Twitter Bot Detection with Multi-Head Attention and Supervised Contrastive Learning
Liu Qiang, Jiazhong Lu, Yuanyuan Huang, Weisha Zhang, Jun Lv · 2024
The task of detecting bots on Twitter is an indispensable task for combating social network disinformation, curbing rumors, and managing online public opinion. However, today's social bots often disguise themselves by mimicking various aspects of real user characteristics to escape detection, which poses an unprecedented challenge to the existing Twitter bot detection task. To address this, an innovative detection method is presented in this study that improves the accuracy of Twitter bot detection by realizing the effective fusion of multi-dimensional features through the multi-head attention mechanism and applying it to a relational graph convolutional network. It also combines supervised contrastive learning to strengthen the differentiation ability of the high-latitude features. Experiments conducted on the publicly available dataset Twibot-20, compared to the baseline, the proposed method achieves approximately 1% better results in both Accuracy and the Matthews Correlation Coefficient, proving its effectiveness.