The Effect of Extremist Violence on Hateful Speech Online

By Alexandra Olteanu, Carlos Castillo, Jeremy Boy, Kush R. Varshney. 2018

162 citations recorded by the source corpus.

User-generated content online is shaped by many factors, including endogenous elements such as platform affordances and norms, as well as exogenous elements, in particular significant events. These impact what users say, how they say it, and when they say it. In this paper, we focus on quantifying the impact of violent events on various types of hate speech, from offensive and derogatory to intimidation and explicit calls for violence. We anchor this study in a series of attacks involving Arabs and Muslims as perpetrators or victims, occurring in Western countries, that have been covered extensively by news media. These attacks have fueled intense policy debates around immigration in various fora, including online media, which have been marred by racist prejudice and hateful speech. The focus of our research is to model the effect of the attacks on the volume and type of hateful speech on two social media platforms, Twitter and Reddit. Among other findings, we observe that extremist violence tends to lead to an increase in online hate speech, particularly on messages directly advocating violence. Our research has implications for the way in which hate speech online is monitored and suggests ways in which it could be fought.

Subfields: Artificial Intelligence, Communication, Sociology and Political Science, Natural Language Processing, AI Ethics, Fairness & Society

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Citations in the corpus

ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection (2022, background): “To better understand how harm may be perpetrated against the minority group, we ask readers in-depth questions about text’s content, following Sap et al. (2020) and Olteanu et al. (2018).”
HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection (2020, background): “Real world violent events could also lead to increased hate speech in online space (Olteanu et al. 2018).”
Resources and benchmark corpora for hate speech detection: a systematic review (2020, methodology): “Olteanu et al. (2018) (OCBV)—150 ? millions items from Twitter and Reddit, plus a list of 1,890 unique terms contained in the data.”
Time of Your Hate: The Challenge of Time in Hate Speech Detection on Social Media (2020, methodology): “Moreover, having reliable methods to compute an index of online hate speech in relation to specific geo-times coordinates automatically opens the way to the possibility to investigate the interplay between the volume of hate speech messages and the socio-economic and demographic traditional indexes ”
Towards multidomain and multilingual abusive language detection: a survey (2021, unknown): “Twitter and Reddit 150 millions No [86]”
When Are Search Completion Suggestions Problematic? (2020, background): “For example, hateful content is rather about groups or individuals than about say plants [23, 56, 85].”
A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media (2019, background): “Online Hate Speech and Oﬀensive Language: Researchers have been studying hate speech on social media platforms such as Twitter [3], Reddit [12, 14], and YouTube [15] in the past few years.”
Contextualizing Hate Speech Classifiers with Post-hoc Explanation (2020, methodology): “More recent approaches applied to a wide typology of hate speech (Waseem et al., 2017), build supervised models trained on annotated (e.g., Waseem and Hovy, 2016; de Gibert et al., 2018) or heuristically-labeled (Wulczyn et al., 2017; Olteanu et al., 2018) data.”