@inproceedings{rizzi-etal-2024-explanation,
title = "From Explanation to Detection: Multimodal Insights into Disagreement in Misogynous Memes",
author = "Rizzi, Giulia and
Rosso, Paolo and
Fersini, Elisabetta",
editor = "Dell'Orletta, Felice and
Lenci, Alessandro and
Montemagni, Simonetta and
Sprugnoli, Rachele",
booktitle = "Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)",
month = dec,
year = "2024",
address = "Pisa, Italy",
publisher = "CEUR Workshop Proceedings",
url = "https://aclanthology.org/2024.clicit-1.89/",
pages = "821--828",
ISBN = "979-12-210-7060-6",
abstract = "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) 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."
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<abstract>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) 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.</abstract>
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%0 Conference Proceedings
%T From Explanation to Detection: Multimodal Insights into Disagreement in Misogynous Memes
%A Rizzi, Giulia
%A Rosso, Paolo
%A Fersini, Elisabetta
%Y Dell’Orletta, Felice
%Y Lenci, Alessandro
%Y Montemagni, Simonetta
%Y Sprugnoli, Rachele
%S Proceedings of the 10th Italian Conference on Computational Linguistics (CLiC-it 2024)
%D 2024
%8 December
%I CEUR Workshop Proceedings
%C Pisa, Italy
%@ 979-12-210-7060-6
%F rizzi-etal-2024-explanation
%X 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) 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.
%U https://aclanthology.org/2024.clicit-1.89/
%P 821-828
Markdown (Informal)
[From Explanation to Detection: Multimodal Insights into Disagreement in Misogynous Memes](https://aclanthology.org/2024.clicit-1.89/) (Rizzi et al., CLiC-it 2024)
ACL