The Library
Gaussian processes for rumour stance classification in social media
Tools
Tools
Tools
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
![[img]](http://wrap.warwick.ac.uk/style/images/fileicons/application_pdf.png) |
PDF
WRAP-Gaussian-processes-rumour-stance-classification-Procter-2019.pdf - Published Version Embargoed item. Restricted access to Repository staff only - Requires a PDF viewer. Download (2802Kb) |
Official URL: https://doi.org/10.1145/3295823
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: |
|
||||||
| 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: |
|
||||||
| Open Access Version: |
Request changes or add full text files to a record
Repository staff actions (login required)
 |
View Item |
