Title
An Integrated Multi-Task Model for Fake News Detection
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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">fake news detection multi-task learning (FDML)</i> 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.
Year
DOI
Venue
2022
10.1109/TKDE.2021.3054993
IEEE Transactions on Knowledge and Data Engineering
Keywords
DocType
Volume
Fake news detection,multi-task learning,topic classification
Journal
34
Issue
ISSN
Citations 
11
1041-4347
0
PageRank 
References 
Authors
0.34
19
7
Name
Order
Citations
PageRank
Liao Qing11916.80
Heyan Chai200.34
Hao Han300.34
Xiang Zhang419534.67
Xuan Wang529157.12
Wen Xia629220.79
Ye Ding700.34