Emotion-Drive Interpretable Fake News Detection

Xiaoyi Ge, Mingshu Zhang, Xu An Wang, Jia Liu, Bin Wei

Source Title: International Journal of Data Warehousing and Mining (IJDWM)18(1)

ISSN: 1548-3924|EISSN: 1548-3932|EISBN13: 9781799893684|DOI: 10.4018/IJDWM.314585

MLA

Ge, Xiaoyi, et al. "Emotion-Drive Interpretable Fake News Detection." IJDWM vol.18, no.1 2022: pp.1-17. http://doi.org/10.4018/IJDWM.314585

APA

Ge, X., Zhang, M., Wang, X. A., Liu, J., & Wei, B. (2022). Emotion-Drive Interpretable Fake News Detection. International Journal of Data Warehousing and Mining (IJDWM), 18(1), 1-17. http://doi.org/10.4018/IJDWM.314585

Chicago

Ge, Xiaoyi, et al. "Emotion-Drive Interpretable Fake News Detection," International Journal of Data Warehousing and Mining (IJDWM) 18, no.1: 1-17. http://doi.org/10.4018/IJDWM.314585

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Abstract

Fake news has brought significant challenges to the healthy development of social media. Although current fake news detection methods are advanced, many models directly utilize unselected user comments and do not consider the emotional connection between news content and user comments. The authors propose an emotion-driven explainable fake news detection model (EDI) to solve this problem. The model can select valuable user comments by using sentiment value, obtain the emotional correlation representation between news content and user comments by using collaborative annotation, and obtain the weighted representation of user comments by using the attention mechanism. Experimental results on Twitter and Weibo show that the detection model significantly outperforms the state-of-the-art models and provides reasonable interpretation.