BotUmc: An Uncertainty-Aware Twitter Bot Detection with Multi-view Causal Inference

• URL: http://arxiv.org/abs/2503.03775v1
• Date: Tue, 04 Mar 2025 13:39:31 GMT
• Title: BotUmc: An Uncertainty-Aware Twitter Bot Detection with Multi-view Causal Inference
• Authors: Tao Yang, Yang Hu, Feihong Lu, Ziwei Zhang, Qingyun Sun, Jianxin Li,
• Abstract summary: We propose an uncertainty-aware bot detection method to inform the confidence and use the uncertainty score to pick a high-confidence decision from multiple views of a social network under different environments.<n>Specifically, our proposed BotUmc uses LLM to extract information from tweets. Then, we construct a graph based on the extracted information, the original user information, and the user relationship and generate multiple views of the graph by causal interference. Lastly, an uncertainty loss is used to force the model to quantify the uncertainty of results and select the result with low uncertainty in one view as the final decision.
• Score: 30.448232690207387
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Social bots have become widely known by users of social platforms. To prevent social bots from spreading harmful speech, many novel bot detections are proposed. However, with the evolution of social bots, detection methods struggle to give high-confidence answers for samples. This motivates us to quantify the uncertainty of the outputs, informing the confidence of the results. Therefore, we propose an uncertainty-aware bot detection method to inform the confidence and use the uncertainty score to pick a high-confidence decision from multiple views of a social network under different environments. Specifically, our proposed BotUmc uses LLM to extract information from tweets. Then, we construct a graph based on the extracted information, the original user information, and the user relationship and generate multiple views of the graph by causal interference. Lastly, an uncertainty loss is used to force the model to quantify the uncertainty of results and select the result with low uncertainty in one view as the final decision. Extensive experiments show the superiority of our method.

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