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2019 | OriginalPaper | Chapter

Stance Detection in Web and Social Media: A Comparative Study

Authors : Shalmoli Ghosh, Prajwal Singhania, Siddharth Singh, Koustav Rudra, Saptarshi Ghosh

Published in: Experimental IR Meets Multilinguality, Multimodality, and Interaction

Publisher: Springer International Publishing

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Abstract

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.

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Title: Stance Detection in Web and Social Media: A Comparative Study
Authors: Shalmoli Ghosh
Prajwal Singhania
Siddharth Singh
Koustav Rudra
Saptarshi Ghosh
Publisher: Springer International Publishing
Book: Experimental IR Meets Multilinguality, Multimodality, and Interaction
Print ISBN: 978-3-030-28576-0

Electronic ISBN: 978-3-030-28577-7

Copyright Year: 2019
DOI: https://doi.org/10.1007/978-3-030-28577-7_4

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