Identifying Fake News with External Knowledge and User Interaction Features

[Objective] This paper proposes a multidimensional-data classification model to improve the efficiency of fake news detection. The new model incorporates external knowledge features and user interaction features to reduce fake news spreading in social media. [Methods] First, we extracted the background knowledge of fake news. Then, we introduced external knowledge through the Wikipedia knowledge graph to detect the consistency between the news content and the existing knowledge system. Third, we analyzed the user interaction on the communication chain according to the psychological“similarity effect”. Finally, we improved the connection edge weight of the graph convolutional network to reflect the interaction between users. [Results] We examined the new model’s performance with two public datasets, Twitter15 and Twitter16. Compared with the other five similar models, our model’s accuracy reached 0.901 and 0.927. [Limitations] We did not consider features like knowledge information and language expression hidden in the additional news content. The model’s interpretability needs to be further improved. [Conclusions] By integrating news content, external knowledge, and user interaction characteristics of the communication chain, the proposed model can effectively detect fake news. © 2023 Chinese Academy of Sciences. All rights reserved.