BotUmc: An Uncertainty-Aware Twitter Bot Detection with Multi-view Causal Inference
This work addresses the challenge of low-confidence bot detection for social media platforms, offering an incremental improvement by integrating uncertainty quantification with existing methods.
The paper tackles the problem of detecting social bots on Twitter by quantifying uncertainty in detection outputs, using multi-view causal inference to select high-confidence decisions, and demonstrates superior performance in experiments.
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.