Effects of the autonomous vehicle crashes on public perception of the technology
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 t...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-12-01
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| Series: | IATSS Research |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S0386111221000224 |
| _version_ | 1819096048873766912 |
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| author | Praveena Penmetsa Pezhman Sheinidashtegol Aibek Musaev Emmanuel Kofi Adanu Matthew Hudnall |
| author_facet | Praveena Penmetsa Pezhman Sheinidashtegol Aibek Musaev Emmanuel Kofi Adanu Matthew Hudnall |
| author_sort | Praveena Penmetsa |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T23:53:00Z |
| format | Article |
| id | doaj.art-2378d15832ac4988a4cdd6a398452dfd |
| institution | Directory Open Access Journal |
| issn | 0386-1112 |
| language | English |
| last_indexed | 2024-12-21T23:53:00Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | IATSS Research |
| spelling | doaj.art-2378d15832ac4988a4cdd6a398452dfd2022-12-21T18:45:52ZengElsevierIATSS Research0386-11122021-12-01454485492Effects of the autonomous vehicle crashes on public perception of the technologyPraveena Penmetsa0Pezhman Sheinidashtegol1Aibek Musaev2Emmanuel Kofi Adanu3Matthew Hudnall4Alabama Transportation Institute, The University of Alabama, Tuscaloosa, AL, USA; Corresponding author at: 248 Kirbride Ln, Cyber Hall 3014, Tuscaloosa 35401, USA.Department of Computer Science, The University of Alabama, Tuscaloosa, AL, USACollege of Computing, Georgia Institute of Technology, Atlanta, GA, USAAlabama Transportation Institute, The University of Alabama, Tuscaloosa, AL, USAInformation Systems, Statistics, and Management Science, The University of Alabama, Tuscaloosa, AL, USAIn 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.http://www.sciencedirect.com/science/article/pii/S0386111221000224Autonomous vehiclesSelf-driving vehiclesPerceptionsCrashesSentiment analysisSocial media data |
| spellingShingle | Praveena Penmetsa Pezhman Sheinidashtegol Aibek Musaev Emmanuel Kofi Adanu Matthew Hudnall Effects of the autonomous vehicle crashes on public perception of the technology IATSS Research Autonomous vehicles Self-driving vehicles Perceptions Crashes Sentiment analysis Social media data |
| title | Effects of the autonomous vehicle crashes on public perception of the technology |
| title_full | Effects of the autonomous vehicle crashes on public perception of the technology |
| title_fullStr | Effects of the autonomous vehicle crashes on public perception of the technology |
| title_full_unstemmed | Effects of the autonomous vehicle crashes on public perception of the technology |
| title_short | Effects of the autonomous vehicle crashes on public perception of the technology |
| title_sort | effects of the autonomous vehicle crashes on public perception of the technology |
| topic | Autonomous vehicles Self-driving vehicles Perceptions Crashes Sentiment analysis Social media data |
| url | http://www.sciencedirect.com/science/article/pii/S0386111221000224 |
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