Stance Classification of Social Media Texts for Under-Resourced Scenarios in Social Sciences

Victoria Yantseva () and Kostiantyn Kucher
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Victoria Yantseva: Infolab, Department of Information Technology, Uppsala University, 751 05 Uppsala, Sweden
Kostiantyn Kucher: Department of Computer Science and Media Technology, Linnaeus University, 351 95 Växjö, Sweden

Data, 2022, vol. 7, issue 11, 1-19

Abstract: In this work, we explore the performance of supervised stance classification methods for social media texts in under-resourced languages and using limited amounts of labeled data. In particular, we focus specifically on the possibilities and limitations of the application of classic machine learning versus deep learning in social sciences. To achieve this goal, we use a training dataset of 5.7K messages posted on Flashback Forum, a Swedish discussion platform, further supplemented with the previously published ABSAbank-Imm annotated dataset, and evaluate the performance of various model parameters and configurations to achieve the best training results given the character of the data. Our experiments indicate that classic machine learning models achieve results that are on par or even outperform those of neural networks and, thus, could be given priority when considering machine learning approaches for similar knowledge domains, tasks, and data. At the same time, the modern pre-trained language models provide useful and convenient pipelines for obtaining vectorized data representations that can be combined with classic machine learning algorithms. We discuss the implications of their use in such scenarios and outline the directions for further research.

Keywords: text mining; machine learning; deep learning; neural networks; stance classification; computational social science; social media; supervised learning; sentiment classification; Swedish language data (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2022
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