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Semi-Supervised Learning and Graph Neural Networks for Fake News Detection

Abstract : Social networks have become the main platforms for information dissemination. Nevertheless, due to the increasing number of users, social media platforms tend to be highly vulnerable to the propagation of disinformation-making the detection of fake news a challenging task. In this work, we focus on content-based methods for detecting fake news-casting the problem to a binary text classification one (an article corresponds to either fake news or not). The main challenge here stems from the fact that the number of labeled data is limited; very few articles can be examined and annotated as fake. To this extend, we opted for semi-supervised learning approaches. In particular, our work proposes a graph-based semi-supervised fake news detection method, based on graph neural networks. The experimental results indicate that the proposed methodology achieves better performance compared to traditional classification techniques, especially when trained on limited number of labeled articles.
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Submitted on : Saturday, October 26, 2019 - 2:51:06 PM
Last modification on : Friday, February 4, 2022 - 3:25:25 AM
Long-term archiving on: : Monday, January 27, 2020 - 12:41:43 PM


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  • HAL Id : hal-02334445, version 1


Adrien Benamira, Benjamin Devillers, Etienne Lesot, Ayush K. Ray, Manal Saadi, et al.. Semi-Supervised Learning and Graph Neural Networks for Fake News Detection. ASONAM 2019 - IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Aug 2019, Vancouver, Canada. ⟨hal-02334445⟩



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