Stance Detection in Web and Social Media: A Comparative Study

Online forums and social media platforms are increasingly being used to discuss topics of varying polarities where different people take different stances. Several methodologies for automatic stance detection from text have been proposed in literature. To our knowledge, there has not been any system...

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Bibliographic Details
Published inExperimental IR Meets Multilinguality, Multimodality, and Interaction Vol. 11696; pp. 75 - 87
Main Authors Ghosh, Shalmoli, Singhania, Prajwal, Singh, Siddharth, Rudra, Koustav, Ghosh, Saptarshi
Format Book Chapter
LanguageEnglish
Published Switzerland Springer International Publishing AG 2019
Springer International Publishing
SeriesLecture Notes in Computer Science
Online AccessGet full text
ISBN9783030285760
3030285766
ISSN0302-9743
1611-3349
DOI10.1007/978-3-030-28577-7_4

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Summary:Online forums and social media platforms are increasingly being used to discuss topics of varying polarities where different people take different stances. Several methodologies for automatic stance detection from text have been proposed in literature. To our knowledge, there has not been any systematic investigation towards their reproducibility, and their comparative performances. In this work, we explore the reproducibility of several existing stance detection models, including both neural models and classical classifier-based models. Through experiments on two datasets – (i) the popular SemEval microblog dataset, and (ii) a set of health-related online news articles – we also perform a detailed comparative analysis of various methods and explore their shortcomings.
Bibliography:S. Ghosh, P. Singhania and S. Singh—Equal contribution by authors. K. Rudra—The work was done when the author was a Research Associate at IIT Kharagpur.
ISBN:9783030285760
3030285766
ISSN:0302-9743
1611-3349
DOI:10.1007/978-3-030-28577-7_4