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11 mai 2021
- doi: 10.4000/books.aaccademia.7084
- 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.) Women-hatingCiter ce document
Alessandra Teresa Cignarella et al., « SardiStance @ EVALITA2020: Overview of the Task on Stance Detection in Italian Tweets », Accademia University Press, ID : 10.4000/books.aaccademia.7084
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Résumé
SardiStance is the first shared task for Italian on the automatic classification of stance in tweets. It is articulated in two different settings: A) Textual Stance Detection, exploiting only the information provided by the tweet, and B) Contextual Stance Detection, with the addition of information on the tweet itself such as the number of retweets, the number of favours or the date of posting; contextual information about the author, such as follower count, location, user’s biography; and additional knowledge extracted from the user’s network of friends, followers, retweets, quotes and replies. The task has been one of the most participated at EVALITA 2020 (Basile et al. 2020), with a total of 22 submitted runs for Task A, and 13 for Task B, and 12 different participating teams from both academia and industry.