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The ease of spreading false information online enables individuals withmalicious intent to manipulate public opinion and destabilize social stability.Recently, fake news detection based on evidence retrieval has gained popularityin an effort to identify fake news reliably and reduce its impact. Evidenceretrieval-based methods can improve the reliability of fake news detection bycomputing the textual consistency between the evidence and the claim in thenews. In this paper, we propose a framework for fake news detection based onMUlti-Step Evidence Retrieval enhancement (MUSER), which simulates the steps ofhuman beings in the process of reading news, summarizing, consulting materials,and inferring whether the news is true or fake. Our model can explicitly modeldependencies among multiple pieces of evidence, and perform multi-stepassociations for the evidence required for news verification through multi-stepretrieval. In addition, our model is able to automatically collect existingevidence through paragraph retrieval and key evidence selection, which can savethe tedious process of manual evidence collection. We conducted extensiveexperiments on real-world datasets in different languages, and the resultsdemonstrate that our proposed model outperforms state-of-the-art baselinemethods for detecting fake news by at least 3% in F1-Macro and 4% in F1-Micro.Furthermore, it provides interpretable evidence for end users.

MUSER: A MUlti-Step Evidence Retrieval Enhancement Framework for Fake News Detection