Not All Counterhate Tweets Elicit the Same Replies: A Fine-Grained Analysis

Abdullah Albanyan, Ahmed Hassan, Eduardo Blanco

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Counterhate arguments can effectively fight and limit the spread of hate speech. However, they can also exacerbate the hate, as some people may respond with aggression if they feel threatened or targeted by the counterhate. In this paper, we investigate replies to counterhate arguments beyond whether the reply agrees or disagrees with the counterhate argument. We present a corpus with 2,621 replies to counterhate arguments countering hateful tweets, and annotate them with fine-grained characteristics. We show that (a) half of the replies (51%) to the counterhate arguments disagree with the argument, and (b) this kind of reply often supports the hateful tweet (40%). We also analyze the language of counterhate arguments that elicit certain types of replies. Experimental results show that it is feasible to anticipate the kind of replies a counterhate argument will elicit.

Original languageEnglish
Title of host publicationStarSEM 2023 - 12th Joint Conference on Lexical and Computational Semantics, Proceedings of the Conference
EditorsAlexis Palmer, Jose Camacho-Collados
PublisherAssociation for Computational Linguistics (ACL)
Pages71-88
Number of pages18
ISBN (Electronic)9781959429760
StatePublished - 2023
Event12th Joint Conference on Lexical and Computational Semantics, StarSEM 2023, co-located with ACL 2023 - Toronto, Canada
Duration: 13 Jul 202314 Jul 2023

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

Conference12th Joint Conference on Lexical and Computational Semantics, StarSEM 2023, co-located with ACL 2023
Country/TerritoryCanada
CityToronto
Period13/07/2314/07/23

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