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11 mai 2021
- doi: 10.4000/books.aaccademia.7330
- eisbn: 9791280136329
https://creativecommons.org/licenses/by-nc-nd/4.0/ , info:eu-repo/semantics/openAccess
Mots-clés
EVALITA COVID-19 Infodemic linguistica computazionale Automatic Misogyny Identification Misogyny on Twitter Posts AlBERTo Convolutional Neural Network BERT Model Hate Speech Detection Multimodal Meme Detection MEME management Language Game ``La Ghigliottina'' Language & Linguistics LAN000000 CBXSujets proches
Language (New words, slang, etc.) Field guides Keys (Identification guides) Identification guides Speech, Hate Group libel Group defamation Defamation against groups Racist speech Political thought Government Political theory Civil government Commonwealth, The Science, Political Administration Politics Women-hating Forensic identificationCiter ce document
Martina Miliani et al., « DANKMEMES @ EVALITA 2020: The Memeing of Life: Memes, Multimodality and Politics », Accademia University Press, ID : 10.4000/books.aaccademia.7330
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Résumé
DANKMEMES is a shared task proposed for the 2020 EVALITA campaign, focusing on the automatic classification of Internet memes. Providing a corpus of 2.361 memes on the 2019 Italian Government Crisis, DANKMEMES features three tasks: A) Meme Detection, B) Hate Speech Identification, and C) Event Clustering. Overall, 5 groups took part in the first task, 2 in the second and 1 in the third. The best system was proposed by the UniTor group and achieved a F1 score of 0.8501 for task A, 0.8235 for task B and 0.2657 for task C. In this report, we describe how the task was set up, we report the system results and we discuss them.