Aker, A., Zubiaga, A., Bontcheva, K. orcid.org/0000-0001-6152-9600 et al. (3 more authors) (2017) Stance classification in out-of-domain rumours: a case study around mental health disorders. In: Ciampaglia, G.L., Mashhadi, A.J. and Yasseri, T., (eds.) Social Informatics. 9th International Conference, SocInfo 2017, 13-15 Sep 2017, Oxford, UK. Lecture Notes in Computer Science, 10540 . Springer International Publishing , pp. 53-64. ISBN 9783319672557
Abstract
Social media being a prolific source of rumours, stance classification of individual posts towards rumours has gained attention in the past few years. Classification of stance in individual posts can then be useful to determine the veracity of a rumour. Research in this direction has looked at rumours in different domains, such as politics, natural disasters or terrorist attacks. However, work has been limited to in-domain experiments, i.e. training and testing data belong to the same domain. This presents the caveat that when one wants to deal with rumours in domains that are more obscure, training data tends to be scarce. This is the case of mental health disorders, which we explore here. Having annotated collections of tweets around rumours emerged in the context of breaking news, we study the performance stability when switching to the new domain of mental health disorders. Our study confirms that performance drops when we apply our trained model on a new domain, emphasising the differences in rumours across domains. We overcome this issue by using a little portion of the target domain data for training, which leads to a substantial boost in performance. We also release the new dataset with mental health rumours annotated for stance.
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 Springer International Publishing AG |
Keywords: | Social media; Stance classification; Veracity; Rumours; Mental health |
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 EUROPEAN COMMISSION - HORIZON 2020 654024 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 14 Feb 2025 14:49 |
Last Modified: | 14 Feb 2025 14:49 |
Status: | Published |
Publisher: | Springer International Publishing |
Series Name: | Lecture Notes in Computer Science |
Refereed: | Yes |
Identification Number: | 10.1007/978-3-319-67256-4_6 |
Sustainable Development Goals: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:223253 |