Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Discourse-aware rumour stance classification in social media using sequential classifiers

Tools
- Tools
+ Tools

Zubiaga, Arkaitz, Kochkina, Elena, Liakata, Maria, Procter, Rob, Lukasik, Michal, Bontcheva, Kalina, Cohn, Trevor and Augenstein, Isabelle (2018) Discourse-aware rumour stance classification in social media using sequential classifiers. Information Processing & Management, 54 (2). pp. 273-290. doi:10.1016/j.ipm.2017.11.009 ISSN 0306-4573.

[img]
Preview
PDF
WRAP-discourse-aware-rumour-stance-social-sequential-Procter-2017.pdf - Accepted Version - Requires a PDF viewer.

Download (1139Kb) | Preview
Official URL: http://doi.org/10.1016/j.ipm.2017.11.009

Request Changes to record.

Abstract

Rumour stance classification, defined as classifying the stance of specific social media posts into one of supporting, denying, querying or commenting on an earlier post, is becoming of increasing interest to researchers. While most previous work has focused on using individual tweets as classifier inputs, here we report on the performance of sequential classifiers that exploit the discourse features inherent in social media interactions or 'conversational threads'. Testing the effectiveness of four sequential classifiers -- Hawkes Processes, Linear-Chain Conditional Random Fields (Linear CRF), Tree-Structured Conditional Random Fields (Tree CRF) and Long Short Term Memory networks (LSTM) -- on eight datasets associated with breaking news stories, and looking at different types of local and contextual features, our work sheds new light on the development of accurate stance classifiers. We show that sequential classifiers that exploit the use of discourse properties in social media conversations while using only local features, outperform non-sequential classifiers. Furthermore, we show that LSTM using a reduced set of features can outperform the other sequential classifiers; this performance is consistent across datasets and across types of stances. To conclude, our work also analyses the different features under study, identifying those that best help characterise and distinguish between stances, such as supporting tweets being more likely to be accompanied by evidence than denying tweets. We also set forth a number of directions for future research.

Item Type: Journal Article
Subjects: H Social Sciences > HM Sociology
Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Rumor in mass media, Online social networks -- Social aspects, Social media, Data mining, Rumor, Natural language processing (Computer science)
Journal or Publication Title: Information Processing & Management
Publisher: Elsevier
ISSN: 0306-4573
Official Date: March 2018
Dates:
DateEvent
March 2018Published
6 December 2017Available
28 November 2017Accepted
Volume: 54
Number: 2
Page Range: pp. 273-290
DOI: 10.1016/j.ipm.2017.11.009
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 7 December 2017
Date of first compliant Open Access: 6 June 2019
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
PHEME - Grant No. 611233FP7 Information and Communication Technologieshttp://dx.doi.org/10.13039/100011273
EP/I004327/1Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
UNSPECIFIEDElsevier Foundationhttp://dx.doi.org/10.13039/100005201
EP/N510129/1Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us