[Objective] This paper proposes a multi-perspective evidence fusion model for identifying fake news, aiming to address the issues of lacking evidence and inaccurate classification in traditional model. [Methods] With the help of subjective logical model and uncertainty measurements for the classification from different perspectives, we modified the Dempster-Shafer evidence theory. Then, we used different weights to combine the evidence from multiple perspectives, and obtained the uncertainty measurements of the overall evidence and classification. [Results] We examined our model with two public data sets, and found its accuracy and F1 values were significantly higher than the traditional models. [Limitations] Evidence fusion from multiple perspectives generated some noise, which might reduce the accuracy of the results. [Conclusions] Multi-perspective evidence fusion could effectively identify fake news.
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