Conference Proceedings
Early Rumour Detection
Kaimin Zhou, Chang Shu, Binyang Li, Jey Han Lau
Association for Computational Linguistics | Published : 2019
DOI: 10.18653/v1/n19-1163
Abstract
Rumours can spread quickly through social media, and malicious ones can bring about significant economical and social impact. Motivated by this, our paper focuses on the task of rumour detection; particularly, we are interested in understanding how early we can detect them. Although there are numerous studies on rumour detection, few are concerned with the timing of the detection. A successfully-detected malicious rumour can still cause significant damage if it isn’t detected in a timely manner, and so timing is crucial. To address this, we present a novel methodology for early rumour detection. Our model treats social media posts (e.g. tweets) as a data stream and integrates reinforcement l..
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Awarded by National Natural Science Foundation of China
Funding Acknowledgements
This work is partially funded by the National Natural Science Foundation of China (61502115, U1636103, U1536207). We would also like to thank Wei Gao and Jing Li for their valuable suggestions.