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

Analysing how people orient to and spread rumours in social media by looking at conversational threads

Tools
- Tools
+ Tools

Zubiaga, Arkaitz, Liakata, Maria, Procter, Rob, Wong Sak Hoi, Geraldine and Tolmie, Peter (2016) Analysing how people orient to and spread rumours in social media by looking at conversational threads. PLoS One, 11 (3). pp. 1-29. doi:10.1371/journal.pone.0150989 ISSN 1932-6203.

[img]
Preview
PDF
WRAP_journal.pone.0150989.PDF - Published Version - Requires a PDF viewer.
Available under License Creative Commons Attribution 4.0.

Download (9Mb) | Preview
Official URL: http://dx.doi.org/10.1371%2Fjournal.pone.0150989

Request Changes to record.

Abstract

As breaking news unfolds people increasingly rely on social media to stay abreast of the latest updates. The use of social media in such situations comes with the caveat that new information being released piecemeal may encourage rumours, many of which remain unverified long after their point of release. Little is known, however, about the dynamics of the life cycle of a social media rumour. In this paper we present a methodology that has enabled us to collect, identify and annotate a dataset of 330 rumour threads (4,842 tweets) associated with 9 newsworthy events. We analyse this dataset to understand how users spread, support, or deny rumours that are later proven true or false, by distinguishing two levels of status in a rumour life cycle i.e., before and after its veracity status is resolved. The identification of rumours associated with each event, as well as the tweet that resolved each rumour as true or false, was performed by journalist members of the research team who tracked the events in real time. Our study shows that rumours that are ultimately proven true tend to be resolved faster than those that turn out to be false. Whilst one can readily see users denying rumours once they have been debunked, users appear to be less capable of distinguishing true from false rumours when their veracity remains in question. In fact, we show that the prevalent tendency for users is to support every unverified rumour. We also analyse the role of different types of users, finding that highly reputable users such as news organisations endeavour to post well-grounded statements, which appear to be certain and accompanied by evidence. Nevertheless, these often prove to be unverified pieces of information that give rise to false rumours. Our study reinforces the need for developing robust machine learning techniques that can provide assistance in real time for assessing the veracity of rumours. The findings of our study provide useful insights for achieving this aim.</p>

Item Type: Journal Article
Subjects: H Social Sciences > HM Sociology
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Online social networks, Rumor in mass media
Journal or Publication Title: PLoS One
Publisher: Public Library of Science
ISSN: 1932-6203
Official Date: 4 March 2016
Dates:
DateEvent
4 March 2016Published
21 February 2016Accepted
16 November 2015Submitted
Volume: 11
Number: 3
Number of Pages: 29
Page Range: pp. 1-29
DOI: 10.1371/journal.pone.0150989
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access (Creative Commons)
Date of first compliant deposit: 14 March 2016
Date of first compliant Open Access: 14 March 2016
Funder: Seventh Framework Programme (European Commission) (FP7)
Grant number: 611233 (FP7)

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