to ground freedom of expression as an autonomous right, as it occurs with Alexander ( 2005 ), p. 175: “If liberalism is the correct political philosophy, then it cannot attach value to messages that undermine it, just as if freedom of expression is valuable, advocacy of its abolition cannot be”.

See Heinze ( 2016 ), p. 22.

See Heinze ( 2016 ), p. 20.

See Sunstein ( 1995 ), pp. 9–10. On commercial speech, see Bezanson ( 2012 ), pp. 7–64.

Regarding these theses, and highlighting their similarity and overlapping in important aspects, see Machado ( 2002 ), pp. 237 ff. and Sankievicz ( 2011 ), pp. 19 ff. and Neto ( 2017 ), pp. 37 ff.

See Post ( 2011 ), p. 479.

See Sunstein ( 1995 ), pp. 17–18.

See Post ( 2011 ), pp. 479–482, 487–488.

See Post ( 2011 ), p. 483; see also Hoffmann-Riem and Schulz ( 1998 ), p. 157.

See Urbinati ( 2014 ), p. 2.

See Dworkin, R, p. 342. Dworkin, as is well known, distinguishes between an instrumental justification of freedom of expression and a constitutive justification. The former seeks to justify freedom of expression due to its benefic effects, defending that if freedom of expression is observed, it will be easier to discover the truth and produce good public policies; the second seeks to justify freedom of expression with the argument that a fair political society is only possible if freedom of expression is respected (Dworkin 1996 , pp. 199–200). Dworkin’ view is often presented as an example of an intrinsic or deontological type of approach, based on the value of self-determination, in opposition to an instrumental approach, according to which freedom of expression makes easier the discovery of the truth or the deepening democracy (see Neto 2017 , p. 48). On the contrary, there is no incompatibility between the conception of Dworkin and the one that views freedom of expression as essential for the idea of self-government.

See Schauer ( 1982 ), p. 46.

See Post ( 1995 ), p. 1275.

See Sunstein ( 1995 ), p. 119.

See Rosen ( 2018 ).

See De Tocqueville ( 1961 ), p. 185 (my translation).

Alexander L (2005) Is there a right of Freedom of expression? Cambridge University Press

Book   Google Scholar  

Balkin J (2004) Digital speech and democratic culture: a theory of freedom of expression for the information society. New York Univ Law Rev LXXIX-1

Google Scholar  

Balkin J (2009) The future of free expression in a digital age. Pepperdine Law Rev, XXXVI

Balkin J (2014) Old-school/new-school speech regulation. Harv Law Rev, 127

Banks J (2010) Regulating hate speech online. Int Rev Law Comput Technol, XXIV-3

Bentley A, Earls M, O’Brien M (2011) I’ll have what she’s having: mapping social behavior. The MIT Press, Cambridge

Bezanson R (2012) Too much free speech? University of Illinois Press, Urbana

Brugger W (2002) Ban on or protection of hate speech? Some observations based on German and American Law. Tulane European & Civil Law Forum, 17

Chemerinsky E, Gillman H (2018) Free Speech on Campus. Yale University Press, New Haven and London

De Tocqueville A (1961) De la Démocratie em Amérique. Gallimard, Paris, Deuxième Partie, Chapitre III

Dworkin R (1996) Freedom’s law: the moral reading of the American constitution. Harvard University Press, Cambridge

Dworkin R. Reply to Jeremy Waldron. In: Herz, M, Moldnar, P (eds) The Content and Context of Hate Speech: Rethinking Regulation and Responses

Fischer-Lescano A (2016) Struggles for a global Internet constitution: protecting global communication structures against surveillance measures. Glob Const 5:2

Heinze E (2016) Hate speech and democratic citizenship. Oxford University Press

Hoffmann-Riem W, Schulz W (1998) Politische Kommunikation – Rechtswissenschaftliche Perspektiven. In: Jarren O, Sarcinelli R, Saxer U (eds) Politische Kommunikation in der demokratischen Gesellschaft. Ein Handbuch mit Lexikonteil. Westdeutscher Verlag, Opladen/Wiesbaden

Iglezakis, I (2017) The legal regulation of hate speech on the internet. In: Tatiana-Eleni et al (orgs) EU Internet law. Regulation and enforcement. Springer, Cham

Jacobson, A, Schlink, B. Hate Speech and Self-Restraint. Herz, M, Molnar, P (orgs) The Content and Context of Hate Speech: Rethinking Regulation and Responses

Klonick J (2018) The new governors: the people, rules, and processes governing online speech. Harv Law Rev, CXXXI

Machado J (2002) Liberdade de Expressão: Dimensões Constitucionais da Esfera Pública no Sistema Social. Coimbra Editora, Coimbra

Marx K, On the Jewish Question. https://www.marxists.org/archive/marx/works/1844/jewish-question/

Morozov E (2011) The net delusion: the dark side of internet freedom. Public Affairs, New York

Mounk Y (2018) The People vs. democracy: why our freedom is in danger and how to save it. Harvard University Press, Cambridge

Neto J (2017) Liberdade de Expressão: O Conflito entre o Legislador e o Juiz Constitucional. Saraiva, São Paulo

Post R (1995) Recuperating First Amendment Doctrine. Stanford Law Rev, XLVI

Post R (2009) Hate speech. In: Hare I, Weinstein J (orgs) Extreme speech and democracy. Oxford University Press, Oxford

Post R (2011) Participatory democracy and free speech. Virginia Law Rev, XCVII-3

Post R (2012) Interview. In: Herz, M, Molnar, P (orgs) The content and context of hate speech: rethinking regulation and responses. Cambridge University Press

Rose F (2016) The Tyranny of silence: how one cartoon ignited a global debate on the future of free speech. Cato Institute, Washington

Rosen J (2018) The Delete Squad: Google, Twitter, Facebook and the new global battle over the future of free speech. The New Republic. https://newrepublic.com/article/113045/free-speech-internet-silicon-valley-making-rules

Sankievicz A (2011) Liberdade de Expressão e Pluralismo: Perspetivas de Regulação. Saraiva, São Paulo

Schauer F (1982) Free Speech: a philosophical enquiry. Cambridge University Press, Cambridge

Schauer, F (2005) The Exceptional First Amendment. In: Ignatieff, M (org) American exceptionalism and human rights. Princeton University Press, Princeton

Suk J, Denying Experience: Holocaust Denial and the Free-Speech Theory of the State. In: Herz, M, Molnar, P (orgs) The Content and Context of Hate Speech: Rethinking Regulation and Responses

Sunstein C (1995) Democracy and the problem of free speech. The Free Press, New York

Sunstein C (2007) Republic.com 2.0. Princeton University Press, Princeton

Sunstein C (2017) #Republic: divided democracy in the age of social media. Princeton University Press, Princeton

Urbinati N (2014) Democracy disfigured: opinion, truth, and the people. Harvard University Press, Cambridge

Van Mill D (2017) Free Speech and the State: An Unprincipled Approach. Palgrave Macmillan, Cham Jacobson, A, Schlink, Hate Speech and Self-Restraint. In: Herz M, Molnar P (orgs) The Content and Context of Hate Speech: Rethinking Regulation and Responses

Vesting T (2019) Die Veränderung der Öffentlichkeit durch künstliche Intelligenz. In: Unger S, von Ungern-Sternberg A (eds) Demokratie und künstliche Intelligenz. Tübingen, Mohr Siebeck

Waldron J (2012) The harm in hate speech. Harvard University Press, Cambridge

Weinstein, J (2009) Am overview of American free speech doctrine and its application to extreme speech. In: Hare, I, Weinstein, J (orgs) Extreme speech and democracy. Oxford University Press, Oxford

Whitman J (2000) Enforcing civility and respect: three societies. Yale Law J, CIX:1279–1398

Whitman J (2004) The two Western cultures of privacy: dignity versus liberty. Yale Law J CXIII:1151–1221

Other Legal Documents

Harassment Policy and Procedures. Duke University, United States: https://oie.duke.edu/sites/default/files/u32/Harassment%20Policy%20and%20Procedures%20Nov2%202018.pdf , last access on 3 November 2018).

Download references

Author information

Authors and affiliations.

University of Lisbon School of Law, Lisbon, Portugal

Miguel Nogueira de Brito

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Miguel Nogueira de Brito .

Editor information

Editors and affiliations.

School of Law, University of Lisbon, Lisbon, Portugal

Carlos Blanco de Morais

Supremo Tribunal Federal, Brazilian Supreme Court (STF), Brasília, Brazil

Gilmar Ferreira Mendes

Faculty of Law, Goethe University Frankfurt, Frankfurt am Main, Germany

Thomas Vesting

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Cite this chapter.

de Brito, M.N. (2022). Hate Speech and Social Media. In: Blanco de Morais, C., Ferreira Mendes, G., Vesting, T. (eds) The Rule of Law in Cyberspace. Law, Governance and Technology Series, vol 49. Springer, Cham. https://doi.org/10.1007/978-3-031-07377-9_15

Download citation

DOI : https://doi.org/10.1007/978-3-031-07377-9_15

Published : 07 September 2022

Publisher Name : Springer, Cham

Print ISBN : 978-3-031-07376-2

Online ISBN : 978-3-031-07377-9

eBook Packages : Law and Criminology Law and Criminology (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

Help | Advanced Search

Computer Science > Machine Learning

Title: cross-platform hate speech detection with weakly supervised causal disentanglement.

Abstract: Content moderation faces a challenging task as social media's ability to spread hate speech contrasts with its role in promoting global connectivity. With rapidly evolving slang and hate speech, the adaptability of conventional deep learning to the fluid landscape of online dialogue remains limited. In response, causality inspired disentanglement has shown promise by segregating platform specific peculiarities from universal hate indicators. However, its dependency on available ground truth target labels for discerning these nuances faces practical hurdles with the incessant evolution of platforms and the mutable nature of hate speech. Using confidence based reweighting and contrastive regularization, this study presents HATE WATCH, a novel framework of weakly supervised causal disentanglement that circumvents the need for explicit target labeling and effectively disentangles input features into invariant representations of hate. Empirical validation across platforms two with target labels and two without positions HATE WATCH as a novel method in cross platform hate speech detection with superior performance. HATE WATCH advances scalable content moderation techniques towards developing safer online communities.

Submission history

Access paper:.

• HTML (experimental)
• Other Formats

References & Citations

• Google Scholar
• Semantic Scholar

BibTeX formatted citation

Bibliographic and Citation Tools

Code, data and media associated with this article, recommenders and search tools.

• Institution

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs .