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        <title>Finding Authentic Counterhate Arguments: A Case Study with Public Figures</title>
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    <abstract>We explore authentic counterhate arguments for online hateful content toward individuals. Previous efforts are limited to counterhate to fight against hateful content toward groups. Thus, we present a corpus of 54,816 hateful tweet-paragraph pairs, where the paragraphs are candidate counterhate arguments. The counterhate arguments are retrieved from 2,500 online articles from multiple sources. We propose a methodology that assures the authenticity of the counter argument and its specificity to the individual of interest. We show that finding arguments in online articles is an efficient alternative to counterhate generation approaches that may hallucinate unsupported arguments. We also present linguistic insights on the language used in counterhate arguments. Experimental results show promising results. It is more challenging, however, to identify counterhate arguments for hateful content toward individuals not included in the training set.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.855</identifier>
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%0 Conference Proceedings
%T Finding Authentic Counterhate Arguments: A Case Study with Public Figures
%A Albanyan, Abdullah
%A Hassan, Ahmed
%A Blanco, Eduardo
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F albanyan-etal-2023-finding
%X We explore authentic counterhate arguments for online hateful content toward individuals. Previous efforts are limited to counterhate to fight against hateful content toward groups. Thus, we present a corpus of 54,816 hateful tweet-paragraph pairs, where the paragraphs are candidate counterhate arguments. The counterhate arguments are retrieved from 2,500 online articles from multiple sources. We propose a methodology that assures the authenticity of the counter argument and its specificity to the individual of interest. We show that finding arguments in online articles is an efficient alternative to counterhate generation approaches that may hallucinate unsupported arguments. We also present linguistic insights on the language used in counterhate arguments. Experimental results show promising results. It is more challenging, however, to identify counterhate arguments for hateful content toward individuals not included in the training set.
%R 10.18653/v1/2023.emnlp-main.855
%U https://aclanthology.org/2023.emnlp-main.855
%U https://doi.org/10.18653/v1/2023.emnlp-main.855
%P 13862-13876

Markdown (Informal)

[Finding Authentic Counterhate Arguments: A Case Study with Public Figures](https://aclanthology.org/2023.emnlp-main.855) (Albanyan et al., EMNLP 2023)

• Finding Authentic Counterhate Arguments: A Case Study with Public Figures (Albanyan et al., EMNLP 2023)

ACL

• Abdullah Albanyan, Ahmed Hassan, and Eduardo Blanco. 2023. Finding Authentic Counterhate Arguments: A Case Study with Public Figures. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 13862–13876, Singapore. Association for Computational Linguistics.