  The public's attitudes play a critical role in the acceptance, purchase, use,
and research and development of autonomous vehicles (AVs). To date, the
public's attitudes towards AVs were mostly estimated through traditional survey
data with high labor costs and a low quantity of samples, which also might be
one of the reasons why the influencing factors on the public's attitudes of AVs
have not been studied from multiple aspects in a comprehensive way yet. To
address the issue, this study aims to propose a method by using large-scale
social media data to investigate key factors that affect the public's attitudes
and acceptance of AVs. A total of 954,151 Twitter data related to AVs and 53
candidate independent variables from seven categories were extracted using the
web scraping method. Then, sentiment analysis was used to measure the public
attitudes towards AVs by calculating sentiment scores. Random forests algorithm
was employed to preliminarily select candidate independent variables according
to their importance, while a linear mixed model was performed to explore the
impacting factors considering the unobserved heterogeneities caused by the
subjectivity level of tweets. The results showed that the overall attitude of
the public on AVs was slightly optimistic. Factors like "drunk", "blind spot",
and "mobility" had the largest impacts on public attitudes. In addition, people
were more likely to express positive feelings when talking about words such as
"lidar" and "Tesla" that relate to high technologies. Conversely, factors such
as "COVID-19", "pedestrian", "sleepy", and "highway" were found to have
significantly negative effects on the public's attitudes. The findings of this
study are beneficial for the development of AV technologies, the guidelines for
AV-related policy formulation, and the public's understanding and acceptance of
AVs.

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