Misogyny and Aggressiveness Tend to Come Together and Together We Address Them

Arianna Muti, Francesco Fernicola, Alberto Barrón-Cedeño

Abstract
We target the complementary binary tasks of identifying whether a tweet is misogynous and, if that is the case, whether it is also aggressive. We compare two ways to address these problems: one multi-class model that discriminates between all the classes at once: not misogynous, non aggressive-misogynous and aggressive-misogynous; as well as a cascaded approach where the binary classification is carried out separately (misogynous vs non-misogynous and aggressive vs non-aggressive) and then joined together. For the latter, two training and three testing scenarios are considered. Our models are built on top of AlBERTo and are evaluated on the framework of Evalita’s 2020 shared task on automatic misogyny and aggressiveness identification in Italian tweets. Our cascaded models —including the strong naïve baseline— outperform significantly the top submissions to Evalita, reaching state-of-the-art performance without relying on any external information.
Anthology ID:
2022.lrec-1.440
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
4142–4148
Language:
URL:
https://aclanthology.org/2022.lrec-1.440
DOI:
Bibkey:
Cite (ACL):
Arianna Muti, Francesco Fernicola, and Alberto Barrón-Cedeño. 2022. Misogyny and Aggressiveness Tend to Come Together and Together We Address Them. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 4142–4148, Marseille, France. European Language Resources Association.
Cite (Informal):
Misogyny and Aggressiveness Tend to Come Together and Together We Address Them (Muti et al., LREC 2022)
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PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2022.lrec-1.440.pdf
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