Improving automatic cyberbullying detection in social network environments by fine-tuning a pre-trained sentence transformer language model

Published: 01 Jan 2024, Last Modified: 01 Mar 2025Soc. Netw. Anal. Min. 2024EveryoneRevisionsBibTeXCC BY-SA 4.0
Abstract: The internet use among children and adolescents has increased massively recently. This situation has promoted harmful situations such as cyberbullying, which is becoming a worldwide problem that entails serious consequences for well-being. The detection of these attitudes is essential to prevent and act accordingly. Groundbreaking techniques based on deep learning, like pre-trained language models, have achieved state-of-the-art results in many downstream Natural Language Processing tasks. This paper presents a simple but effective approach to improve the detection of cyberbullying situations by fine-tuning a pre-trained sentence transformer language model. We experimented on three datasets, and the results surpassed the state-of-the-art results. The approach could help prevent cyberbullying, filter those messages, and detect those children involved in the situation, providing opportunities to develop intervention programs to address this problem.
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