Abstract
The detection of misinformation as “fake news” is vital for a well-informed and highly functioning society. Most of the recent works on the identification of fake news make use of deep learning and large language models to achieve high levels of performance. However, traditional fake news detection methods may lack a nuanced “understanding” of content, including ignoring important information in the form of potential aspects in documents or relying on external knowledge sources to identify such aspects. This paper focuses on aspect-based fake news detection, which aims to uncover deceptive narratives through fine-grained analysis of news articles. We propose a novel aspect-based fake news detection method based on a lower, paragraph-level attention mechanism that identifies different aspects within a news-related document. The proposed approach utilizes aspects to provide concise yet meaningful representations of long news articles without reliance on any external reference knowledge. We investigate the impact of learning aspects from documents on the effectiveness of fake news detection. Our experiments on four benchmark datasets show statistically significant improvements over the results of several baseline models.
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Hou, Z., Ofoghi, B., Zaidi, N., Yearwood, J. (2024). Aspect-Based Fake News Detection. In: Yang, DN., Xie, X., Tseng, V.S., Pei, J., Huang, JW., Lin, J.CW. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2024. Lecture Notes in Computer Science(), vol 14650. Springer, Singapore. https://doi.org/10.1007/978-981-97-2266-2_8
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DOI: https://doi.org/10.1007/978-981-97-2266-2_8
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