Spread of Hate Speech in Online Social Media

By Binny Mathew, Ritam Dutt, Pawan Goyal, Animesh Mukherjee. Web Science Conference, 2018

364 citations recorded by the source corpus.

Hate speech is considered to be one of the major issues currently plaguing the online social media. With online hate speech culminating in gruesome scenarios like the Rohingya genocide in Myanmar, anti-Muslim mob violence in Sri Lanka, and the Pittsburgh synagogue shooting, there is a dire need to understand the dynamics of user interaction that facilitate the spread of such hateful content. In this paper, we perform the first study that looks into the diffusion dynamics of the posts made by hateful and non-hateful users on Gab (Gab.com). We collect a massive dataset of 341K users with 21M posts and investigate the diffusion of the posts generated by hateful and non-hateful users. We observe that the content generated by the hateful users tend to spread faster, farther and reach a much wider audience as compared to the content generated by normal users. We further analyze the hateful and non-hateful users on the basis of their account and network characteristics. An important finding is that the hateful users are far more densely connected among themselves. Overall, our study provides the first cross-sectional view of how hateful users diffuse hate content in online social media.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/193c621626ddc5bff23541052bdf2f1d67041206

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Citations in the corpus

Handling Bias in Toxic Speech Detection: A Survey (2022, background): “At extreme online, hate speech (a type of toxic speech) [50] can lead to incidents of offline violence causing loss of life, and property [25, 51].”
Leveraging Bias in Pre-Trained Word Embeddings for Unsupervised Microaggression Detection (2021, neutral): “For instance, Mathew et al. (2019b) analyses the temporal effects of allowing hate speech on Gab, and finds that the language of users tends to become more and more similar to that of hateful users over time.”
DeepHate: Hate Speech Detection via Multi-Faceted Text Representations (2020, background): “The spread of hate speech in social media has not only sowed discord among individuals or communities online but also resulted in violent hate crimes [27, 34, 44].”
Directions in Abusive Language Training Data: Garbage In, Garbage Out (2020, background): “However, more niche platforms, such as Gab or 4chan, tolerate more offensive forms of speech and are more likely to contain explicit abuse, such as racism and very intrusive forms of harassment, such as ‘doxxing’ [55, 73, 120].”
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).”
Directions in abusive language training data, a systematic review: Garbage in, garbage out (2020, background): “had lost more than 60% of its content [6, 72].”
HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection (2020, methodology): “In case of Gab, we use the dataset provided by Mathew et al. (2019a).”