An Integrated Multi-Task Model for Fake News Detection
- 28 January 2021
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Knowledge and Data Engineering
- Vol. 34 (11), 5154-5165
- https://doi.org/10.1109/tkde.2021.3054993
Abstract
Fake news detection attracts many researchers' attention due to the negative impacts on the society. Most existing fake news detection approaches mainly focus on semantic analysis of news' contents. However, the detection performance will dramatically decrease when the content of news is short. In this paper, we propose a novel $fake news detection multi-task learning (FDML)$ model based on the following observations: 1) some certain topics have higher percentages of fake news; and 2) some certain news authors have higher intentions to publish fake news. FDML model investigates the impact of topic labels for the fake news and introduce contextual information of news at the same time to boost the detection performance on the short fake news. Specifically, the FDML model consists of representation learning and multi-task learning parts to train the fake news detection task and the news topic classification task, simultaneously. As far as we know, this is the first fake news detection work that integrates the above two tasks. The experiment results show that the FDML model outperforms state-of-the-art methods on real-world fake news dataset.
Keywords
Funding Information
- Guangdong Major Project of Basic and Applied Basic Research (2019B030302002)
- National Key Research and Development Program of China (2017YFB0202201)
- Basic Research Project of Shenzhen (JCYJ20180306174743727)
- National Natural Science Foundation of China (62076079, U1711261)
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