A Measurement Study of Hate Speech in Social Media

By Mainack Mondal, Leandro Araújo Silva, Fabrício Benevenuto. ACM Conference on Hypertext & Social Media, 2017

317 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Communication

View Paper: https://www.semanticscholar.org/paper/34dcfc790e63309a95c13f6c57134212d131a7d9

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Citations in the corpus

Online Hate Interpretation Varies by Country, But More by Individual: A Statistical Analysis Using Crowdsourced Ratings (2018, background): “[6] identified several explicit hate targets, e.”
Spread of Hate Speech in Online Social Media (2018, background): “Research on hate speech: The majority of the research in hate speech has been done in automatic detection in various social media platforms like Twitter [4, 10, 31, 40], Facebook [12], Yahoo! Finance and News [13, 27, 39] and Whisper [25].”
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).”
Anatomy of Online Hate: Developing a Taxonomy and Machine Learning Models for Identifying and Classifying Hate in Online News Media (2018, methodology): “The study by Mondal et al. (2017) illustrates the limitations of using keywords only.”
Hate Speech in Pixels: Detection of Offensive Memes towards Automatic Moderation (2019, background): “It appears in media 42 such as newspapers or TV news, but one of the main focus of hate speech with very diverse targets has 43 appeared in social networks [12, 8, 10].”
Few-shot Instruction Prompts for Pretrained Language Models to Detect Social Biases (2021, background): “Despite wide use (Sood et al., 2012; Mondal et al., 2017; Simonite, 2021) they can be at a significant mismatch with human ratings (Davidson et al., 2017b) and can’t easily capture phenomenons such as sarcasm, humor (Rajadesingan et al., 2015) or polysemy (Sahlgren et al., 2018).”
Hate speech detection: Challenges and solutions (2019, background): “Due to the societal concern and how widespread hate speech is becoming on the Internet [7], there is strong motivation to study automatic detection of hate speech.”
Developing an online hate classifier for multiple social media platforms (2020, methodology): “[32] used a simple sentence structure “I<intensity><userint ent><hatetarget>” to identify hate targets.”