Stance Detection in Web and Social Media: A Comparative Study
- URL: http://arxiv.org/abs/2007.05976v1
- Date: Sun, 12 Jul 2020 12:39:35 GMT
- Title: Stance Detection in Web and Social Media: A Comparative Study
- Authors: Shalmoli Ghosh, Prajwal Singhania, Siddharth Singh, Koustav Rudra,
Saptarshi Ghosh
- Abstract 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, and their comparative performances.
- Score: 3.937145867005019
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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. Implementations of all algorithms discussed in
this paper are available at
https://github.com/prajwal1210/Stance-Detection-in-Web-and-Social-Media.
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