Abstract
Fake news detection is a real problem, especially in social media. The untruth news dispreads very quickly and brings huge damage. This paper is devoted to proposing a topic modeling method that allows for comparing tweets, which is one of the stages of fake news detection. Our method, named Content Weighted Topic (CWT), is based on applying WordNet. Experiments showed that our method is better than the well-known Latent Dirichlet Allocation (LDA) algorithm regarding topic coherence measure. Our CWT method assigns topics for tweets that are more consistent than topics assigned by the LDA algorithm.
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Bezerra, J.F.R., Pietranik, M., Nguyen, T.T., Kozierkiewicz, A. (2023). A New Topic Modeling Method for Tweets Comparison. In: Nguyen, N.T., et al. Computational Collective Intelligence. ICCCI 2023. Lecture Notes in Computer Science(), vol 14162. Springer, Cham. https://doi.org/10.1007/978-3-031-41456-5_25
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DOI: https://doi.org/10.1007/978-3-031-41456-5_25
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