A dual-branch multi-path propagation reasoning network for rumor detection integrating neural symbolic commonsense reasoning mechanism

Abstract

With the widespread dissemination of rumors on social media platforms, achieving automated rumor detection in the early stage has become an important challenge. To this end, we propose a Dual-branch Multi-path Propagation Reasoning Network (DMPRN) for rumor detection. For branch 1: to simulate various human thinking chains, we calculate the centrality of the nodes in the propagation graph and use pruning methods to construct propagation graphs of different paths. Then, we use the Graph Convolutional Network to capture the rumor propagation structure. For branch 2: to simulate human logical reasoning based on common sense, we design a Neural-Symbolic Commonsense Reasoning Mechanism. First, we use the Transformer network and the commonsense knowledge graph to dynamically reason about the commonsense knowledge related to tweets. Then, we use neural-symbolic learning to denoise the knowledge and tweets. Finally, we use logic operators $\wedge$ and $\vee$ to integrate the knowledge with the rumor content. The model achieves accuracies of 89.7%, 91.4%, and 78.6% on three publicly available datasets. Compared to state-of-the-art baseline methods, our approach improves accuracy by up to 3% across all three datasets. Moreover, experiments demonstrate that the proposed method is effective for early rumor detection.