PostCom2DR: Utilizing information from post and comments to detect rumors

The development of social media has caused a boom in information sharing, but it also provides an ideal platform for publishing and spreading rumors. On social media platforms, there are a lot of comments, which contain the user’s most direct views and reactions to the post. They can be utilized as clues to detect rumors. Recently, some methods are proposed to detect rumors through post and comments which usually focus on content information. However, except for this, other information such as reply structure, mutual selection information between post and comments, topic drift within comments also can help detect rumors. In this paper, we propose a novel model, PostCom2DR, for rumor detection. In PostCom2DR, a reply graph between post and comments is first created. Then a bilevel GCN and self-attention mechanism are built to learn the representation of comments. Secondly, a post-comment co-attention mechanism is introduced to selectively fuse information, and this helps the model focus on more relevant information. Through the above modules, we can get the global representation of post and comments. In addition, a 1D CNN is built to capture the local topic drift on time series inside the comments. Finally, we concatenate the global representation and local representation for rumor detection. Extensive experiments conducted on Chinese and English datasets show that PostCom2DR significantly outperforms other state-of-the-art models on both rumor detection and early detection.