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

Rumour stance and veracity classification in social media conversations

Tools
- Tools
+ Tools

Kochkina, Elena (2019) Rumour stance and veracity classification in social media conversations. PhD thesis, University of Warwick.

[img]
Preview
PDF
WRAP_Theses_Kochkina_2019.pdf - Submitted Version - Requires a PDF viewer.

Download (11Mb) | Preview
Official URL: http://webcat.warwick.ac.uk/record=b3494379~S15

Request Changes to record.

Abstract

Social media platforms are popular as sources of news, often delivering updates faster than traditional news outlets. The absence of verification of the posted information leads to wide proliferation of misinformation. The effects of propagation of such false information can have far-reaching consequences on society. Traditional manual verification by fact-checking professionals is not scalable to the amount of misinformation being spread. Therefore there is a need for an automated verification tool that would assist the process of rumour resolution. In this thesis we address the problem of rumour verification in social media conversations from a machine learning perspective.

Rumours that attract a lot of scepticism in the form of questions and denials among the responses are more likely to be proven false later (Zhao et al., 2015). Thus we explore how crowd wisdom in the form of the stance of responses towards a rumour can contribute to an automated rumour verification system. We study the ways of determining the stance of each response in a conversation automatically. We focus on the importance of incorporating conversation structure into stance classification models and also identifying characteristics of supporting, denying, questioning and commenting posts. We follow by proposing several models for rumour veracity classification that incorporate different feature sets, including the stance of the responses, attempting to find the set that would lead to the most accurate models across several datasets. We view the rumour resolution process as a sequence of tasks: rumour detection, tracking, stance classification and, finally, rumour verification. We then study relations between the tasks in the rumour verification pipeline through a joint learning approach, showing its benefits comparing to single-task learning. Finally, we address the issue of transparency of model decisions by incorporating uncertainty estimation methods into rumour verification models. We then conclude and point directions for future research.

Item Type: Thesis or Dissertation (PhD)
Subjects: B Philosophy. Psychology. Religion > BC Logic
H Social Sciences > HM Sociology
Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software
Library of Congress Subject Headings (LCSH): Social media, Rumor, Truth, Verification (Logic)
Official Date: 2019
Dates:
DateEvent
2019UNSPECIFIED
Institution: University of Warwick
Theses Department: Department of Computer Science
Thesis Type: PhD
Publication Status: Unpublished
Supervisor(s)/Advisor: Liakata, Maria
Sponsors: Bridges Programmes ; University of Warwick. Department of Computer Science
Format of File: pdf
Extent: xiii, 201 leaves : charts, illustrations
Language: eng

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