Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
5-2019
Abstract
Rumors can cause devastating consequences to individual and/or society. Analysis shows that widespread of rumors typically results from deliberately promoted information campaigns which aim to shape collective opinions on the concerned news events. In this paper, we attempt to fight such chaos with itself to make automatic 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, complicating the original conversational threads in order to pressurize the discriminator to learn stronger rumor indicative representations from the augmented, more challenging examples. Different from traditional data-driven approach to rumor detection, our method can capture low-frequency but stronger non-trivial patterns via such adversarial training. Extensive experiments on two Twitter benchmark datasets demonstrate that our rumor detection method achieves much better results than state-of-the-art methods.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the Web Conference (WWW 2019)
First Page
3049
Last Page
3055
ISBN
9781450366748
Identifier
10.1145/3308558.3313741
Publisher
ACM Press
City or Country
San Francisco, CA, USA
Citation
MA, Jing; GAO, Wei; and WONG, Kam-Fai.
Detect rumors on Twitter by promoting information campaigns with generative adversarial learning. (2019). Proceedings of the Web Conference (WWW 2019). 3049-3055.
Available at: https://ink.library.smu.edu.sg/sis_research/4559
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/10.1145/3308558.3313741