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Gaussian processes for rumour stance classification in social media

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Lukasik, M., Bontcheva, K., Cohen, Trevor, Zubiaga, Arkaitz, Liakata, Maria and Procter, Rob (2019) Gaussian processes for rumour stance classification in social media. ACM Transactions on Information Systems, 37 (2). 20. doi:10.1145/3295823

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Official URL: https://doi.org/10.1145/3295823

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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 conversation around a rumour as either supporting, denying or questioning the rumour. Using a Gaussian Process classifier, 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 show both ordinary users of Twitter and professional news practitioners how others orient to the disputed veracity of a rumour, with the final aim of establishing its actual truth value.

Item Type: Journal Article
Divisions: Faculty of Science > Computer Science
Journal or Publication Title: ACM Transactions on Information Systems
Publisher: ACM
ISSN: 1046-8188
Official Date: 20 February 2019
Dates:
DateEvent
20 February 2019Published
23 November 2018Accepted
Volume: 37
Number: 2
Article Number: 20
DOI: 10.1145/3295823
Status: Peer Reviewed
Publication Status: Published
Publisher Statement: "© ACM, 2019. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in ACM Transactions on Information Systems, 37(7), 2019 http://doi.acm.org/10.1145/3295823
Access rights to Published version: Restricted or Subscription Access
Copyright Holders: © 2019 Copyright held by the owner/author(s).
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
611233FP7 Fusion Energy Researchhttp://dx.doi.org/10.13039/100011270
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