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. Copyright © 2019 Springer Nature Switzerland AG.
|Title of host publication||Web engineering: 19th International Conference, ICWE 2019, Daejeon, South Korea, June 11–14, 2019, proceedings|
|Editors||Maxim BAKAEV, Flavius FRASINCAR, In-Young KO|
|Place of Publication||Cham|
|Publication status||Published - 2019|
Recurrent neural networks
CitationLiu, Y., Chen, X., Rao, Y., Xie, H., Li, Q., Zhang, J., . . . Wang, F. L. (2019). Supervised group embedding for rumor detection in social media. In M. Bakaev, F. Frasincar, & I.-Y. Ko (Eds.), Web engineering: 19th International Conference, ICWE 2019, Daejeon, South Korea, June 11–14, 2019, proceedings (pp. 139-153). Cham: Springer.
- Rumor detection
- Social media
- Convolutional neural network