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

In: People, Society, and Ethical Challenges of Information Systems

Author

Listed:
  • Till Schirrmeister

    (University Potsdam)

  • Lina Goerlich

    (University Potsdam)

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.

Suggested Citation

  • Till Schirrmeister & Lina Goerlich, 2026. "Counteracting Attacks on Science with Social Sentiment Analysis: A Comparison of Approaches for Custom Social Sentiment Analysis Tool," Lecture Notes in Information Systems and Organization, in: Christoph M. Flath & Gunther Gust & Frédéric Thiesse & Axel Winkelmann (ed.), People, Society, and Ethical Challenges of Information Systems, pages 3-11, Springer.
    • Handle: RePEc:spr:lnichp:978-3-032-08486-6_1
    DOI: 10.1007/978-3-032-08486-6_1
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