A systematic review of machine learning techniques for stance detection and its applications

Nora Alturayeif, Hamzah Luqman*, Moataz Ahmed

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

6 Scopus citations

Abstract

Stance detection is an evolving opinion mining research area motivated by the vast increase in the variety and volume of user-generated content. In this regard, considerable research has been recently carried out in the area of stance detection. In this study, we review the different techniques proposed in the literature for stance detection as well as other applications such as rumor veracity detection. Particularly, we conducted a systematic literature review of empirical research on the machine learning (ML) models for stance detection that were published from January 2015 to October 2022. We analyzed 96 primary studies, which spanned eight categories of ML techniques. In this paper, we categorize the analyzed studies according to a taxonomy of six dimensions: approaches, target dependency, applications, modeling, language, and resources. We further classify and analyze the corresponding techniques from each dimension’s perspective and highlight their strengths and weaknesses. The analysis reveals that deep learning models that adopt a mechanism of self-attention have been used more frequently than the other approaches. It is worth noting that emerging ML techniques such as few-shot learning and multitask learning have been used extensively for stance detection. A major conclusion of our analysis is that despite that ML models have shown to be promising in this field, the application of these models in the real world is still limited. Our analysis lists challenges and gaps to be addressed in future research. Furthermore, the taxonomy presented can assist researchers in developing and positioning new techniques for stance detection-related applications.

Original languageEnglish
Pages (from-to)5113-5144
Number of pages32
JournalNeural Computing and Applications
Volume35
Issue number7
DOIs
StatePublished - Mar 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

Keywords

  • Machine learning
  • PRISMA
  • Rumor detection
  • Sentiment analysis
  • Stance classification
  • Stance detection

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'A systematic review of machine learning techniques for stance detection and its applications'. Together they form a unique fingerprint.

Cite this