TY - GEN
T1 - Supervised group embedding for rumor detection in social media
AU - Liu, Yuwei
AU - Chen, Xingming
AU - Rao, Yanghui
AU - Xie, Haoran
AU - Li, Qing
AU - Zhang, Jun
AU - Zhao, Yingchao
AU - Wang, Fu Lee
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Convolutional Neural Network
KW - Rumor detection
KW - Social media
UR - http://www.scopus.com/inward/record.url?scp=85065497389&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-19274-7_11
DO - 10.1007/978-3-030-19274-7_11
M3 - Conference contribution
AN - SCOPUS:85065497389
SN - 9783030192730
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 139
EP - 153
BT - Web Engineering - 19th International Conference, ICWE 2019, Proceedings
A2 - Ko, In-Young
A2 - Bakaev, Maxim
A2 - Frasincar, Flavius
T2 - 19th International Conference on Web Engineering, ICWE 2019
Y2 - 11 June 2019 through 14 June 2019
ER -