Vectors for Counterspeech on Twitter

By Lucas Wright, Derek Ruths, K. Dillon, Haji Mohammad Saleem, Susan Benesch. 2017

54 citations recorded by the source corpus.

A study of conversations on Twitter found that some arguments between strangers led to favorable change in discourse and even in attitudes. The authors propose that such exchanges can be usefully distinguished according to whether individuals or groups take part on each side, since the opportunity for a constructive exchange of views seems to vary accordingly.

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

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Citations in the corpus

A Survey of Race, Racism, and Anti-Racism in NLP (2021, background): “Bias Survey/Position Shahid et al. (2020) 2020 Workshop Social Science/Media Analyze Corpus Tan et al. (2020) 2020 ACL Ethics/Task-indep.”
Anatomy of Online Hate: Developing a Taxonomy and Machine Learning Models for Identifying and Classifying Hate in Online News Media (2018, methodology): “While different methods have been applied to reduce hateful commenting, including counterspeech (Wright et al. 2017), non-anonymity, and mandatory registration (Hughey and Daniels 2013), these efforts have not been fully successful, and so online hate remains a highly topical research problem with…”
Developing an online hate classifier for multiple social media platforms (2020, background): “Some of these studies deal with “counterspeech”, i.e., ways of defusing the hateful comments with language-based strategies [26–28].”
Thou shalt not hate: Countering Online Hate Speech (2018, background): “(Wright et al. 2017) noted that “Compu-tational approaches are required in order to study and engage counterspeech efforts at scale and there is no work which perform automatic detection of counterspeech”.”
CONAN - COunter NArratives through Nichesourcing: a Multilingual Dataset of Responses to Fight Online Hate Speech (2019, background): “The study of Wright et al. (2017) shows that some arguments among strangers induce favorable changes in discourse and attitudes.”
Examining a hate speech corpus for hate speech detection and popularity prediction (2018, background): “It is even more likely that the user’s response is an instance of counterspeech — interaction used to challenge hate speech (Wright et al., 2017).”
A Just and Comprehensive Strategy for Using NLP to Address Online Abuse (2019, background): “Wright et al. (2017) notes that real-world examples of bystanders intervening can be found on-line, thereby providing a potential source of training data but methods are needed to reliably identify such counter speech examples.”