[Objective] This paper proposes a fake news detection method based on the difference between news titles and contents, aiming to address the issues of extracting features from short news texts or retrieving comments. [Methods] Firstly, we designed the Cos-Gap calculation method to obtain the difference between news titles and contents’ textual and emotional features. Then, we constructed a News Differential Heterogeneous Graph Network (NDHN) based on the obtained differential features and the Heterogeneous Graph Attention Networks. The NDHN contains edges constructed based on differential features and nodes constructed based on semantic and emotional features of title, content, and emotion. [Results] We examined the proposed model on the GossipCop dataset and found that the NDHN can improve the classification accuracy by 2.7% and the F1 by 3.2%. [Limitations] This method is suitable for analyzing the news with title and has limitations for untitled texts from Sina Weibo or Twitter. [Conclusions] The new model could effectively detect fake news from social media.
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