This paper presents a probabilistic semantic approach to identifying disagreement-related textual constituents in hateful content. Several methodologies to exploit the selected constituents to determine if a message could lead to disagreement have been defined. The proposed approach is evaluated on 4 datasets made available for the SemEval 2023 Task 11 shared task, highlighting that a few constituents can be used as a proxy to identify if a sentence could be perceived differently by multiple readers. The source code of our approaches is publicly available (https://github.com/MIND-Lab/Unrevealing-Disagreement-Constituents-in-Hateful-Speech).
Rizzi, G., Astorino, A., Rosso, P., Fersini, E. (2024). Unraveling Disagreement Constituents in Hateful Speech. In Advances in Information Retrieval 46th European Conference on Information Retrieval, ECIR 2024, Glasgow, UK, March 24–28, 2024, Proceedings, Part IV (pp.21-29). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-56066-8_3].
Unraveling Disagreement Constituents in Hateful Speech
Rizzi G.Primo
;Astorino A.Secondo
;Fersini E.Ultimo
2024
Abstract
This paper presents a probabilistic semantic approach to identifying disagreement-related textual constituents in hateful content. Several methodologies to exploit the selected constituents to determine if a message could lead to disagreement have been defined. The proposed approach is evaluated on 4 datasets made available for the SemEval 2023 Task 11 shared task, highlighting that a few constituents can be used as a proxy to identify if a sentence could be perceived differently by multiple readers. The source code of our approaches is publicly available (https://github.com/MIND-Lab/Unrevealing-Disagreement-Constituents-in-Hateful-Speech).
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