IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i19p12186-d925544.html
   My bibliography  Save this article

Investigating the Impacting Factors on the Public’s Attitudes towards Autonomous Vehicles Using Sentiment Analysis from Social Media Data

Author

Listed:
  • Shengzhao Wang

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, 4800 Cao’an Highway, Shanghai 201804, China)

  • Meitang Li

    (Human Factors Group, University of Michigan Transportation Research Institute, 2901 Baxter Rd., Ann Arbor, MI 48109, USA)

  • Bo Yu

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, 4800 Cao’an Highway, Shanghai 201804, China)

  • Shan Bao

    (Human Factors Group, University of Michigan Transportation Research Institute, 2901 Baxter Rd., Ann Arbor, MI 48109, USA
    Industrial and Manufacturing Systems Engineering Department, University of Michigan—Dearborn, 4901 Evergreen Rd., Dearborn, MI 48128, USA)

  • Yuren Chen

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, College of Transportation Engineering, Tongji University, 4800 Cao’an Highway, Shanghai 201804, China)

Abstract

The attitudes of the public play a critical role in the acceptance, purchase, utilization, and research and development of autonomous vehicles (AVs). Currently, the attitudes of the public toward AVs have been mostly estimated through traditional survey data, which bears a low quantity of samples with high labor costs. It is probably also one of the reasons why the critical factors on the attitudes of the public toward AVs have not been studied from a comprehensive perspective 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 attitudes of the public toward 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 and a linear mixed model was utilized to explore the impacting factors, considering the unobserved heterogeneities caused by the subjectivity level of tweets. The results showed that the attitudes of the public toward AVs were slightly optimistic. Factors, such as “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”, related to high technologies. Conversely, factors, such as “COVID-19”, “pedestrian”, “sleepy”, and “highway”, were found to have significantly negative effects on the attitudes of the public. The findings of this study are beneficial for the development of AV technologies, the guidelines for AV-related policy formulation, and the understanding and acceptance of the public toward AVs.

Suggested Citation

  • Shengzhao Wang & Meitang Li & Bo Yu & Shan Bao & Yuren Chen, 2022. "Investigating the Impacting Factors on the Public’s Attitudes towards Autonomous Vehicles Using Sentiment Analysis from Social Media Data," Sustainability, MDPI, vol. 14(19), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12186-:d:925544
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/19/12186/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/19/12186/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wright, Marvin N. & Ziegler, Andreas, 2017. "ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 77(i01).
    2. Nielsen, Thomas Alexander Sick & Haustein, Sonja, 2018. "On sceptics and enthusiasts: What are the expectations towards self-driving cars?," Transport Policy, Elsevier, vol. 66(C), pages 49-55.
    3. Kapser, Sebastian & Abdelrahman, Mahmoud & Bernecker, Tobias, 2021. "Autonomous delivery vehicles to fight the spread of Covid-19 – How do men and women differ in their acceptance?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 148(C), pages 183-198.
    4. Bennett, Roger & Vijaygopal, Rohini & Kottasz, Rita, 2019. "Attitudes towards autonomous vehicles among people with physical disabilities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 127(C), pages 1-17.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Márta Volosin & Martin Kálnay & Ádám Bánffi & Natália Nyeső & Gabriella Viktória Molnár & Zsolt Palatinus & Tamás Martos, 2024. "The leading role of personality in concerns about autonomous vehicles," PLOS ONE, Public Library of Science, vol. 19(6), pages 1-23, June.
    2. Guo, Yuntao & Souders, Dustin & Labi, Samuel & Peeta, Srinivas & Benedyk, Irina & Li, Yujie, 2021. "Paving the way for autonomous Vehicles: Understanding autonomous vehicle adoption and vehicle fuel choice under user heterogeneity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 154(C), pages 364-398.
    3. Waltermann, Juliana & Henkel, Sven, 2025. "The human element in autonomous driving: Motivations, expectations, and behavioral change," Technological Forecasting and Social Change, Elsevier, vol. 213(C).
    4. Garrow, Laurie A. & Mokhtarian, Patricia L. & German, Brian J. & “Jack” S. Glodek, John & Leonard, Caroline E., 2025. "Market segmentation of an electric vertical takeoff and landing (eVTOL) air taxi commuting service in five large U.S. cities," Transportation Research Part A: Policy and Practice, Elsevier, vol. 191(C).
    5. Li, Ruimin & Han, Yuqian & Zhou, Huiyu, 2025. "What changes the autonomous vehicle acceptance after COVID-19? Evidence from China," Research in Transportation Economics, Elsevier, vol. 109(C).
    6. Wang, Song & Li, Zhixia & Wang, Yi & Aaron Wyatt, Daniel, 2022. "How do age and gender influence the acceptance of automated vehicles? – Revealing the hidden mediating effects from the built environment and personal factors," Transportation Research Part A: Policy and Practice, Elsevier, vol. 165(C), pages 376-394.
    7. Backer, David & Billing, Trey, 2024. "Forecasting the prevalence of child acute malnutrition using environmental and conflict conditions as leading indicators," World Development, Elsevier, vol. 176(C).
    8. Luis A Barboza & Shu-Wei Chou-Chen & Paola Vásquez & Yury E García & Juan G Calvo & Hugo G Hidalgo & Fabio Sanchez, 2023. "Assessing dengue fever risk in Costa Rica by using climate variables and machine learning techniques," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 17(1), pages 1-13, January.
    9. Mariana Oliveira & Luís Torgo & Vítor Santos Costa, 2021. "Evaluation Procedures for Forecasting with Spatiotemporal Data," Mathematics, MDPI, vol. 9(6), pages 1-27, March.
    10. Wang, Song & Li, Zhixia & Wang, Yi & Wyatt, Daniel Aaron, 2024. "How effective is automated vehicle education? – A Kentucky case study revealing the dynamic nature of education effectiveness," Transport Policy, Elsevier, vol. 147(C), pages 140-157.
    11. Qian, Lixian & Yin, Juelin & Huang, Youlin & Liang, Ya, 2023. "The role of values and ethics in influencing consumers’ intention to use autonomous vehicle hailing services," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    12. Bokelmann, Björn & Lessmann, Stefan, 2024. "Improving uplift model evaluation on randomized controlled trial data," European Journal of Operational Research, Elsevier, vol. 313(2), pages 691-707.
    13. Joel Podgorski & Oliver Kracht & Luis Araguas-Araguas & Stefan Terzer-Wassmuth & Jodie Miller & Ralf Straub & Rolf Kipfer & Michael Berg, 2024. "Groundwater vulnerability to pollution in Africa’s Sahel region," Nature Sustainability, Nature, vol. 7(5), pages 558-567, May.
    14. Heinisch, Katja & Scaramella, Fabio & Schult, Christoph, 2025. "Assumption errors and forecast accuracy: A partial linear instrumental variable and double machine learning approach," IWH Discussion Papers 6/2025, Halle Institute for Economic Research (IWH).
    15. Kassens-Noor, Eva & Cai, Meng & Kotval-Karamchandani, Zeenat & Decaminada, Travis, 2021. "Autonomous vehicles and mobility for people with special needs," Transportation Research Part A: Policy and Practice, Elsevier, vol. 150(C), pages 385-397.
    16. Alimo, Philip Kofi & Agyeman, Stephen & Agen-Davis, Lawrencia & Hisseine, Mahamat Ali & Sarfo, Isaac, 2024. "Lived transportation barriers for persons with disabilities: Contextualizing the Ghana disability law through the lenses of Giddens' theory of structuration," Journal of Transport Geography, Elsevier, vol. 118(C).
    17. Chakravorty, Bhaskar & Arulampalam, Wiji & Bhatiya, Apurav Yash & Imbert, Clément & Rathelot, Roland, 2024. "Can information about jobs improve the effectiveness of vocational training? Experimental evidence from India," Journal of Development Economics, Elsevier, vol. 169(C).
    18. Arjan S. Gosal & Janine A. McMahon & Katharine M. Bowgen & Catherine H. Hoppe & Guy Ziv, 2021. "Identifying and Mapping Groups of Protected Area Visitors by Environmental Awareness," Land, MDPI, vol. 10(6), pages 1-14, May.
    19. Mishra, Sabyasachee & Sharma, Ishant & Pani, Agnivesh, 2023. "Analyzing autonomous delivery acceptance in food deserts based on shopping travel patterns," Transportation Research Part A: Policy and Practice, Elsevier, vol. 169(C).
    20. David Dorn & Florian Schoner & Moritz Seebacher & Lisa Simon & Ludger Woessmann, 2024. "Multidimensional Skills on LinkedIn Profiles: Measuring Human Capital and the Gender Skill Gap," Papers 2409.18638, arXiv.org, revised May 2025.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:19:p:12186-:d:925544. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.