Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media

By Mai Elsherief, Vivek Kulkarni, Dana Nguyen, William Yang Wang, Elizabeth M. Belding-Royer. International Conference on Web and Social Media, 2018

346 citations recorded by the source corpus.

While social media empowers freedom of expression and individual voices, it also enables anti-social behavior, online harassment, cyberbullying, and hate speech. In this paper, we deepen our understanding of online hate speech by focusing on a largely neglected but crucial aspect of hate speech -- its target: either directed towards a specific person or entity, or generalized towards a group of people sharing a common protected characteristic. We perform the first linguistic and psycholinguistic analysis of these two forms of hate speech and reveal the presence of interesting markers that distinguish these types of hate speech. Our analysis reveals that Directed hate speech, in addition to being more personal and directed, is more informal, angrier, and often explicitly attacks the target (via name calling) with fewer analytic words and more words suggesting authority and influence. Generalized hate speech, on the other hand, is dominated by religious hate, is characterized by the use of lethal words such as murder, exterminate, and kill; and quantity words such as million and many. Altogether, our work provides a data-driven analysis of the nuances of online-hate speech that enables not only a deepened understanding of hate speech and its social implications, but also its detection.

Subfields: Artificial Intelligence, Communication, Political Science and International Relations

View Paper: https://www.semanticscholar.org/paper/7554e7c56813731acfefdaf898ccb03e0d667007

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Citations in the corpus

Multilingual and Multi-Aspect Hate Speech Analysis (2019, background): “…(2016) use racist, sexist, and normal as labels; Davidson et al. (2017) label their data as hateful, offensive (but not hateful), and neither, while ElSherief et al. (2018) present an English dataset that records the target category based on which hate speech discriminates against people, such as…”
BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection (2020, neutral): “With anonymity safeguards, individuals easily express hatred against others based on their superﬁcial characteristics such as gender, sexual orientation, and age (ElSherief et al., 2018).”
Developing an online hate classifier for multiple social media platforms (2020, background): “…we could locate from our literature review include (implementations in footnotes) Waseem and Hovy [65], 3 Davidson et al. [66], 4 ElSherief et al. [67], 5 Saha et al. [68], 6 Qian et al. [69], 7 Ross et al. [70], 8 de Gibert et al. [71], 9 Badjatiya et al. [29], 10 and Chandrasekharan et al. [64].”
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].”
Interpretable Multi-Modal Hate Speech Detection (2021, methodology): “This is backed by the procedure followed by Golbeck et al.(Golbeck et al., 2017; ?”
A Framework for the Computational Linguistic Analysis of Dehumanization (2020, background): “In addition, our focus on dehumanization is closely related to the detection and analysis of hate speech and abusive language (Schmidt and Wiegand, 2017; ElSherief et al., 2018).”
Towards a Comprehensive Taxonomy and Large-Scale Annotated Corpus for Online Slur Usage (2020, background): “Although the drawbacks of keyword-based approaches are known to researchers, there are currently no clear alternatives to this technique (Waseem and Hovy, 2016; Davidson et al., 2017; ElSherief et al., 2018).”
Abusive Language Detection using Syntactic Dependency Graphs (2020, background): “Abusive speech is deﬁned as an attack targeted towards a particular individual or entity belonging to a protected group (protected group may include, but are not always limited to, religious, gender or racial minorities) (ElSherief et al., 2018).”