From hype to hesitation: analyzing themes and sentiment shifts regarding emerging technologies on Reddit
How does the public perceive emerging technologies over time? This study analyzes 107,370 comments from the r/Futurology community (2021–2025), combining BERTopic modelling, thematic analysis, and RoBERTa-based sentiment analysis to trace themes and emotional responses across eleven higher-level categories and time. Negative sentiment exceeds positive sentiment across all categories and years in the unweighted comment texts, although neutral sentiment is often the largest class. Several categories show temporary reductions in negativity followed by later increases. At the same time, categories centred on unresolved dilemmas, including ‘Ethical Implications’, ‘Governance and Regulation’, and ‘Geopolitics and Economics’, remained persistently negative, reflecting concerns about privacy, bias, regulation, and geopolitical risk. Thematic and sentiment shifts are interpreted in the context of external developments, including the release of ChatGPT, the COVID-19 pandemic, and the invasion of Ukraine, without establishing their causal effects. Drawing on Social Representation Theory, we interpret Reddit as a platform where dispersed public discourse coalesces into shared representations that influence technology adoption and governance. By tracking sentiment trajectories and thematic shifts, our study deepens understanding of socio-technical dynamics. It offers practical implications for policymakers and innovators seeking to align emerging technologies with public expectations.
Authors
- Katharina Hölzle (ORCID: https://orcid.org/0000-0002-9733-4650)
- Anne Radunski (ORCID: https://orcid.org/0000-0002-1847-4410)
- Robert Rose (ORCID: https://orcid.org/0000-0002-1517-157X)
- Valeska Liebner
Institutions
- University of Stuttgart (DE)
- University of Potsdam (DE)
- Fraunhofer Institute for Industrial Engineering (DE)
- Fraunhofer-Gesellschaft (DE)
Publication Details
- Journal
- Behaviour and Information Technology
- Published
- 2026-10-19
- DOI
- https://doi.org/10.1080/0144929x.2026.2741400
- Primary Topic
- Sentiment Analysis and Opinion Mining
- Type
- article
- Field-Weighted Citation Impact
- 0.00