Publication Type
Journal Article
Version
acceptedVersion
Publication Date
3-2023
Abstract
Rumors can cause devastating consequences to individuals and our society. Analysis shows that the widespread of rumors typically results from deliberate promotion of information aiming to shape the collective public opinions on the concerned event. In this paper, we combat such chaotic phenomenon with a countermeasure by mirroring against how such chaos is created to make rumor detection more robust and effective. Our idea is inspired by adversarial learning method originated from Generative Adversarial Networks (GAN). We propose a GAN-style approach, where a generator is designed to produce uncertain or conflicting voices, further polarizing the original conversational threads to boost the discriminator. We reveal that feature learning effectiveness is highly relevant to the quality of generated parody. Given the strong natural language generation performance of transformer, we propose a transformer-based method to improve the generated posts, which appear to be closely responsive to the source post and retain the authentic propagation structure. Different from traditional data-driven rumor detection approaches, our method can capture low-frequency but more salient non-trivial discriminant patterns. Extensive experiments on THREE benchmarks demonstrate that our rumor detection method achieves much better results than state-of-the-art methods, and the transformer-based model further improve the performance of our GAN-style approach.
Keywords
Blogs, Feature extraction, Generative Adversarial Networks, Generators, Information Campaigns, Recurrent neural networks, Rumor Detection, Self-attention, Social networking (online), Training, Transformer, Transformers
Discipline
Databases and Information Systems | Numerical Analysis and Scientific Computing
Research Areas
Data Science and Engineering
Publication
IEEE Transactions on Knowledge and Data Engineering
Volume
35
Issue
3
First Page
2657
Last Page
2670
ISSN
1041-4347
Identifier
10.1109/TKDE.2021.3112497
Publisher
Institute of Electrical and Electronics Engineers
Citation
MA, Jing; LI, Jun; GAO, Wei; YANG, Yang; and WONG, Kam-Fai.
Improving rumor detection by promoting information campaigns with transformer-based generative adversarial learning. (2023). IEEE Transactions on Knowledge and Data Engineering. 35, (3), 2657-2670.
Available at: https://ink.library.smu.edu.sg/sis_research/6659
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/TKDE.2021.3112497
Included in
Databases and Information Systems Commons, Numerical Analysis and Scientific Computing Commons