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.
Original language | English |
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Title of host publication | Web Engineering - 19th International Conference, ICWE 2019, Proceedings |
Editors | Maxim BAKAEV, Flavius FRASINCAR, In-Young KO |
Place of Publication | Switzerland |
Publisher | Springer Nature Switzerland AG |
Pages | 139-153 |
Number of pages | 15 |
ISBN (Electronic) | 9783030192747 |
ISBN (Print) | 9783030192730 |
DOIs | |
Publication status | Published - 2019 |
Externally published | Yes |
Event | 19th International Conference on Web Engineering - Daejeon, Korea, Republic of Duration: 11 Jun 2019 → 14 Jun 2019 https://icwe2019.webengineering.org/ |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11496 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 19th International Conference on Web Engineering |
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Abbreviated title | ICWE 2019 |
Country/Territory | Korea, Republic of |
Period | 11/06/19 → 14/06/19 |
Internet address |
Keywords
- Convolutional Neural Network
- Rumor detection
- Social media