Abstract
Fake news, or false information presented as news, is an increasing risk in today’s society. The practice of automatically detecting fake news is by no means an easy task, since the authors of fake news intend to confuse the readers and make them vulnerable to false information. Traditional methods only consider a limited number of characteristics of fake news, and hence, they face many difficulties in predicting the credibility of the news. This paper proposes WES, an integrated stacking model where the multiple-view text representation from (i) Word-level features, (ii) Emotional features, and (iii) Sentence-level features are used to classify the news article. The proposed system is applied on a real-world dataset, FakeNewsNet, and the experimental results show that the proposed approach achieves significantly better performance than the current state-of-the-art fake news detection method.
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Ha, T., Gao, X. (2021). Fake News Detection Using Multiple-View Text Representation. In: Pham, D.N., Theeramunkong, T., Governatori, G., Liu, F. (eds) PRICAI 2021: Trends in Artificial Intelligence. PRICAI 2021. Lecture Notes in Computer Science(), vol 13032. Springer, Cham. https://doi.org/10.1007/978-3-030-89363-7_8
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