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

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 prop...

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Detalles Bibliográficos
Autores principales: Alturayeif, Nora, Luqman, Hamzah, Ahmed, Moataz
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer London 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9884072/
https://www.ncbi.nlm.nih.gov/pubmed/36743664
http://dx.doi.org/10.1007/s00521-023-08285-7
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author Alturayeif, Nora
Luqman, Hamzah
Ahmed, Moataz
author_facet Alturayeif, Nora
Luqman, Hamzah
Ahmed, Moataz
author_sort Alturayeif, Nora
collection PubMed
description 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.
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spelling pubmed-98840722023-01-30 A systematic review of machine learning techniques for stance detection and its applications Alturayeif, Nora Luqman, Hamzah Ahmed, Moataz Neural Comput Appl Review 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. Springer London 2023-01-28 2023 /pmc/articles/PMC9884072/ /pubmed/36743664 http://dx.doi.org/10.1007/s00521-023-08285-7 Text en © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Review
Alturayeif, Nora
Luqman, Hamzah
Ahmed, Moataz
A systematic review of machine learning techniques for stance detection and its applications
title A systematic review of machine learning techniques for stance detection and its applications
title_full A systematic review of machine learning techniques for stance detection and its applications
title_fullStr A systematic review of machine learning techniques for stance detection and its applications
title_full_unstemmed A systematic review of machine learning techniques for stance detection and its applications
title_short A systematic review of machine learning techniques for stance detection and its applications
title_sort systematic review of machine learning techniques for stance detection and its applications
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9884072/
https://www.ncbi.nlm.nih.gov/pubmed/36743664
http://dx.doi.org/10.1007/s00521-023-08285-7
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