Profiling Italian Misogynist: An Empirical Study

Elisabetta Fersini, Debora Nozza, Giulia Boifava


Abstract
Hate speech may take different forms in online social environments. In this paper, we address the problem of automatic detection of misogynous language on Italian tweets by focusing both on raw text and stylometric profiles. The proposed exploratory investigation about the adoption of stylometry for enhancing the recognition capabilities of machine learning models has demonstrated that profiling users can lead to good discrimination of misogynous and not misogynous contents.
Anthology ID:
2020.restup-1.3
Volume:
Proceedings of the Workshop on Resources and Techniques for User and Author Profiling in Abusive Language
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Johanna Monti, Valerio Basile, Maria Pia Di Buono, Raffaele Manna, Antonio Pascucci, Sara Tonelli
Venue:
ResTUP
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
9–13
Language:
English
URL:
https://aclanthology.org/2020.restup-1.3
DOI:
Bibkey:
Cite (ACL):
Elisabetta Fersini, Debora Nozza, and Giulia Boifava. 2020. Profiling Italian Misogynist: An Empirical Study. In Proceedings of the Workshop on Resources and Techniques for User and Author Profiling in Abusive Language, pages 9–13, Marseille, France. European Language Resources Association (ELRA).
Cite (Informal):
Profiling Italian Misogynist: An Empirical Study (Fersini et al., ResTUP 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nschneid-patch-1/2020.restup-1.3.pdf