Counteracting Attacks on Science with Social Sentiment Analysis: A Comparison of Approaches for Custom Social Sentiment Analysis Tool

Till Schirrmeister () and Lina Goerlich ()
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Till Schirrmeister: University Potsdam
Lina Goerlich: University Potsdam

A chapter in People, Society, and Ethical Challenges of Information Systems, 2026, pp 3-11 from Springer

Abstract: Abstract Democracy-harming forces in online social networks (OSNs) attack the credibility of scientists aiming to hinder the spread of scientific knowledge. Current sentiment analysis tools are to a large extent inadequate for effectively monitoring attacks on scientists, highlighting the need for custom tools. Our study addresses this by exploring the best techniques for a custom sentiment analysis tool. We manually coded a dataset of tweets appreciating or criticizing scientists during the COVID-19 pandemic and evaluated various supervised machine learning algorithms, ensemble techniques, and zero-shot classification methods. Our findings indicate that stacking is the most effective method for training a custom sentiment analysis tool, while zero-shot classification is unsuitable. These results provide insights for researchers and practitioners to improve their monitoring tools, encouraging scientists to share their knowledge.

Keywords: Sentiment analysis; Digital democracy; Online social networks (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-08486-6_1

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