A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Public Perceptions and Attitudes Towards Driverless Technologies in the United States: A Text Mining of Twitter Data
https://orcid.org/https://orcid.org/0000-0002-7625-7799
https://orcid.org/https://orcid.org/0000-0001-7231-1164
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.
Public Perceptions and Attitudes Towards Driverless Technologies in the United States: A Text Mining of Twitter Data
https://orcid.org/https://orcid.org/0000-0002-7625-7799
https://orcid.org/https://orcid.org/0000-0001-7231-1164
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.
Public Perceptions and Attitudes Towards Driverless Technologies in the United States: A Text Mining of Twitter Data
https://orcid.org/https://orcid.org/0000-0002-7625-7799
https://orcid.org/https://orcid.org/0000-0001-7231-1164
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.
Public Perceptions and Attitudes Towards Driverless Technologies in the United States: A Text Mining of Twitter Data
The Urban Book Series
Geertman, S. C. M. (editor) / Pettit, Christopher (editor) / Goodspeed, Robert (editor) / Staffans, Aija (editor) / Jiang, Zhiqiu (author) / Zheng, Max (author)
2021-07-16
18 pages
Article/Chapter (Book)
Electronic Resource
English
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