Warning: This paper contains examples of language and images that may be offensive. This paper presents a probabilistic approach to identifying the disagreement-related elements in misogynistic memes by considering both modalities that compose a meme (i.e., visual and textual sources). Several methodologies to exploit such elements in the identification of disagreement among annotators have been investigated and evaluated on the Multimedia Automatic Misogyny Identification (MAMI) [1] dataset. The proposed unsupervised approach reaches comparable performances, and in some cases even better, with state-of-the-art approaches, but with a reduced number of parameters to be estimated. The source code of our approaches is publicly available.
Rizzi, G., Rosso, P., Fersini, E. (2024). From Explanation to Detection: Multimodal Insights into Disagreement in Misogynous Memes. In Proceedings of the Tenth Italian Conference on Computational Linguistics (CLiC-it 2024), Dec 04 — 06, 2024, Pisa, Italy (pp.1-8). CEUR-WS.
From Explanation to Detection: Multimodal Insights into Disagreement in Misogynous Memes
Rizzi G.Primo
;Fersini E.Ultimo
2024
Abstract
Warning: This paper contains examples of language and images that may be offensive. This paper presents a probabilistic approach to identifying the disagreement-related elements in misogynistic memes by considering both modalities that compose a meme (i.e., visual and textual sources). Several methodologies to exploit such elements in the identification of disagreement among annotators have been investigated and evaluated on the Multimedia Automatic Misogyny Identification (MAMI) [1] dataset. The proposed unsupervised approach reaches comparable performances, and in some cases even better, with state-of-the-art approaches, but with a reduced number of parameters to be estimated. The source code of our approaches is publicly available.
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