Analyzing the Targets of Hate in Online Social Media

By Leandro Araújo Silva, Mainack Mondal, D. Correa, Fabrício Benevenuto, Ingmar Weber. 2016

377 citations recorded by the source corpus.

Social media systems allow Internet users a congenial platform to freely express their thoughts and opinions. Although this property represents incredible and unique communication opportunities, it also brings along important challenges. Online hate speech is an archetypal example of such challenges. Despite its magnitude and scale, there is a significant gap in understanding the nature of hate speech on social media. In this paper, we provide the first of a kind systematic large scale measurement study of the main targets of hate speech in online social media. To do that, we gather traces from two social media systems: Whisper and Twitter. We then develop and validate a methodology to identify hate speech on both these systems. Our results identify online hate speech forms and offer a broader understanding of the phenomenon, providing directions for prevention and detection approaches.

Subfields: Artificial Intelligence, Communication, Information Systems, Natural Language Processing

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Citations in the corpus

Build it Break it Fix it for Dialogue Safety: Robustness from Adversarial Human Attack (2019, background): “Such behavior includes, but is not limited to, trolling in public discussion forums (Herring et al., 2002) and via social media (Silva et al., 2016; Davidson et al., 2017), employing hate speech that expresses prejudice against a particular group, or offensive language specifically targeting an…”
Automated Hate Speech Detection and the Problem of Offensive Language (2017, background): “…for example sentences where a relevant noun and verb occur (e.g. kill and Jews ) (Gitari et al. 2015), the POS trigram “DT jewish NN” (Warner and Hirschberg 2012), and the syntactic structure I < intensity > < user intent > < hate target > , e.g. “I f*cking hate white people” (Silva et al. 2016).”
Racial Bias in Hate Speech and Abusive Language Detection Datasets (2019, methodology): “When labeling datasets and evaluating our models we must pay more attention to the baseline rates of usage of different types of abusive language and how they may vary across populations (Silva et al., 2016).”
Hate Speech Dataset from a White Supremacy Forum (2018, background): “…seems to be a pattern shared by most of the literature consulted (Nockleby, 2000; Djuric et al., 2015; Gitari et al., 2015; Nobata et al., 2016; Silva et al., 2016; Davidson et al., 2017), which would deﬁne hate speech as a) a deliberate attack, b) directed towards a speciﬁc group of people,…”
Challenges and frontiers in abusive content detection (2019, background): “Abuse directed against an identity, such as a social group, demographic or affiliation (e.g. ‘I hate Conservatives’ or ‘Ban Muslims’) (Silva, Mondal, Correa, Benevenuto, & Weber, 2016).”
Measuring the Reliability of Hate Speech Annotations: The Case of the European Refugee Crisis (2016, background): “Hate speech has recently gained some interest as a research topic on the one hand – e.g. (Djuric et al., 2014; Burnap and Williams, 2014; Silva et al., 2016) – but also as a problem to deal with in politics such as the No Hate Speech Movement by the Council of Europe.”
Understanding Abuse: A Typology of Abusive Language Detection Subtasks (2017, background): “Generalized abuse online tends to target people belonging to a small set of categories, primarily racial, religious, and sexual minorities (Silva et al., 2016).”
HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection (2020, neutral): “These observations are in agreement with previous research (Silva et al. 2016).”
Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection (2021, background): “This can be especially important given that hateful content very often targets marginalized groups and racial minorities (Silva et al., 2016; Sap et al., 2020), and can catalyze violence against them (O’Keeffe et al.”
Demoting Racial Bias in Hate Speech Detection (2020, background): “…toll on human moderators has generated much interest in automated systems for detecting hate speech and abusive language (Schmidt and Wiegand, 2017; Fortuna and Nunes, 2018), especially language that targets particular social groups (Silva et al., 2016; Mondal et al., 2017; Mathew et al., 2019).”
Latent Hatred: A Benchmark for Understanding Implicit Hate Speech (2021, background): “…explicitly abusive text since they were originally seeded with hate lexicons (Basile et al., 2019; Founta et al., 2018; Davidson et al., 2017; Waseem and Hovy, 2016), racial identiﬁers (Warner and Hirschberg, 2012), or explicitly hateful phrases such as “I hate <target> ” (Silva et al., 2016).”
Towards a Comprehensive Taxonomy and Large-Scale Annotated Corpus for Online Slur Usage (2020, background): “Silva et al. (2016) is a target-based analysis of the explicit form.”
Generating Counter Narratives against Online Hate Speech: Data and Strategies (2020, background): “…(Waseem and Hovy, 2016; Waseem, 2016; Ross et al., 2017), Facebook (Kumar et al., 2018), WhatsApp (Sprug-noli et al., 2018), and forums (de Gibert et al., 2018), in order to perform hate speech classiﬁcation (Xiang et al., 2012; Silva et al., 2016; Del Vi-gna et al., 2017; Mathew et al., 2018).”
Examining a hate speech corpus for hate speech detection and popularity prediction (2018, background): “Silva et al. (2016) say that “hate speech lies in a complex nexus with freedom of expression, group rights, as well as concepts of dignity, liberty, and equality.”
Online Hate Interpretation Varies by Country, But More by Individual: A Statistical Analysis Using Crowdsourced Ratings (2018, background): “Prior research has shown that online hate has several subcategories like (a) online hate speech, which refers to offensive content that targets a specific group of people [1], [13]; (b) online harassment, where a user deliberately attacks others in an online environment [14], [15]; and (c) cyberbull”
Abusive Language Detection using Syntactic Dependency Graphs (2020, methodology): “The syntactic structure of the text was also used to help identify the target group and the intensity of hate speech (Warner and Hirschberg, 2012; Silva et al., 2016).”
Detecting the Hate Code on Social Media (2017, background): “S I ] 16 M a r 2017 on building classiﬁers that can identify content that is hate-ful and antagonistic in nature (Burnap and Williams 2015; Silva et al. 2016).”
Determining Code Words in Euphemistic Hate Speech Using Word Embedding Networks (2018, methodology): “These have ranged from classiﬁcation of hateful tweets using bag-of-words models and typed dependencies (Burnap and Williams, 2015), to using semantic structure of sentences to the study of tar-Code gets of hate (Silva et al., 2016).”