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.

翻译：社交媒体平台为分析用户行为提供了丰富的环境。近年来，基于深度学习的方法已成为处理复杂模式的社交媒体分析模型的主流手段。然而，这些方法容易受到训练数据中偏差的影响，例如参与不平等。基本而言，社交网络中仅1%的用户生成了大部分内容，而其余用户尽管参与程度不同，但在内容创作方面往往不太活跃且基本保持沉默。这些沉默用户消费并倾听平台上传播的信息，但其声音、态度和兴趣并未在在线内容中反映出来，导致当前方法的决策倾向于活跃用户的观点。因此，模型可能将最活跃的用户误认为多数派。我们提出利用重加权技术让沉默的大多数得以发声，进而探究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。