沉默胜过千言万语：虚假新闻检测中代表性不足用户的重加权技术
Silence Speaks Volumes: Re-weighting Techniques for Under-Represented Users in Fake News Detection
AI总结：
针对虚假新闻检测中因参与度不平等导致模型偏向活跃用户的问题，提出利用重加权技术让沉默用户发声，并研究其线索能否提升模型检测性能。
摘要：
社交媒体平台为分析用户行为提供了丰富的环境。近期，基于深度学习的方法已成为涉及复杂模式的社交媒体分析模型的主流方法。然而，这些方法容易受到训练数据中偏差的影响，例如参与度不平等。基本上，仅有1%的用户在社交网站上产生了大部分内容，而其余用户尽管参与程度不同，但在内容创建方面往往不那么活跃，且在很大程度上是沉默的。这些沉默用户消费并倾听平台上传播的信息。然而，他们的声音、态度和兴趣并未反映在在线内容中，使得当前方法的决策倾向于活跃用户的意见。因此，模型可能会将最响亮的用户误认为是多数。我们提出利用重加权技术让沉默的大多数被听到，进而研究来自这些用户的线索是否能提升当前模型在虚假新闻检测下游任务中的性能。
英文摘要：
Social media platforms provide a rich environment for analyzing user behavior. Recently, deep learning-based methods have been a mainstream approach for social media analysis models involving complex patterns. However, these methods are susceptible to biases in the training data, such as participation inequality. Basically, a mere 1% of users generate the majority of the content on social networking sites, while the remaining users, though engaged to varying degrees, tend to be less active in content creation and largely silent. These silent users consume and listen to information that is propagated on the platform. However, their voice, attitude, and interests are not reflected in the online content, making the decision of the current methods predisposed towards the opinion of the active users. So models can mistake the loudest users for the majority. We propose to leverage re-weighting techniques to make the silent majority heard, and in turn, investigate whether the cues from these users can improve the performance of the current models for the downstream task of fake news detection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
