Contrastive Graph Learning for Cross-document Misinformation Detection
Misinformation proliferates on the Internet,undermining the normal functioning of various industries.Detecting false-hoods accurately has therefore become an urgent challenge.Existing research on this task focuses primarily on three aspects:ac-count traits,textual content,and multimodality.However,most methods overlook the key attribute of misinformation diffusion the novelty of content.They analyze the veracity of target claims in isolation,failing to capture public opinion dynamics.To ad-dress this issue,this paper proposes a cross-document misinformation detection framework called contrastive graph learning(CAL).CAL focuses on content novelty and comprises two key components:a contrastive learning module and a heterogeneous graph module.The former expands the representational difference between factual and false claims,and the latter encompasses five entity types:words,events,event sets,sentences,and documents.It injects semantic features of the public discourse into enti-ty embeddings.We evaluate CAL on the IED,TL17,and Crisis datasets at both document and event levels.CGL achieves state-of-the-art performance,which verifies the efficacy of its design.It provides a robust solution for combating misinformation by mode-ling novelty and environmental context.
万方数据
维普
