@inproceedings{798848bcf757434a802465d50cff449a,
title = "Supervised group embedding for rumor detection in social media",
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.",
keywords = "Convolutional Neural Network, Rumor detection, Social media",
author = "Yuwei Liu and Xingming Chen and Yanghui Rao and Haoran Xie and Qing Li and Jun Zhang and Yingchao Zhao and Wang, {Fu Lee}",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-19274-7_11",
language = "English",
isbn = "9783030192730",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "139--153",
editor = "Maxim Bakaev and Flavius Frasincar and In-Young Ko",
booktitle = "Web Engineering - 19th International Conference, ICWE 2019, Proceedings",
note = "19th International Conference on Web Engineering, ICWE 2019 ; Conference date: 11-06-2019 Through 14-06-2019",
}