基于递归图神经网络的网络论坛谣言检测
Recurrent Graph Neural Networks for Rumor Detection in Online Forums
摘要：
在线社交网络在日常生活中的广泛采用产生了对有效地分类用户生成的内容的迫切需要。这项工作提出了分类链接的内容在论坛网站上传播的技术-特别是，链接到新闻文章或博客-单独使用用户交互信号。重要的是，Reddit等在线论坛没有用户生成的社交图谱，这是在基于社交网络行为的分类设置中假设的。使用Reddit作为案例研究，我们展示了如何获得派生的社交图，并使用此图，Reddit帖子序列和评论树作为递归图神经网络（R-GNN）编码器的输入。我们训练了R-GNN的新闻链接分类和谣言检测，显示出优于最近基线的结果。我们的代码可在https://github.com/google-research/social_cascades上公开获取。
英文摘要：
The widespread adoption of online social networks in daily life has created a pressing need for effectively classifying user-generated content. This work presents techniques for classifying linked content spread on forum websites -- specifically, links to news articles or blogs -- using user interaction signals alone. Importantly, online forums such as Reddit do not have a user-generated social graph, which is assumed in social network behavioral-based classification settings. Using Reddit as a case-study, we show how to obtain a derived social graph, and use this graph, Reddit post sequences, and comment trees as inputs to a Recurrent Graph Neural Network (R-GNN) encoder. We train the R-GNN on news link categorization and rumor detection, showing superior results to recent baselines. Our code is made publicly available at https://github.com/google-research/social_cascades.
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。
