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Urban Informatics and Future CitiesPublic Perceptions and Attitudes Towards Driverless Technologies in the United States: A Text Mining of Twitter Data

Urban Informatics and Future Cities: Public Perceptions and Attitudes Towards Driverless... [Driverless technology offers the promise of providing safer and more efficient transportation in US cities. To promote public adoption of driverless vehicles, governments need to understand the public perceptions and attitudes towards driverless technologies and the factors that influence them. Twitter data offers a means to capture these insights. In this study, we performed text mining of tweets about driverless technology in the U.S. through topic modeling and sentiment analysis. We uncovered a set of five latent themes embedded in the tweets, consisting of Safety Perception, Technology Development, Industrial/System Integration, Design and Functionality, and Ethics and Policy, and the public’s attitudes of positive and negative towards each of them. The findings indicate that Ethics and Policy, Safety, and Design and Functionality are of major concern that may prohibit the acceptance of driverless vehicles. Insights gained from this analysis would support decision-making, implementation, and prompt communication strategies about driverless vehicles.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Urban Informatics and Future CitiesPublic Perceptions and Attitudes Towards Driverless Technologies in the United States: A Text Mining of Twitter Data

Part of the The Urban Book Series Book Series
Editors: Geertman, S. C. M.; Pettit, Christopher; Goodspeed, Robert; Staffans, Aija
Jiang, Zhiqiu; Zheng, Max
Urban Informatics and Future Cities — Jul 16, 2021
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Publisher
Springer International Publishing
Copyright
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
ISBN
978-3-030-76058-8
Pages
109 –126
DOI
10.1007/978-3-030-76059-5_7
Publisher site
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Abstract

[Driverless technology offers the promise of providing safer and more efficient transportation in US cities. To promote public adoption of driverless vehicles, governments need to understand the public perceptions and attitudes towards driverless technologies and the factors that influence them. Twitter data offers a means to capture these insights. In this study, we performed text mining of tweets about driverless technology in the U.S. through topic modeling and sentiment analysis. We uncovered a set of five latent themes embedded in the tweets, consisting of Safety Perception, Technology Development, Industrial/System Integration, Design and Functionality, and Ethics and Policy, and the public’s attitudes of positive and negative towards each of them. The findings indicate that Ethics and Policy, Safety, and Design and Functionality are of major concern that may prohibit the acceptance of driverless vehicles. Insights gained from this analysis would support decision-making, implementation, and prompt communication strategies about driverless vehicles.]

Published: Jul 16, 2021

Keywords: Driverless technologies; Perceptions and attitudes; Text mining; Sentiment analysis; Urban transportation

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