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arXiv · 2304.02388

Machine Learning of Public Sentiments toward Wind Energy in Norway

Oskar Vågerö·Anders Bråte·Alexandra Wittemann·Jessica Yarin Robinson·Natalia Sirotko-Sibirskaya·Marianne Zeyringer
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Abstract

Across Europe negative public opinion has and may continue to limit the deployment of renewable energy infrastructure required for the transition to net-zero energy systems. Understanding public sentiment and its spatio-temporal variations is as such important for decision-making and socially accepted energy systems. In this study, we apply a sentiment classification model based on a machine learning framework for natural language processing, NorBERT, on data collected from Twitter between 2006 and 2022 to analyse the case of wind power opposition in Norway. From the 68828 tweets with geospatial information, we show how discussions about wind power intensified in 2018/2019 together with a trend of more negative tweets up until 2020, both on a regional level and for Norway as a whole. Furthermore, we find weak geographical clustering in our data, indicating that discussions are country wide and not dominated by specific regional events or developments. Twitter data allows for detailed insight into the temporal nature of public sentiments and extending this research to additional case studies of technologies, countries and sources of data (e.g. newspapers, other social media) may prove important to complement traditional survey research and the understanding of public sentiment.

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Oskar Vågerö, Anders Bråte, Alexandra Wittemann, Jessica Yarin Robinson, Natalia Sirotko-Sibirskaya, Marianne Zeyringer. 2023-04-05. Machine Learning of Public Sentiments toward Wind Energy in Norway. https://doi.org/10.1002/we.2902

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