SEN-CTD: semantic enhancement network with content-title discrepancy for fake news detection
International Journal of Web Information Systems
ISSN: 1744-0084
Article publication date: 4 November 2024
Issue publication date: 19 November 2024
Abstract
Purpose
The discrepancy between the content of an article and its title is a key characteristic of fake news. Current methods for detecting fake news often ignore the significant difference in length between the content and its title. In addition, relying solely on textual discrepancies between the title and content to distinguish between real and fake news has proven ineffective. The purpose of this paper is to develop a new approach called semantic enhancement network with content–title discrepancy (SEN–CTD), which enhances the accuracy of fake news detection.
Design/methodology/approach
The SEN–CTD framework is composed of two primary modules: the SEN and the content–title comparison network (CTCN). The SEN is designed to enrich the representation of news titles by integrating external information and position information to capture the context. Meanwhile, the CTCN focuses on assessing the consistency between the content of news articles and their corresponding titles examining both emotional tones and semantic attributes.
Findings
The SEN–CTD model performs well on the GossipCop, PolitiFact and RealNews data sets, achieving accuracies of 80.28%, 86.88% and 84.96%, respectively. These results highlight its effectiveness in accurately detecting fake news across different types of content.
Originality/value
The SEN is specifically designed to improve the representation of extremely short texts, enhancing the depth and accuracy of analyses for brief content. The CTCN is tailored to examine the consistency between news titles and their corresponding content, ensuring a thorough comparative evaluation of both emotional and semantic discrepancies.
Keywords
Acknowledgements
This work was supported by the Natural Science Foundation of Shandong Province (ZR2022LZH016), the National Natural Science Foundation of China (72471103), the Shandong Provincial Key R&D Program of China (2021CXGC010103) and the Shandong Provincial Teaching Research Project of Graduate Education (SDYAL2022102 and SDYJG21034).
Declarations: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Citation
Fang, J., Ma, K., Qiu, Y., Ji, K., Chen, Z. and Yang, B. (2024), "SEN-CTD: semantic enhancement network with content-title discrepancy for fake news detection", International Journal of Web Information Systems, Vol. 20 No. 6, pp. 603-620. https://doi.org/10.1108/IJWIS-04-2024-0116
Publisher
:Emerald Publishing Limited
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