Don't Be Misled by Emotion! Disentangle Emotions and Semantics for Cross-Language and Cross-Domain Rumor Detection

Cross-language and cross-domain rumor detection is a crucial research topic for maintaining a healthy social media environment. Previous studies reveal that the emotions expressed in posts are important features for rumor detection. However, existing studies typically leverage the entangled representation of semantics and emotions, ignoring the fact that different languages and domains have different emotions toward rumors. Therefore, it inevitably leads to a biased adaptation of the features learned from the source to the target language and domain. To address this issue, this paper proposes a novel approach to adapt the knowledge obtained from the source to the target dataset by disentangling the emotional and semantic features of the datasets. Specifically, the proposed method mainly consists of three steps: (1) disentanglement, which encodes rumors into two separate semantic and emotional spaces to prevent emotional interference; (2) adaptation, merging semantics with the emotions from another language and domain for contrastive alignment to ensure effective adaptation; (3) joint training strategy, which enables the above two steps to work in synergy and mutually promote each other. Extensive experimental results demonstrate that the proposed method outperforms state-of-the-art baselines.