Abstract
To detect rumors automatically in social media, methods based on recurrent neural network and convolutional neural network have been proposed. These methods split a stream of posts related to an event into several groups along time, and represent each group using unsupervised methods such as paragraph vector. However, many posts in a group (e.g., retweeted posts) do not contribute much to rumor detection, which deteriorates the performance of rumor detection based on unsupervised group embedding. In this paper, we propose a Supervised Group Embedding based Rumor Detection (SGERD) model that considers both textual and temporal information. Particularly, SGERD exploits post-level textual information to generate group embeddings, and is able to identify salient posts for further analysis. Experimental results on two real-world datasets demonstrate the effectiveness of our proposed model.
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Acknowledgments
We are grateful to the anonymous reviewers for their valuable comments on this manuscript. This research has been supported in part by the National Natural Science Foundation of China (U1611264), a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (UGC/FDS11/E03/16), the Individual Research Scheme of the Dean’s Research Fund 2017–2018 (FLASS/DRF/IRS-8), Top-up Fund for General Research Fund/Early Career Scheme (TFG-3) and Seed Fund for General Research Fund/Early Career Scheme (SFG-6) of the 2018 Dean’s Research Fund to MIT Department, Small Grant for Academic Staff (MIT/SGA05/18-19) of The Education University of Hong Kong, and a Collaborative Research Grant (project no. C1031-18G) from the Research Grants Council of Hong Kong SAR.
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Liu, Y. et al. (2019). Supervised Group Embedding for Rumor Detection in Social Media. In: Bakaev, M., Frasincar, F., Ko, IY. (eds) Web Engineering. ICWE 2019. Lecture Notes in Computer Science(), vol 11496. Springer, Cham. https://doi.org/10.1007/978-3-030-19274-7_11
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