Lukasik, M., Cohn, T. and Bontcheva, K. (2015) Classifying Tweet Level Judgements of Rumours in Social Media. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 2015 Conference on Empirical Methods in Natural Language Processing, 17-21 Sep 2015, Lisbon, Portugal. , pp. 2590-2595. ISBN 978-1-941643-32-7
Abstract
Social media is a rich source of rumours and corresponding community reactions. Rumours reflect different characteristics, some shared and some individual. We formulate the problem of classifying tweet level judgements of rumours as a supervised learning task. Both supervised and unsupervised domain adaptation are considered, in which tweets from a rumour are classified on the basis of other annotated rumours. We demonstrate how multi-task learning helps achieve good results on rumours from the 2011 England riots.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 The Association for Computational Linguistics. |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 09 Jun 2017 15:11 |
Last Modified: | 19 Dec 2022 13:35 |
Status: | Published |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:110910 |