Skip to main content

A semi-supervised approach to message stance classification

Giasemidis, Georgios, Kaplis, Nikolaos, Agrafiotis, Ioannis, Nurse, Jason R. C. (2018) A semi-supervised approach to message stance classification. IEEE Transactions on Knowledge and Data Engineering, 32 (1). pp. 1-11. ISSN 1041-4347. E-ISSN 1558-2191. (doi:10.1109/TKDE.2018.2880192) (KAR id:69952)

PDF Author's Accepted Manuscript
Language: English
Download (886kB) Preview
[thumbnail of tkde-gkan-camera-ready.pdf]
This file may not be suitable for users of assistive technology.
Request an accessible format
Official URL:


Social media communications are becoming increasingly prevalent; some useful, some false, whether unwittingly or maliciously. An increasing number of rumours daily flood the social networks. Determining their veracity in an autonomous way is a very active and challenging field of research, with a variety of methods proposed. However, most of the models rely on determining the constituent messages’ stance towards the rumour, a feature known as the “wisdom of the crowd”. Although several supervised machine-learning approaches have been proposed to tackle the message stance classification problem, these have numerous shortcomings. In this paper we argue that semi-supervised learning is more effective than supervised models and use two graph-based methods to demonstrate it. This is not only in terms of classification accuracy, but equally important, in terms of speed and scalability. We use the Label Propagation and Label Spreading algorithms and run experiments on a dataset of 72 rumours and hundreds of thousands messages collected from Twitter. We compare our results on two available datasets to the state-of-the-art to demonstrate our algorithms’ performance regarding accuracy, speed and scalability for real-time applications.

Item Type: Article
DOI/Identification number: 10.1109/TKDE.2018.2880192
Uncontrolled keywords: message stance, Twitter, rumours, semi-supervised, label propagation, label spreading
Subjects: Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics
T Technology > T Technology (General)
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Divisions > Division for the Study of Law, Society and Social Justice > School of Social Policy, Sociology and Social Research
Depositing User: Jason Nurse
Date Deposited: 06 Nov 2018 11:46 UTC
Last Modified: 19 Nov 2022 22:45 UTC
Resource URI: (The current URI for this page, for reference purposes)
Nurse, Jason R. C.:
  • Depositors only (login required):


Downloads per month over past year