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Multi-level word features based on CNN for fake news detection in cultural communication

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A Correction to this article was published on 11 March 2022

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Abstract

In recent years, due to the booming development of online social networks, fake news has been appearing in large numbers and widespread in the online world. With deceptive words, online social network users can get infected by these online fake news easily, which has brought about tremendous effects on the offline society already. An important goal in improving the trustworthiness of information in online social networks is to identify the fake news timely. However, fake news detection remains to be a challenge, primarily because the content is crafted to resemble the truth in order to deceive readers, and without fact-checking or additional information, it is often hard to determine veracity by text analysis alone. In this paper, we first proposed multi-level convolutional neural network (MCNN), which introduced the local convolutional features as well as the global semantics features, to effectively capture semantic information from article texts which can be used to classify the news as fake or not. We then employed a method of calculating the weight of sensitive words (TFW), which has shown their stronger importance with their fake or true labels. Finally, we develop MCNN-TFW, a multiple-level convolutional neural network-based fake news detection system, which is combined to perform fake news detection in that MCNN extracts article representation and WS calculates the weight of sensitive words for each news. Extensive experiments have been done on fake news detection in cultural communication to compare MCNN-TFW with several state-of-the-art models, and the experimental results have demonstrated the effectiveness of the proposed model.

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Funding

This work is supported by the National key R&D Program of China under Grant No. 2018YFB0203901, 2016YFB1000604, and 2018YFB1402700 and the Key Research and Development Program of Shaanxi Province (No. 2018ZDXM-GY-036).

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Authors and Affiliations

  1. Xi’an Jiaotong University, 28 Xianning West Road, Xi’an, China

    Qingyuan Hu, Qian Li, Youshui Lu, Yue Yang & Jingxian Cheng

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  1. Qingyuan Hu
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  2. Qian Li
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  3. Youshui Lu
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  5. Jingxian Cheng
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Correspondence to Qingyuan Hu.

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The original online version of this article was revised due to change in the order of authors, addition of references and deletion of Figures 7 and 11.

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Hu, Q., Li, Q., Lu, Y. et al. Multi-level word features based on CNN for fake news detection in cultural communication. Pers Ubiquit Comput 24, 259–272 (2020). https://doi.org/10.1007/s00779-019-01289-y

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