Aker, A., Derczynski, L. and Bontcheva, K. (2017) Simple open stance classification for rumour analysis. In: Mitkov, R. and Angelova, G., (eds.) Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017. Recent Advances in Natural Language Processing, RANLP 2017, 04-06 Sep 2017, Varna, Bulgaria. INCOMA Ltd. , pp. 31-39.
Abstract
Stance classification determines the attitude, or stance, in a (typically short) text. The task has powerful applications, such as the detection of fake news or the automatic extraction of attitudes toward entities or events in the media. This paper describes a surprisingly simple and efficient classification approach to open stance classification in Twitter, for rumour and veracity classification. The approach profits from a novel set of automatically identifiable problem-specific features, which significantly boost classifier accuracy and achieve above state-of-the-art results on recent benchmark datasets. This calls into question the value of using complex sophisticated models for stance classification without first doing informed feature extraction.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2017 ACL. Available under the terms of the Creative Commons Attribution Licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Computation and Language |
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) |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - FP6/FP7 PHEME - 611233 ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC) EP/I004327/1 EUROPEAN COMMISSION - HORIZON 2020 COMRADES - 687847 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 04 Sep 2017 08:32 |
Last Modified: | 13 Jul 2020 16:37 |
Published Version: | https://www.aclweb.org/anthology/R17-1005/ |
Status: | Published |
Publisher: | INCOMA Ltd. |
Refereed: | Yes |
Identification Number: | 10.26615/978-954-452-049-6_005 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:120759 |