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Assessing technology legitimacy with topic models and sentiment analysis – The case of wind power in Germany

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  • Dehler-Holland, Joris
  • Okoh, Marvin
  • Keles, Dogan

Abstract

Legitimacy is a crucial factor determining the success of technologies in the early stages of development and for maintaining resource flows as well as public and political support across the technology life cycle. In sustainability transitions that unfold over long periods of time, the maintenance of legitimacy of technologies identified as vital for sustainability becomes a key challenge. In the energy sector, wind power contributes to the transition to an energy system with low greenhouse gas emissions. In Germany, wind power recently faced a series of lawsuits and decreasing investment activity. Therefore, we assess the legitimacy of wind power in Germany by analyzing newspaper articles from four national newspapers from 2009 to 2018. A large amount of articles motivates the use of topic models, sentiment analysis, and statistics to shed light on the changing alignment of wind power with its context. The results show that various issues temporarily gain prominence on the agenda. Lately, the legitimacy of wind power in Germany has been increasingly challenged by adverse effects on humans, animals, and landscapes. Policymakers and project developers may address aspects of pragmatic legitimacy, such as civic participation and the local distribution of profits.

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  • Dehler-Holland, Joris & Okoh, Marvin & Keles, Dogan, 2022. "Assessing technology legitimacy with topic models and sentiment analysis – The case of wind power in Germany," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    • Handle: RePEc:eee:tefoso:v:175:y:2022:i:c:s004016252100785x
    DOI: 10.1016/j.techfore.2021.121354
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    5. Myriam Caratù & Valerio Brescia & Ilaria Pigliautile & Paolo Biancone, 2023. "Assessing Energy Communities’ Awareness on Social Media with a Content and Sentiment Analysis," Sustainability, MDPI, vol. 15(8), pages 1-28, April.
    6. Xu, Qianwen Ariel & Jayne, Chrisina & Chang, Victor, 2024. "An emoji feature-incorporated multi-view deep learning for explainable sentiment classification of social media reviews," Technological Forecasting and Social Change, Elsevier, vol. 202(C).

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