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

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.

Developing a Natural Language Understanding Model to Characterize Cable News Bias

“…However, despite the benefits of sentiment and the recent research into computational tools for sentiment classification, sentiment alone has limited utility in understanding more contextual attitudes and opinions, like stance. Stance detection entails the automated prediction of an author's viewpoint or stance towards a subject of interest, often referred to as the "target" [31]. Typically, a stance towards a subject is categorized as "Agree", "Disagree", or "Neutral".…”

Enhancing Stance Detection through Sequential Weighted Multi-task Learning

“…According to the identified gap in a recent Systematic Literature Review on stance detection by [2], further exploration is required in the field to investigate the potential of developing a joint neural architecture based on the MTL paradigm. In addition, hypotheses regarding the interaction between sentiment and stance appear to be debatable.…”

“…Despite its recent emergence, there has been a noteworthy endeavor to construct models specifically tailored for tackling stance detection [2]. Past studies on stance detection utilized feature engineering with a support vector machine (SVM) classifier [15,[20][21][22][23], gradient boosting [24], and k-nearest neighbors (KNN) [25].…”

“…This unprecedented surge in online discourse has sparked an urgent need to develop automated tools and techniques capable of effectively analyzing the opinions and attitudes expressed within these expansive streams of text. Stance detection, a critical task within the field of Natural Language Processing (NLP), aims to identify the position or perspective of a writer towards a specific topic or entity by analyzing their written text and/or social media activity, such as preferences and connections [1,2]. The applications of stance detection are diverse and encompass domains such as politics, marketing, and social media analysis.…”

Use of large language models as a scalable approach to understanding public health discourse

“…A systematic review performed in 2019 on existing opinion mining approaches using data from social media platforms found that 94% of the 461 included studies use data only from one social media platform and the vast majority of the studies used data from X (formerly known as Twitter), followed by Sina Weibo and Facebook [12]. Furthermore, in another systematic review of techniques for stance detection published in 2023 [13], social media was the data source of 72% of the 96 included articles in the study.…”