Citation

Penmetsa P, Sheinidashtegol P, Musaev A, Adanu EK, Hudnall M. IATSS Res. 2021; 45(4): 485-492.

Copyright

(Copyright © 2021, International Association of Traffic and Safety Sciences, Publisher Elsevier Publishing)

DOI

10.1016/j.iatssr.2021.04.003

PMID

unavailable

Abstract

In March 2018, an Uber-pedestrian crash and a Tesla's Model X crash attracted a lot of media attention because the vehicles were operating under self-driving and autopilot mode respectively at the time of the crash. This study aims to conduct before-and-after sentiment analysis to examine how these two fatal crashes have affected people's perceptions of self-driving and autonomous vehicle technology using Twitter data. Five different and relevant keywords were used to extract tweets. Over 1.7 million tweets were found within 15 days before and after the incidents with the specific keywords, which were eventually analyzed in this study. The results indicate that after the two incidents, the negative tweets on "self-driving/autonomous" technology increased by 32 percentage points (from 14% to 46%). The compound scores of "pedestrian crash", "Uber", and "Tesla" keywords saw a 6% decrease while "self-driving/autonomous" recorded the highest change with an 11% decrease. Before the Uber-incident, 19% of the tweets on Uber were negative and 27% were positive. With the Uber-pedestrian crash, these percentages have changed to 30% negative and 23% positive. Overall, the negativity in the tweets and the percentage of negative tweets on self-driving/autonomous technology have increased after their involvement in fatal crashes. Providing opportunities to interact with this developing technology has shown to positively influence peoples' perception.

Language: en

Keywords

Autonomous vehicles; Crashes; Perceptions; Self-driving vehicles; Sentiment analysis; Social media data