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Abstract
Rumour detection on Twitter is an important problem. Existing studies mainly focus on high detection accuracy, which often requires large volumes of data on contents, source credibility or propagation. In this paper we focus on early detection of rumours when data for information sources or propagation is scarce. We observe that tweets attract immediate comments from the public who often express uncertain and questioning attitudes towards rumour tweets. We therefore propose to learn user attitude distribution for Twitter posts from their comments, and then combine it with content analysis for early detection of rumours. Specifically we propose convolutional neural network (CNN) CNN and BERT neural network language models to learn attitude representation for user comments without human annotation via transfer learning based on external data sources for stance classification. We further propose CNN-BiLSTM- and BERT-based deep neural models to combine attitude representation and content representation for early rumour detection. Experiments on real-world rumour datasets show that our BERT-based model can achieve effective early rumour detection and significantly outperform start-of-the-art rumour detection models.
Original language | English |
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Title of host publication | Advances in Information Retrieval |
Subtitle of host publication | 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14–17, 2020, Proceedings, Part I |
Editors | Joemon M. Jose, Emine Yilmaz, João Magalhães, Pablo Castells, Nicola Ferro, Mário J. Silva, Flávio Martins |
Place of Publication | Cham, Switzerland |
Publisher | Springer, Springer Nature |
Pages | 575-588 |
Number of pages | 14 |
ISBN (Electronic) | 9783030454395 |
ISBN (Print) | 9783030454388 |
DOIs | |
Publication status | Published - 2020 |
Event | 42nd European Conference on IR Research, ECIR 2020 - Lisbon, Portugal Duration: 14 Apr 2020 → 17 Apr 2020 |
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 | 12035 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 42nd European Conference on IR Research, ECIR 2020 |
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Country/Territory | Portugal |
City | Lisbon |
Period | 14/04/20 → 17/04/20 |
Keywords
- BERT
- CNN
- Rumour detection
- Stance detection
- Transfer learning
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- 1 Finished
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Combating Fake News on Social Media: From Early Detection to Intervention
Zhang, X., Wang, Y. & Liu, H.
1/09/20 → 31/08/23
Project: Research