Contrastive Learning for Rumor Detection via Fitting Beta Mixture Model

Jiachen Ma, Jing Dai, Yong Liu, Meng Han, Chunyu Ai

Abstract: The rise of social media has posed a challenging problem of effectively identifying rumors. With the great success of contrastive learning in many fields, many contrastive learning models for rumor detection have been proposed. However, existing models usually use the propagation structure of other events as negative samples and regard more similar samples to anchor events as hard ones across all the training processes, resulting in undesirably pushing away the samples of the same class. Thus, we propose a novel contrastive learning model (CRFB) to solve the above problem. Specifically, we employ contrastive learning between two augmented propagation structure and fit a two-component (true-false) beta mixture model (BMM) to measure the probability of negative samples being true. In addition, we propose a CNN-based model to capture the consistent and complementary information between two augmented propagation structure. The experimental results on public datasets demonstrate that our CRFB outperforms the existing state-of-the-art models for rumor detection.