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Cited by 27Hate Speech and Cyberbullying DetectionMedia Discourse and Social AnalysisTerrorism, Counterterrorism, and Political Violence

Hate in Word and Deed: The Temporal Association Between Online and Offline Islamophobia

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Wiedlitzka, Susann, Prati, Gabriele, Brown, Rupert, Smith, Josh, Walters, Mark A.·2023·Journal of Quantitative Criminology
DOI 10.1007/s10940-021-09530-9
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  1. Allport, G.W. (1954). The nature of prejudice, Addison-Wesley. Reading.
  2. Alorainy, W., Burnap, P., Liu, H., and Williams, M.L. (2019). "The enemy among us": Detecting cyber hate speech with threats-based othering language embeddings. ACM Transactions on the Web 13(3, Article 14): 1--26.
  3. Amisano, G., and Giannini, C. (1997). Topics in structural VAR econometrics, Springer- Verlag, Heidelberg.
  4. Anderson, C.A., and Bushman, B.J. (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. Psychological Science 12: 353--359.
  5. Awan, I. (2016a). Cyber-Islamophobia and internet hate crime. In Awan, I. (ed.), Islamophobia in cyberspace -Hate crimes go viral, 1st ed., Ashgate Publishing, Oxon, New York, pp. 7--22.
  6. Awan, I. (2016b). Islamophobia, hate crime and the internet. In Awan, I. (ed.), Islamophobia in cyberspace -Hate crimes go viral, 1st ed, Ashgate Publishing, Oxon, New York, pp. 167--187.
  7. Awan, I. (ed.), Islamophobia in cyberspace -Hate crimes go viral, 1st ed., Ashgate Publishing, Oxon, New York, pp. 23--39.
  8. Awan, I., and Zempi, I. (2017). I will blow your face off' -Virtual and physical world anti- Muslim hate crime. The British Journal of Criminology 57: 362--380.
  9. Bakalis, C. (2018). Rethinking cyberhate laws. Information & Communications Technology Law 27(1): 86--110.
  10. Binns, A. (2013). Facebook's ugly sisters: Anonymity and abuse on Formspring and Ask.fm. Media Education Research Journal.
  11. Binns, A. (2014). Twitter City and Facebook Village: Teenage girls' personas and experiences influenced by choice architecture in social networking sites. Journal of Media Practice 15: 71--91.
  12. Blakemore, B. (2016). Online hate and political activist groups. In Awan, I. (ed.), Islamophobia in cyberspace -Hate crimes go viral, 1st ed., Ashgate Publishing, Oxon, New York, pp. 63--83.
  13. Bowling, B. (1993). Racial harassment and the process of victimization: Conceptual and methodological implications for the local crime survey. The British Journal of Criminology 33: 231--250.
  14. Bowling, B. (1998). Violent racism: Victimisation, policing, and social context, Ocford University Press, New York.
  15. Carson, J.V., Dugan, L., and 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. Journal of Quantitative Criminoligy 36: 701--724.
  16. Chan, J., Ghose, A., and Seamans, R. (2016). The internet and racial hate crime: Offline spillovers from online access. MIS Quarterly 40: 381--404.
  17. Cialdini, R.B., and Goldstein, N.J. (2004). Social influence: Compliance and conformity. Annual Review of Psychology 55: 591--621.
  18. Berkowitz, L. (ed.), Advances in experimental social psychology, Academic Press, San Diego, 201--234.
  19. Cialdini, R.B., Reno, R.R., and Kallgren, C.A. (1990). A focus theory of normative conduct: Recycling the concept of norms to reduce littering in public places. Journal of Personality and Social Psychology 58: 1015--1026.
  20. Ciftci, S. (2012). Islamophobia and threat perceptions: Explaining anti-Muslim sentiment in the West. Journal of Muslim Minority Affairs 32: 293--309.
  21. Cuerden, G., and Rogers, C. (2017). Exploring race hate crime reporting in Wales following Brexit. Review of European Studies 9: 158--164.
  22. Davidson, T., Warmsley, D., Macy, M., and Weber, I. (2017). Automated hate speech detection and the problem of offensive language. Proceedings of the Eleventh International AAAI Conference on Web and Social Media: 1--4.
  23. Eckert, S., O'Shay Wallace, S., Metzger-Riftkin, J., and Kolhoff, S. (2018). "The best damn representation of Islam:" Muslims, gender, social media and Islamophobia in the United States. CyberOrient 12(1): 4--30.
  24. Eckert, S., Metzger-Riftkin, J., Kolhoff, S., and O'Shay-Wallace, S. (2021). A hyper differential counterpublic: Muslim social media users and Islamophobia during the 2016 US presidential election. New Media & Society 23(1): 78--98.
  25. Green, D.P., McFalls, L.H., and Smith, J.K. (2001). Hate crime: An emergent research agenda. Annual Review of Sociology 27: 479--504.
  26. Hamilton, J.D. (1994). Time series analysis, Princeton University Press, Princeton.
  27. Hanes, E., and Machin, S. (2014). Hate crime in the wake of terror attacks. Journal of Contemporary Criminal Justice 30: 247--267.
  28. Hewstone, M. (1990), The 'ultimate attribution error'? A review of the literature on intergroup causal attribution. European Journal of Social Psychology 20(4): 311--335.
  29. Jaki, S., and De Smedt, T. (2019). Right-wing German hate speech on Twitter: Analysis and automatic detection. arXiv preprint. arXiv: 1910.07518. · arXiv
  30. Kaplan, J. (2006). Islamophobia in America?: September 11 and Islamophobic hate crime. Terrorism and Political Violence 18(1): 1--33.
  31. Kwon, K.H., Chadha, M., and Wang, F. (2019). Proximity and networked news public: Structural topic modeling of global Twitter conversations about the 2017 Quebec Mosque Shooting. International Journal of Communication 13: 2652--2675.
  32. Lütkepohl, H. (2005). New introduction to multiple time series analysis, Springer-Verlag, Berlin.
  33. Maynard, J.L., and Benesch, S. (2016). Dangerous speech and dangerous ideology: An integrated model for monitoring and prevention. Genocide Studies and Prevention: An International Journal 9: 70--95.
  34. McDevitt, J., Levin, J., and Bennett, S. (2002). Hate crime offenders: An expanded typology. Journal of Social Issues 58: 303--317.
  35. Müller, K., and Schwarz, C. (2020). Fanning the flames of hate: Social media and hate crime. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3082972
  36. Awan, I. (ed.), Islamophobia in cyberspace -Hate crimes go viral, Ashgate Publishing, Oxon, New York, pp. 41--61.
  37. Oriola, O., and Kotzé, E. (2020). Evaluating machine learning techniques for detecting offensive and hate speech in South African tweets. IEEE Access 8: 21496--21509.
  38. Perry, B., and Alvi, S. (2012). 'We are all vulnerable': The in terrorem effects of hate crimes. International Review of Victimology 18: 57--71.
  39. Pettigrew, T. F. (1979). The ultimate attribution error: Extending Allport's cognitive analysis of prejudice. Personality and Social Psychology Bulletin 5(4): 461--476.
  40. Piatkowska, S.J., and 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. The British Journal of Criminology 61(3): 648--669.
  41. 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.
  42. Settles, B. (2011). Closing the loop: Fast, interactive semi-supervised annotation with queries on features and instances. EMNLP 2011 -Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, Edinburgh, Scotland, 1467--1478.
  43. Sloan, L., Morgan, J., Housley, W., Williams, M., Edwards, A., Burnap, P., and Rana, O. (2013). Knowing the tweeters: Deriving sociologically relevant demographics from Twitter. Sociological Research Online, 18(3): 74--84.
  44. Sutherland, E.H., and Cressey, D.R. (1974). Criminology,J. B. Lippincott, New York.
  45. Vidgen, B., and Yasseri, T. (2020). Detecting weak and strong Islamophobic hate speech on social media. Journal of Information Technology & Politics 17(1): 66--78.
  46. Vidgen, B., Yasseri, T., and Margetts, H. (2019). Trajectories of Islamophobic hate amongst far right actors on Twitter. arXiv preprint. arXiv: 1910.05794. · arXiv
  47. Wibberley, S., Weir, D., and Reffin, J. (2014). Method51 for mining insight from social media datasets. COLING 2014, the 25th International Conference on Computational Linguistics: System Demonstrations. Dublin, Ireland, 115--119.
  48. Wibberley, S., Reffin, J., and Weir, D. (2013). Language technology for agile social media science. 7th Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities. Sofia, Bulgaria: Association for Computational Linguistics, 36--42.
  49. Williams, M.L., and Burnap, P. (2016). Cyberhate on social media in the aftermath of Woolwich: A case study in computational criminology and big data. The British Journal of Criminology 56: 211--238.
  50. Williams, M.L., Burnap, P., Javed, A., et al. (2020). 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 60(1): 93--117.
  51. Wilson, R.A., and Land, M.K. (2021). Hate speech on social media: Content moderation in context. Connecticut Law Review 52(3): 1029--1076.
  52. Zunes, S. (2017). Europe's refugee crisis, terrorism, and Islamophobia. Peace Review 29: 1-- 6.

Cited by

Sorted by citations

Authors
Wiedlitzka, S., Prati, G., Brown, R., Smith, J., & Walters, M. A.
Affiliations
  • University of Auckland (NZ)
  • University of Bologna (IT)
  • University of Sussex (GB)
  • Demos (GB)
Corresponding author
Wiedlitzka, Susann
ORCID
Venue
Journal of Quantitative Criminology
Publisher
Springer Science and Business Media LLC
Published
2021-08-28
Volume
39
Issue
1
Pages
75-96
ISSN
  • 0748-4518 (ISSN-L)
  • 1573-7799
DOI
10.1007/s10940-021-09530-9
OpenAlex
W3198379394
Cited by
27
References
0
Citations by year
2026: 6 · 2025: 5 · 2024: 9 · 2023: 5 · 2022: 1 · 2021: 1
FWCI
1.41
Type
journal article
Language
en
Open access
Licence
Locations
  • Journal of Quantitative Criminology (publishedVersion)
  • Archivio istituzionale della ricerca (Alma Mater Studiorum Università di Bologna) (submittedVersion, other-oa)
  • Sussex Research Online (University of Sussex) (submittedVersion)
  • Figshare (submittedVersion, cc-by)
  • ResearchSpace (University of Auckland) (submittedVersion)
Funders
Police Knowledge Fund
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
  • Social Sciences › Sociology and Political Science › Media Discourse and Social Analysis
  • Social Sciences › Sociology and Political Science › Terrorism, Counterterrorism, and Political Violence
Keywords
Islamophobia, online hate speech, temporal association, Twitter, hate incidents, intergroup hostility, United Kingdom

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