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arXiv · 2301.01877

When Cyber Aggression Prediction Meets BERT on Social Media

Zhenkun Zhou·Mengli Yu·Yuxin He·Xingyu Peng
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Abstract

Increasingly, cyber aggression becomes the prevalent phenomenon that erodes the social media environment. However, due to subjective and expense, the traditional self-reporting questionnaire is hard to be employed in the current cyber area. In this study, we put forward the prediction model for cyber aggression based on the cutting-edge deep learning algorithm. Building on 320 active Weibo users' social media activities, we construct basic, dynamic, and content features. We elaborate cyber aggression on three dimensions: social exclusion, malicious humour, and guilt induction. We then build the prediction model combined with pretrained BERT model. The empirical evidence shows outperformance and supports a stronger prediction with the BERT model than traditional machine learning models without extra pretrained information. This study offers a solid theoretical model for cyber aggression prediction. Furthermore, this study contributes to cyber aggression behaviors' probing and social media platforms' organization.

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Zhenkun Zhou, Mengli Yu, Yuxin He, Xingyu Peng. 2023-01-05. When Cyber Aggression Prediction Meets BERT on Social Media. https://arxiv.org/abs/2301.01877

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