物以类聚，人以群分：强化多模态相关性用于虚假信息检测
Like Article, Like Audience: Enforcing Multimodal Correlations for Disinformation Detection
AI总结：
本研究利用用户生成内容与共享内容的相关性，提出仅在训练阶段引入用户画像引导的多模态虚假信息检测算法，适配三类主流神经分类器，能更精准区分真假新闻。
摘要：
用户生成内容（如推文、个人简介）以及用户间共享的内容（如新闻文章）反映了用户的在线身份。本文探究能否利用用户生成内容与用户共享内容之间的相关性，来检测在线新闻文章中的虚假信息。我们提出了一种用于虚假信息检测的多模态学习算法。新闻文章与用户生成内容的潜在表示使得模型在训练过程中，能受到那些偏好与待评估新闻文章相似内容的用户的画像引导，且若该内容在不同用户间被共享，这种引导效应会得到增强。由于仅在模型优化阶段利用用户信息，模型在预测文章真实性时无需依赖用户画像。该算法已成功应用于三种广泛使用的神经分类器，并在不同数据集上取得了结果。可视化技术表明，所提出的模型能学习到未见过的新闻文章的特征表示，可更好地区分假新闻与真新闻文本。
英文摘要：
User-generated content (e.g., tweets and profile descriptions) and shared content between users (e.g., news articles) reflect a user's online identity. This paper investigates whether correlations between user-generated and user-shared content can be leveraged for detecting disinformation in online news articles. We develop a multimodal learning algorithm for disinformation detection. The latent representations of news articles and user-generated content allow that during training the model is guided by the profile of users who prefer content similar to the news article that is evaluated, and this effect is reinforced if that content is shared among different users. By only leveraging user information during model optimization, the model does not rely on user profiling when predicting an article's veracity. The algorithm is successfully applied to three widely used neural classifiers, and results are obtained on different datasets. Visualization techniques show that the proposed model learns feature representations of unseen news articles that better discriminate between fake and real news texts.
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。
