Misogyny Detection in Twitter: a Multilingual and Cross-Domain Study

By Endang Wahyu Pamungkas, Valerio Basile, Viviana Patti. 2020

156 citations recorded by the source corpus.

Abstract The freedom of expression given by social media has a dark side: the growing proliferation of abusive contents on these platforms. Misogynistic speech is a kind of abusive language, which can be simplified as hate speech targeting women, and it is becoming a more and more relevant issue in recent years. AMI IberEval 2018 and AMI EVALITA 2018 were two shared tasks which mainly focused on tackling the problem of misogyny in Twitter, in three different languages, namely English, Italian, and Spanish. In this paper, we present an in-depth study on the phenomena of misogyny in those three languages, by focusing on three main objectives. Firstly, we investigate the most important features to detect misogyny and the issues which contribute to the difficulty of misogyny detection, by proposing a novel system and conducting a broad evaluation on this task. Secondly, we study the relationship between misogyny and other abusive language phenomena, by conducting a series of cross-domain classification experiments. Finally, we explore the feasibility of detecting misogyny in a multilingual environment, by carrying out cross-lingual classification experiments. Our system succeeded to outperform all state of the art systems in all benchmark AMI datasets both subtask A and subtask B. Moreover, intriguing insights emerged from error analysis, in particular about the interaction between different but related abusive phenomena. Based on our cross-domain experiment, we conclude that misogyny is quite a specific kind of abusive language, while we experimentally found that it is different from sexism. Lastly, our cross-lingual experiments show promising results. Our proposed joint-learning architecture obtained a robust performance across languages, worth to be explored in further investigation.

Subfields: Artificial Intelligence, Gender Studies, Information Systems, Natural Language Processing, AI Ethics, Fairness & Society

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Citations in the corpus

Learning from the Worst: Dynamically Generated Datasets to Improve Online Hate Detection (2021, background): “Another source of false negatives is when classification systems are applied to out-ofdomain settings, such as system trained on Twitter data being applied to data from Gab (Karan and Šnajder, 2018; Pamungkas et al., 2020; Swamy et al., 2019; Basile et al., 2019; Salminen et al., 2020).”