Lukasik, M., Bontcheva, K., Cohn, T. et al. (3 more authors) (2016) Using Gaussian Processes for Rumour Stance Classification in Social Media. arXiv. (Unpublished)
Abstract
Social media tend to be rife with rumours while new reports are released piecemeal during breaking news. Interestingly, one can mine multiple reactions expressed by social media users in those situations, exploring their stance towards rumours, ultimately enabling the flagging of highly disputed rumours as being potentially false. In this work, we set out to develop an automated, supervised classifier that uses multi-task learning to classify the stance expressed in each individual tweet in a rumourous conversation as either supporting, denying or questioning the rumour. Using a classifier based on Gaussian Processes, and exploring its effectiveness on two datasets with very different characteristics and varying distributions of stances, we show that our approach consistently outperforms competitive baseline classifiers. Our classifier is especially effective in estimating the distribution of different types of stance associated with a given rumour, which we set forth as a desired characteristic for a rumour-tracking system that will warn both ordinary users of Twitter and professional news practitioners when a rumour is being rebutted.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 The Author(s). Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | cs.CL; cs.CL; cs.IR; cs.SI |
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 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 06 Apr 2017 15:52 |
Last Modified: | 05 Mar 2019 16:29 |
Published Version: | https://arxiv.org/abs/1609.01962 |
Status: | Unpublished |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:110908 |