DSMM: A dual stance-aware multi-task model for rumour veracity on social networks

Rumour veracity on social networks aims to determine the authenticity of a claim based on the conversation graph. Current works typically employ graph neural networks (GNNs) to model the conversation graph and leverage the users' stance classification on the claim (referred to as global stance) to enhance rumour veracity with multi-task learning methods. However, due to the over-smoothing problem, the layers of GNNs are usually very shallow. In Consequence, the graph nodes with limited receptive fields cannot perceive the semantic information of the claim node. This results in GNNs being sub-optimal for global stance learning. To address this issue, we present a novel dual stance-aware multi-task model, DSMM, to improve the rumour veracity results. Firstly, we design a position-aware semantic compensation method, which relies on the relative topological positions between the claim node and comment nodes to compensate graph nodes with different degrees of claim semantics, thus enhancing the awareness of graph nodes for claim semantics. In addition, we propose using local stance, the attitude tendency of comment nodes towards their predecessor nodes, to model local interactions of the conversation graph for the first time. Since the local stance is a fine-grained interaction representation of the conversation graph, it can complement the global stance and enhance the model's understanding for conversation graphs. Further, we introduce the directed attention network to simulate the directedness of local stances and differentiate the importance of different local stances simultaneously. Finally, we use the cross-attention technique to fuse information from the above two stance classifications into the rumour veracity module to enhance model performance further. Experimental results on two publicly available competition datasets demonstrate the effectiveness of our approach.