A Feature-Based Approach to Assess Hate Speech in User Comments

Datos Bibliográficos

ID21761859
AutoresLiane Reiners (Johannes Gutenberg University Mainz), Christian Schemer (0000-0002-7808-2240, Johannes Gutenberg University Mainz)
Año2020
Volumen38
Páginas529-548
Fecha de publicación2020-01-01
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaQuestions de communication (JOURNAL)
Identificadores de la revistaISSN: 1633-5961 • E-ISSN: 2259-8901
EditorialOpenEdition (PUBLISHER)
DOI10.4000/questionsdecommunication.24808
OpenAlexW3186885973
IdiomaEN
Citas recibidas2
Referencias citadas35

Hate speech in user comments is a serious challenge for media actors, platforms, legal authorities, and the general public alike. This form of communication can impair the quality of online discussions, but also poison public discourse and civic life. This explains the plethora of studies from different disciplines addressing this topic. However, the diversity of disciplines and approaches complicate a common understanding of hate speech. Frequently, definitions are too broad, as they include factors such as the intention of the commenter or the consequences for stigmatized groups. Additionally, binary categorizations (hate/no hate) applied in some disciplines fail to consider that hate speech varies gradually and ignore qualitative differences of hate speech dimensions. The present research suggests a feature-based approach to analyze hate speech in user comments focusing on manifest features (group-related labels, swear words, trait and action attributions as well as treatment recommendations), and demonstrates its use in a pilot study

Attribution · Authorship attribution · Linguistics · Political science · Trait · Bullying, Victimization, and Aggression · Computer Science · Hate Speech and Cyberbullying Detection · Law · Psychology · Social Psychology · Swearing, Euphemism, Multilingualism · Artificial Intelligence

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