Stance Detection in Web and Social Media: A Comparative Study
This work provides a systematic comparative analysis for researchers in NLP and social media analysis, but it is incremental as it focuses on evaluating existing methods rather than introducing new ones.
The authors tackled the problem of comparing and reproducing existing stance detection models from text, finding that neural models generally outperform classical classifiers on two datasets, with specific accuracy improvements of up to 5% on the SemEval microblog dataset.
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.