Applying Self-attention for Stance Classification

Bugueño, Margarita Constanza; Mendoza, Marcelo

doi:10.1007/978-3-030-33904-3_5

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Imfd Aims To Be a Reference Center In Latin America Related To State-ofthe-art Research On The Foundational Problems With Data A1

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Cited by 4 publications

(1 citation statement)

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“…In the work of this project, early detection of users' stances, harassment detection, early fake news detection, and the study of polarization dynamics in social media occupy central roles. 17,18 Development of robust information structures. This project focuses on two prominent information disorders; that is, problems that work against the development of robust information structures.…”

Section: Imfd Aims To Be a Reference Center In Latin America Related To State-ofthe-art Research On The Foundational Problems With Data Amentioning

confidence: 99%

Chile's new interdisciplinary institute for foundational research on data

Arenas

Barceló

2020

Commun. ACM

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Section: Imfd Aims To Be a Reference Center In Latin America Related To State-ofthe-art Research On The Foundational Problems With Data Amentioning

confidence: 99%

Chile's new interdisciplinary institute for foundational research on data

Arenas

Barceló

2020

Commun. ACM

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Using Deep Learning to Detect Rumors in Twitter

Providel

Mendoza

2020

Social Computing and Social Media. Design, Ethics, User Behavior, and Social Network Analysis

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

Alturayeif

Luqman

Ahmed

2023

Neural Comput & Applic

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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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Applying Self-attention for Stance Classification

Cited by 4 publications

References 11 publications

Chile's new interdisciplinary institute for foundational research on data

Chile's new interdisciplinary institute for foundational research on data

Using Deep Learning to Detect Rumors in Twitter

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

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