%A Zhang Guobiao,Li Jie %T Detecting Social Media Fake News with Semantic Consistency Between Multi-model Contents %0 Journal Article %D 2021 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.2096-3467.2020.0884 %P 21-29 %V 5 %N 5 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4945.shtml} %8 2021-05-25 %X

[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.