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Detecting Social Media Fake News with Semantic Consistency Between Multi-model Contents |
Zhang Guobiao1,2,Li Jie3() |
1School of Information Management, Wuhan University, Wuhan 430072, China 2Institute for Information Retrieval and Knowledge Mining, Wuhan University, Wuhan 430072, China 3School of Sociology, Soochow University, Suzhou 215000, China |
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Abstract [Objective] This study aims to detect fake news on social media earlier and curb the dissemination of mis/dis-information. [Methods] Based on the features of news images and texts, we mapped the images to semantic tags and calculated the semantic consistency between images and texts. Then, we constructed a model to detect fake news. Finally, we examined our new model with the FakeNewsNet dataset. [Results] The F1 value of our model was up to 0.775 on PolitiFact data and 0.879 on GossipCop data. [Limitations] Due to the limits of existing annotation methods for image semantics, we could not accurately describe image contents, and calculate semantic consistency. [Conclusions] The constructed model could effectively detect fake news from social media.
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Received: 08 September 2020
Published: 24 November 2020
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Fund:The work is supported by Soochow University 2020 Humanities and Social Sciences Excellent Academic Team Project(NH33711520) |
Corresponding Authors:
Li Jie
E-mail: allison_lijie@163.com
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