提出不确定性感知的多视角因果推断方法，提升社交媒体机器人检测可信度。
BotUmc: An Uncertainty-Aware Twitter Bot Detection with Multi-view Causal Inference
- 基于大模型提取推文信息，构建多视角图结构并用因果推断生成不同视图
- 引入不确定损失函数，使模型输出可解释的置信度分数
- 在多个环境下选择低不确定性结果，适合高可靠性场景应用
社交机器人在社交媒体平台中已广为人知。为防止其传播有害言论，已有诸多检测方法被提出。然而，随着社交机器人的演进，现有方法在样本上难以给出高置信度判断。这促使我们量化输出的不确定性，以反映结果可信度。为此，我们提出一种不确定性感知的机器人检测方法（BotUmc），通过多视角因果推断在不同环境下的社交网络中选取高置信度决策。具体地，使用大语言模型（LLM）提取推文信息，结合原始用户信息与用户关系构建图结构，并通过因果干预生成多个视图。最后，采用不确定损失函数迫使模型量化结果不确定性，并选择单一视图中不确定性最低的结果作为最终判定。大量实验表明该方法具有显著优势。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。