A Multi-Task Learning Approach to Hate Speech Detection Leveraging Sentiment Analysis
Flor Miriam Plaza-del-Arco, M. Dolores Molina-González, Luís Alfonso Ureña López, María Teresa Martín Valdivia
Abstract
The rise of social media platforms has significantly changed the way our world communicates, and part of those changes includes a rise in inappropriate behaviors, such as the use of aggressive and hateful language online. Detecting such content is crucial to filtering or blocking inappropriate content on the Web. However, due to the huge amount of data posted every day, automatic methods are essential for identifying this type of content. Seeking to address this issue, the Natural Language Processing community is increasingly involved in testing a wide range of techniques for hate speech detection. While achieving promising results, these techniques consider hate speech detection as the sole optimization objective, without involving other related tasks such as polarity and emotion classification that are strongly linked to offensive behavior. In this paper, we propose the first Multi-task approach that leverages the shared affective knowledge to detect hate speech in Spanish tweets, using a well-known Transformer-based model. Our results show that the combination of both polarity and emotional knowledge helps to detect hate speech more accurately across datasets.