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.
Fersini, E., Nozza, D., Boifava, G. (2020). Profiling Italian Misogynist: An Empirical Study. In Proceedings of the Workshop on Resources and Techniques for User and Author Profiling in Abusive Language, ResTUP@LREC 2020, Marseille, France, May 2020 (pp.9-13). European Language Resources Association (ELRA).
Profiling Italian Misogynist: An Empirical Study
Fersini, E
Primo
;Nozza, DSecondo
;
2020
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.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
