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.
Recommended Citation
Schirrmeister, Till and Goerlich, Lina, "Counteracting Attacks on Science with Social Sentiment Analysis: A Comparison of Approaches for Custom Social Sentiment Analysis Tool" (2024). Wirtschaftsinformatik 2024 Proceedings. 121.
https://aisel.aisnet.org/wi2024/121