Fake News Detection Using CNN-based Sentiment Change Patterns

KIPS Transactions on Software and Data Engineering, Vol. 12, No. 4, pp. 179-188, Apr. 2023
https://doi.org/10.3745/KTSDE.2023.12.4.179,   PDF Download:
Keywords: Convolutional neural networks, Sentiment Change Patterns, fake news

Recently, fake news disguises the form of news content and appears whenever important events occur, causing social confusion. Accordingly, artificial intelligence technology is used as a research to detect fake news. Fake news detection approaches such as automatically recognizing and blocking fake news through natural language processing or detecting social media influencer accounts that spread false information by combining with network causal inference could be implemented through deep learning. However, fake news detection is classified as a difficult problem to solve among many natural language processing fields. Due to the variety of forms and expressions of fake news, the difficulty of feature extraction is high, and there are various limitations, such as that one feature may have different meanings depending on the category to which the news belongs. In this paper, emotional change patterns are presented as an additional identification criterion for detecting fake news. We propose a model with improved performance by applying a convolutional neural network to a fake news data set to perform analysis based on content characteristics and additionally analyze emotional change patterns. Sentimental polarity is calculated for the sentences constituting the news and the result value dependent on the sentence order can be obtained by applying long-term and short-term memory. This is defined as a pattern of emotional change and combined with the content characteristics of news to be used as an independent variable in the proposed model for fake news detection. We train the proposed model and comparison model by deep learning and conduct an experiment using a fake news data set to confirm that emotion change patterns can improve fake news detection performance.

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Cite this article
[IEEE Style]
T. W. Lee, J. S. Park, J. G. Shon, "Fake News Detection Using CNN-based Sentiment Change Patterns," KIPS Transactions on Software and Data Engineering, vol. 12, no. 4, pp. 179-188, 2023. DOI: https://doi.org/10.3745/KTSDE.2023.12.4.179.

[ACM Style]
Tae Won Lee, Ji Su Park, and Jin Gon Shon. 2023. Fake News Detection Using CNN-based Sentiment Change Patterns. KIPS Transactions on Software and Data Engineering, 12, 4, (2023), 179-188. DOI: https://doi.org/10.3745/KTSDE.2023.12.4.179.