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
[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.
张国标,李洁. 融合多模态内容语义一致性的社交媒体虚假新闻检测*[J]. 数据分析与知识发现, 2021, 5(5): 21-29.
Zhang Guobiao,Li Jie. Detecting Social Media Fake News with Semantic Consistency Between Multi-model Contents. Data Analysis and Knowledge Discovery, 2021, 5(5): 21-29.
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