Abstract
Objectives
The aim of the present study is to explore whether there is a temporal association between anti-Islamic online and offline hate, and if so in which direction.
Methods
We used data on hateful Twitter content, and hate incidents and hate crimes/offences recorded by the Metropolitan Police Service in the United Kingdom to analyse this association, using time-series analysis. This study is unique in its use of newly developed technology to undertake big data analysis with recent, disaggregated online and offline hate data.
Results
Our study examined the ‘everyday’ incidents of (online and offline) hate that affect communities throughout the United Kingdom and we found that anti-Islamic hate speech followed rather than preceded Islamophobic hate offline.
Conclusions
Our findings likely point to what we have referred to as compound retaliation, which suggests that media and social media dissemination about offline acts of hate compound already tense intergroup hostilities, providing further permission for those to express hatred online. Such a situation represents the compounding of hate and hostility through offline and online networks that are likely to be reinforcing.
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Data Availability
The data that support the findings of this study were made available by the Metropolitan Police Service and Demos but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available.
Notes
In this paper, the terms hate crime and hate offences will be used interchangeably and are to be distinguished from hate incidents.
However, such statistics are not fully developed yet and need to be viewed with caution at this point (Home Office 2018). For an outline of criminal offences please see The Law Commission (2014, ch. 2) report. Even though not specifically designed to deal with online hate, the stirring up of racial, religious, or sexual orientation hatred offences under Part 3 and 3A of the Public Order Act 1986 can be used to prosecute offences committed via social media. Further, the Communications Act 2003, s. 127 or the Malicious Communications Act 1988, s. 1 can also be used to proscribe ‘grossly offensive’ online communication to which Sect. 145 or 146 Criminal Justice Act 2003 sentence enhancements may be applied. However, according to Bakalis (2018, p. 87), legislation is in place ‘to tackle various aspects of cyberhate, but in practice, the existing offences are difficult to use’.
We want to acknowledge here that there is still an ongoing debate about using the term Islamophobia (see, e.g., Irfan 2021). Concerns include the vague nature of the term and that it ‘target[s] expressions of Muslimness or ‘perceived Muslimness’, rather than bigotry against Muslim individuals themselves’ (Malik 2019). Critique of using the term Islamophobia to describe our Twitter dataset included ‘conflat[ing] criticism of an idea (Islam) with abuse of people (Muslims), and that words like Islamophobia are used to shut down any kind of criticism of Islam’ (Miller 2016). Recently, the debate has shifted towards considering the term anti-Muslim, which refers more specifically to antipathy towards Muslims, but which has also been criticized for not encompassing non-Muslims (e.g. Sikhs) or mosques/schools as targets of such hate crimes/incidents (Irfan 2021). This debate is currently still ongoing and is in need of further scrutiny in the future; however, as Irfan (2021) states: ‘If the debate continues to focus on which term better describes the same phenomenon, we run the risk of getting lost in specifics of wordings and of policies and actions never being approved because no one can define them in one way’. In this paper, we use the terms Islamophobic (used by the Metropolitan Police Service when flagging such hate crimes) and anti-Islamic (terminology that describes our Twitter data) predominantly. In addition, the term anti-Muslim will be used when referring to the work of authors who have made specific reference to such terminology within their scholarship. Collectively, we use the terms Islamophobic, anti-Islamic and anti-Muslim to refer to online and offline incidents that are perceived to express prejudice or hostility towards Muslim people.
This research was first presented at the International Network for Hate Studies Conference in Canada in May 2018 and at the Law Commission Hate Crime Research Conference in the UK in March 2019.
Levin and McDevitt (1993) originally proposed three types of offender motivation (thrill, defensive, and mission) and later added retaliatory motivation to their original offender typology, where ‘retaliatory offenders are inspired by a desire to avenge a perceived degradation or assault on their group’ (McDevitt et al. 2002, p. 306). Compound retaliation is an expansion of this offender motivation.
We acknowledge that there are difficulties with automatically classifying online hate speech using a collection of slurs and that we may have missed hateful content; however, ‘certain terms are particularly useful for distinguishing between hate speech and offensive language’ (Davidson et al. 2017, p. 515) and our search term list includes many hateful slurs. Since the end of our project in 2017, data collection methods have also further evolved (see, e.g., Alorainy et al. 2019; Vidgen and Yasseri 2020).
In particular, the ‘Track API’ (https://developer.twitter.com/en/docs/tweets/filter-realtime/overview/statuses-filter) was used to establish an ongoing, real-time collection of tweets matching a keyword, and the ‘Standard search’ API (https://developer.twitter.com/en/docs/tweets/search/overview/standard) was used to return tweets matching a keyword sent in the seven days prior to beginning the collection. Each of these APIs can be accessed free of charge.
More Twitter statistics can be found here: https://www.internetlivestats.com/twitter-statistics/.
Uses of the term 'p*ki' which were not deemed to be hateful included uses of the term as shorthand for Pakistan, particularly with reference to cricket matches, or as an abbreviation of 'Pakistani' where no other hateful messaging was included. We also termed non-hateful some colloquial uses, which could be argued to be culturally problematic, such as the use of 'p*ki' as a term for a corner shop.
This string allowed us to capture different terms, such as terrorism, terrorist and terrorists.
A full list of collection terms can be found in the following paper, published by Demos: https://www.demos.co.uk/wp-content/uploads/2017/04/Results-Methods-Paper-MOPAC-SUMMIT-Demos.pdf
Due to restrictions placed by Twitter on the inaccessibility of deleted or removed tweets, some of the content identified as hateful by Method52 may since have been deleted or removed from the platform, and is no longer accessible to researchers. However, a Demos report by Miller et al. (2016) includes some more information and exploration of the data. Previous research also exists, exploring Islamophobic tweets (see, e.g., Awan 2016c) and anti-Muslim hate on Facebook (see, e.g., Oboler 2016).
More information can be found here: https://www.gov.uk/government/news/home-secretary-announces-new-national-online-hate-crime-hub.
References
Alam Y, Husband C (2013) Islamophobia, community cohesion and counter-terrorism policies in Britain. Patterns Prejudice 47:235–252
Allen C (2017) Britain must address the pervasive ‘white noise’ against Muslims. The Conversation. https://theconversation.com/britain-must-address-the-pervasive-white-noise-against-muslims-79770
Allport GW (1954) The nature of prejudice. Addison-Wesley, Reading
Alorainy W, Burnap P, Liu H, Williams ML (2019) The enemy among us: detecting cyber hate speech with threats-based othering language embeddings. ACM Trans Web 13(3):1–26
Alsaad A, Taamneh A, Al-Jedaiah MN (2018) Does social media increase racist behavior? An examination of confirmation bias theory. Technol Soc 55:41–46
Amisano G, Giannini C (1997) Topics in structural VAR econometrics. Springer-Verlag, Heidelberg
Anderson CA, Bushman BJ (2001) Effects of violent video games on aggressive behavior, aggressive cognition, aggressive affect, physiological arousal, and prosocial behavior: a meta-analytic review of the scientific literature. Psychol Sci 12:353–359
Awan I (2014) Islamophobia and twitter: a typology of online hate against muslims on social media. Policy Internet 6:133–150
Awan I (2016a) Cyber-Islamophobia and internet hate crime. In: Awan I (ed) Islamophobia in cyberspace - Hate crimes go viral, 1st edn. Ashgate Publishing, Oxon, New York, pp 7–22
Awan I (2016b) Islamophobia, hate crime and the internet. In: Awan I (ed) Islamophobia in cyberspace - Hate crimes go viral, 1st edn. Ashgate Publishing, Oxon, New York, pp 167–187
Awan I (2016c) Virtual Islamophobia: The eight faces of anti-Muslim trolls on Twitter. In: Awan I (ed) Islamophobia in cyberspace - Hate crimes go viral, 1st edn. Ashgate Publishing, Oxon, New York, pp 23–39
Awan I, Zempi I (2015) We fear for our lives: offline and online experiences of anti-Muslim hostility. TellMAMA, London. https://www.tellmamauk.org/wp-content/uploads/resources/We%20Fear%20For%20Our%20Lives.pdf
Awan I, Zempi I (2016) The affinity between online and offline anti-Muslim hate crime: dynamics and impacts. Aggress Violent Beh 27:1–8
Awan I, Zempi I (2017) I will blow your face off’ – Virtual and physical world anti-Muslim hate crime. Br J Criminol 57:362–380
Bakalis C (2018) Rethinking cyberhate laws. Inform Commun Technol Law 27(1):86–110
Bartlett J, Birdwell J, Littler M (2011) The new face of digital populism. London: Demos. https://demosuk.wpengine.com/files/Demos_OSIPOP_Book-web_03.pdf?1320601634
Bartlett J, Reffin J, Rumball N, Williamson S (2014) Anti-social media. Demos, London. https://www.demos.co.uk/files/DEMOS_Anti-social_Media.pdf
Benesch S (2013) Dangerous speech: a proposal to prevent group violence. http://dangerousspeech.org/guidelines/
Binns A (2013) Facebook’s ugly sisters: anonymity and abuse on Formspring and Ask.fm. Media Education Research Journal
Binns A (2014) Twitter city and facebook village: teenage girls’ personas and experiences influenced by choice architecture in social networking sites. J Media Pract 15:71–91
Blakemore B (2016) Online hate and political activist groups. In: Awan I (ed) Islamophobia in cyberspace - Hate crimes go viral, 1st edn. Ashgate Publishing, Oxon, New York, pp 63–83
Bowling B (1993) Racial harassment and the process of victimization: conceptual and methodological implications for the local crime survey. Br J Criminol 33:231–250
Bowling B (1998) Violent racism: victimisation, policing, and social context. Ocford University Press, New York
Burke J (2019) Norway mosque attack suspect 'inspired by Christchurch and El Paso shootings'. The Guardian. https://www.theguardian.com/world/2019/aug/11/norway-mosque-attack-suspect-may-have-been-inspired-by-christchurch-and-el-paso-shootings
Carson JV, Dugan L, Yang SM (2020) A comprehensive application of rational choice theory: how costs imposed by, and benefits derived from, the U.S. Federal Government affect incidents perpetrated by the Radical Eco-Movement. J Quant Criminol 36:701–724
Chan J, Ghose A, Seamans R (2016) The internet and racial hate crime: offline spillovers from online access. MIS Q 40:381–404
Cialdini RB, Goldstein NJ (2004) Social influence: compliance and conformity. Annu Rev Psychol 55:591–621
Cialdini RB, Kallgren CA, Reno RR (1991) A focus theory of normative conduct: a theoretical refinement and reevaluation of the role of norms in human behavior. In: Berkowitz L (ed) Advances in experimental social psychology. Academic Press, San Diego, pp 201–234
Cialdini RB, Reno RR, Kallgren CA (1990) A focus theory of normative conduct: recycling the concept of norms to reduce littering in public places. J Pers Soc Psychol 58:1015–1026
Ciftci S (2012) Islamophobia and threat perceptions: explaining anti-Muslim sentiment in the West. J Muslim Minor Aff 32:293–309
College of Policing (2020) Responding to hate. https://www.app.college.police.uk/app-content/major-investigation-and-public-protection/hate-crime/responding-to-hate/#agreed-definitions
Copsey N, Dack J, Littler M, Feldman M (2013) Anti-Muslim hate crime and the far right. Teesside University Centre for Fascist, Anti-Fascist and Post-Fascist Studies. https://research.tees.ac.uk/en/publications/anti-muslim-hate-crime-and-the-far-right
Cuerden G, Rogers C (2017) Exploring race hate crime reporting in Wales following Brexit. Rev Eur Stud 9:158–164
Davidson T, Warmsley D, Macy M, Weber I (2017) Automated hate speech detection and the problem of offensive language. In: Proceedings of the eleventh international AAAI conference on web and social media 1–4
Dodd V (2019) Anti-Muslim hate crimes soar in UK after Christchurch shootings. The Guardian. https://www.theguardian.com/society/2019/mar/22/anti-muslim-hate-crimes-soar-in-uk-after-christchurch-shootings
Dugan L, Chenoweth E (2020) Threat, emboldenment, or both? The effects of political power on violent hate crimes. Criminology 58:714–746
Eckert S, O’Shay Wallace S, Metzger-Riftkin J, Kolhoff S (2018) “The best damn representation of Islam:” Muslims, gender, social media and Islamophobia in the United States. CyberOrient 12(1):4–30
Eckert S, Metzger-Riftkin J, Kolhoff S, O’Shay-Wallace S (2021) A hyper differential counterpublic: Muslim social media users and Islamophobia during the 2016 US presidential election. New Media Soc 23(1):78–98
Eichhorn K (2001) Re-in/citing linguistic injuries: speech acts, cyberhate, and the spatial and temporal character of networked environments. Comput Compos 18:293–304
Federal Bureau of Investigation (2018) 2017 hate crime statistics. U.S. Department of Justice, Criminal Justice Information Services Division. https://ucr.fbi.gov/hate-crime/2017
Feldman M, Littler M (2014) TellMAMA reporting 2013/14: anti-Muslim overview, analysis and ‘cumulative extremism’. https://www.tellmamauk.org/wp-content/uploads/2014/07/finalreport.pdf
Green DP, McFalls LH, Smith JK (2001) Hate crime: an emergent research agenda. Ann Rev Sociol 27:479–504
Hamilton JD (1994) Time series analysis. Princeton University Press, Princeton
Hanes E, Machin S (2014) Hate crime in the wake of terror attacks. J Contemp Crim Justice 30:247–267
Hawdon J (2012) Applying a differential association theory to online hate groups: a theoretical statement. Res Finnish Soc 5:39–47
Hewstone M (1990) The ‘ultimate attribution error’? A review of the literature on intergroup causal attribution. Eur J Soc Psychol 20(4):311–335
Home Office (2016) Action against hate: the UK Government’s plan for tackling hate crime. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/543679/Action_Against_Hate_-_UK_Government_s_Plan_to_Tackle_Hate_Crime_2016.pdf
Home Office (2018) Hate crime, England and Wales, 2017/18. Home Office, London. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/748598/hate-crime-1718-hosb2018.pdf
Home Office (2019) Hate crime, England and Wales, 2018/19 - Appendix tables (Table 1). Home Office, London. https://www.gov.uk/government/statistics/hate-crime-england-and-wales-2018-to-2019
Home Office (2020) Hate crime, England and Wales, 2019 to 2020. Home Office, London. https://www.gov.uk/government/statistics/hate-crime-england-and-wales-2019-to-2020/hate-crime-england-and-wales-2019-to-2020
House of Commons (2018) Disinformation and ‘fake news’: Interim report. https://publications.parliament.uk/pa/cm201719/cmselect/cmcumeds/363/363.pdf
Igarashi A (2020) Hate begets hate: anti-refugee violence increases anti-refugee attitudes in Germany. Ethnic and Racial Studies [online]
Irfan A (2021) Debating hatred: Islamophobia or anti-Muslim hate? Media Diversity Institute. https://www.media-diversity.org/debating-hatred-islamophobia-or-anti-muslim-hate/
Jaki S, De Smedt T (2019) Right-wing German hate speech on twitter: analysis and automatic detection
Kaplan J (2006) Islamophobia in America?: September 11 and Islamophobic hate crime. Terror Polit Viol 18(1):1–33
Kwon KH, Chadha M, Wang F (2019) Proximity and networked news public: structural topic modeling of global Twitter conversations about the 2017 Quebec Mosque Shooting. Int J Commun 13:2652–2675
Leetaru K, Wang S, Cao G, Padmanabhan A, Shook E (2013) Mapping the global twitter heartbeat: the geography of Twitter. First Monday 18(5)
Levin J, McDevitt J (1993) Hate crimes: the rising tide of bigotry and bloodshed. Plenum, New York
Lightowlers C, Chenevoy N, Malleson N, Beeley S, Blair F, Keay S, Bretherton R, Stone K, Eckersley R, Chapman D, Pascale F (2018) Sharing insights on hate crime: new methods and forms of data. N8 Policing Research Partnership. https://www.liverpool.ac.uk/media/livacuk/law-and-social-justice/3research/Sharing,Insights,on,Hate,Crime.pdf
Lütkepohl H (2005) New introduction to multiple time series analysis. Springer-Verlag, Berlin
Malik N (2019) Instead of Islamophobia, we should focus on defining anti-Muslim hatred. Forbes. https://www.forbes.com/sites/nikitamalik/2019/05/20/instead-of-islamophobia-we-should-focus-on-defining-anti-muslim-hatred/?sh=5f12b0ee69e5
Maynard JL, Benesch S (2016) Dangerous speech and dangerous ideology: an integrated model for monitoring and prevention. Genocide Stud Prevent Int J 9:70–95
McDevitt J, Levin J, Bennett S (2002) Hate crime offenders: an expanded typology. J Soc Issues 58:303–317
Miller C (2016) Measuring Islamophobia on Twitter. Demos, London. https://demos.co.uk/blog/measuring-islamophobia-on-twitter/
Miller C, Smith J (2017) Anti-Islamic content on Twitter. Demos, London. https://demos.co.uk/project/anti-islamic-content-on-twitter/
Miller C, Arcostanzo F, Smith J, Krasodomski-Jones A, Wiedlitzka S, Jamali R, Dale J (2016) From Brussels to Brexit: Islamophobia, xenophobia, racism and reports of hateful incidents on Twitter. Demos, London. http://www.demos.co.uk/wp-content/uploads/2016/07/From-Brussels-to-Brexit_-Islamophobia-Xenophobia-Racism-and-Reports-of-Hateful-Incidents-on-Twitter-Research-Prepared-for-Channel-4-Dispatches-%E2%80%98Racist-Britain%E2%80%99-.pdf
MOPAC (2016). Home Office Police Innovation Fund - Online Hate Crime Hub. https://www.london.gov.uk/sites/default/files/pcd_41_home_office_police_innovation_fund_-_online_hate_crime_hub_0.pdf
Müller K, Schwarz C (2020) Fanning the flames of hate: social media and hate crime. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3082972
O’Neill A (2017) Hate crime, England and Wales, 2016 to 2017. Home Office, London. https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/652136/hate-crime-1617-hosb1717.pdf
Oboler A (2016) The normalisation of Islamophobia through social media: facebook. In: Awan I (ed) Islamophobia in cyberspace - Hate crimes go viral. Ashgate Publishing, Oxon, New York, pp 41–61
Oriola O, Kotzé E (2020) Evaluating machine learning techniques for detecting offensive and hate speech in South African tweets. IEEE Access 8:21496–21509
Paterson J, Walters MA, Brown R, Fearn H (2018) The Sussex Hate Crime Project - Final report. https://www.sussex.ac.uk/webteam/gateway/file.php?name=sussex-hate-crime-project-report.pdf&site=430
Perry B, Alvi S (2012) ‘We are all vulnerable’: the in terrorem effects of hate crimes. Int Rev Victimol 18:57–71
Pettigrew TF (1979) The ultimate attribution error: extending Allport’s cognitive analysis of prejudice. Pers Soc Psychol Bull 5(4):461–476
Piatkowska SJ, Lantz B (2021) Temporal clustering of hate crimes in the aftermath of the Brexit vote and terrorist attacks: a comparison of Scotland and England and Wales. Br J Criminol 61(3):648–669
Poynting S (2006) What caused the Cronulla riot? Race & Class 48:85–92
Rahman M (2016) The media impact of online Islamophobia: an analysis of the Wollwich Murder. In: Awan I (ed), Islamophobia in cyberspace: hate crimes go viral. Routledge, Oxon, New York, ch. 5
Roy EA (2019) 'It brings everything back': Christchurch despairs over white supremacist attacks. The Guardian. https://www.theguardian.com/world/2019/aug/14/it-brings-everything-back-christchurch-despairs-over-white-supremacist-attacks
Sadique K, Tangen J, Perowne A (2018) The importance of narrative in responding to hate incidents following ‘trigger’ events. TellMAMA, London. https://tellmamauk.org/wp-content/uploads/resources/Tell%20MAMA%20-%20Report.pdf?utm_source=Report+Launch+Westminster+Bridge+09122018&utm_campaign=Westminster+Bridge+Report+09122018&utm_medium=email
Settles B (2011) Closing the loop: fast, interactive semi-supervised annotation with queries on features and instances. In: EMNLP 2011 - conference on empirical methods in natural language processing. Association for Computational Linguistics, Edinburgh, Scotland, 1467–1478
Sims C (1980) Macroeconomics and reality. Econometrica 48:1–48
Sloan L, Morgan J, Housley W, Williams M, Edwards A, Burnap P, Rana O (2013) Knowing the tweeters: deriving sociologically relevant demographics from Twitter. Sociol Res Online 18(3):74–84
Sutherland EH, Cressey DR (1974) Criminology. J. B. Lippincott, New York
The Law Commission (2014) Hate crime: should the current offences be extended? http://www.lawcom.gov.uk/app/uploads/2015/03/lc348_hate_crime.pdf
Twitter (2015) Fighting abuse to protect freedom of expression. https://blog.twitter.com/official/en_au/a/2015/fighting-abuse-to-protect-freedom-of-expression-au.html
Twitter Safety (2019) Updating our rules against hateful conduct. https://blog.twitter.com/en_in/topics/company/2019/updating-rules-against-hateful-conduct.html
Vidgen B, Yasseri T (2020) Detecting weak and strong Islamophobic hate speech on social media. J Inform Tech Polit 17(1):66–78
Vidgen B, Yasseri T, Margetts H (2019) Trajectories of Islamophobic hate amongst far right actors on Twitter
Walters MA, Krasodomski-Jones A (2018) Patterns of hate crime: who, what, when and where? Demos, London. https://www.demos.co.uk/wp-content/uploads/2018/08/PatternsOfHateCrimeReport-.pdf
Wibberley S, Weir D, Reffin J (2014) Method51 for mining insight from social media datasets. In: COLING 2014, the 25th international conference on computational linguistics: system demonstrations. Dublin, Ireland, pp 115–119
Wibberley S, Reffin J, Weir D (2013) Language technology for agile social media science. In: 7th workshop on language technology for cultural heritage, social sciences, and humanities. Association for Computational Linguistics, Sofia, Bulgaria, pp 36–42
Williams ML, Burnap P (2016) Cyberhate on social media in the aftermath of Woolwich: a case study in computational criminology and big data. Br J Criminol 56:211–238
Williams ML, Burnap P, Javed A, Liu H, Ozalp S (2020) Hate in the machine: anti-black and anti-Muslim social media posts as predictors of offline racially and religiously aggravated crime. Br J Criminol 60(1):93–117
Wilson RA, Land MK (2021) Hate speech on social media: content moderation in context. Conn Law Rev 52(3):1029–1076
Wong JC (2019) 8chan: the far-right website linked to the rise in hate crimes. The Guardian. https://www.theguardian.com/technology/2019/aug/04/mass-shootings-el-paso-texas-dayton-ohio-8chan-far-right-website
Zuleta L, Burkal R (2017) Hate speech in the public online debate. The Danish Institute for Human Rights, Copenhagen. https://www.humanrights.dk/sites/humanrights.dk/files/media/dokumenter/udgivelser/equal_treatment_2017/hate_speech_in_the_public_online_debate_eng_2017.pdf
Zunes S (2017) Europe’s refugee crisis, terrorism, and Islamophobia. Peace Rev 29:1–6
Acknowledgements
The authors would like to acknowledge the work and contributions of Demos and its interns, and the support of the Metropolitan Police Service, CASM Consulting, and Palantir Technologies throughout the Policing Hate Crime Project, and are thankful to the people and charities who have offered advice on hateful terminology to search for online. We further like to thank the participants of the International Network for Hate Studies Conference in Canada (May 2018) and the Law Commission Hate Crime Research Conference in the UK (March 2019) for their helpful questions, comments and suggestions. Thank you also to the editors and reviewers for their considered comments and suggestions. This work was supported by the Police Knowledge Fund: ‘Policing Hate Crime: Modernising the craft, an evidence-based approach’.
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Wiedlitzka, S., Prati, G., Brown, R. et al. Hate in Word and Deed: The Temporal Association Between Online and Offline Islamophobia. J Quant Criminol 39, 75–96 (2023). https://doi.org/10.1007/s10940-021-09530-9
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DOI: https://doi.org/10.1007/s10940-021-09530-9