Multi-Scale Feature Aggregation for Rumor Detection: Unveiling the Truth within Text

Social media plays a significant role in our lives, providing convenience in accessing information and expressing opinions through various platforms. However, this convenience has also led to the proliferation of rumors. Therefore, detecting rumors on social media has become increasingly important. Many existing works focus on detecting rumors by analyzing source posts and comments posted by users, utilizing pre-trained language models to capture text representations. However, simply averaging the comments from different users fails to capture the correlation between comments and source posts, which is crucial for rumor detection. To address this issue, in this paper, we propose a multi-scale feature aggregation method. We utilize a BERT-based pre-trained language model to encode the source posts and comment texts, obtaining token scale feature representations. Furthermore, to differentiate between different comments, we perform secondary aggregation of features at the comment scale, mitigating the limitations of previous methods that treat all comments equally. We then employ an attention layer to obtain a correlation weight matrix between the source posts and comments, which represents their correlation. This approach significantly extracts comment content most relevant to the current event being detected. Experimental results on existing datasets in both Chinese and English demonstrate the superiority of our method compared to other existing approaches. Further experiments also confirm the importance of the multi-scale approach in mining the correlation between source posts and comments for rumor detection.