Skip to main content
SpringerLink
Log in

The Evolution of Public Perceptions of Automated Vehicles in China: A Text Mining Approach Based Dynamic Topic Modeling

  • Conference paper
  • First Online:
HCI International 2023 – Late Breaking Papers (HCII 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14057))

Included in the following conference series:

  • 297 Accesses

Abstract

Public attitudes and intentions are crucial for successful technological innovation, such as automated vehicles (AVs). To effectively advance the development and future evolution of AVs, it is crucial to comprehensively understand individuals’ perceptions of autonomous driving. However, traditional survey methods using structured questionnaires may limit respondents’ ability to express themselves freely. To address this limitation, we employ Natural Language Processing (NLP) techniques to analyze consumers’ opinions and attitudes towards AVs as shared on social media platforms. Through Python programming, we collected and analyzed consumer comments from leading Chinese social media platforms (Sina Weibo, TikTok) and automotive social media platforms (Autohome Inc.) between June 2020 and April 2023, totaling 120,486 comments. Leveraging advanced text mining techniques such as Dynamic topic models (DTM), sentiment analysis, and semantic network analysis based on Pointwise mutual information (PMI) algorithms, we investigate the evolution of public perception regarding AVs over the past three years. Our findings unveil a predominant negative sentiment towards AVs, with discernible shifts in sentiment coinciding with major AV-related social events. Furthermore, we explore the reasons behind users’ negative attitudes and identify potential factors contributing to the distrust of autonomous driving. These findings provide valuable guidance to public agencies, automobile manufacturers, and technology companies, enhancing their understanding of the adoption of AVs.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 79.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 99.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Fagnant, D.J., Kockelman, K.: Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations. Transp. Res. Part A Policy Pract. 77, 167–181 (2015)

    Article  Google Scholar 

  2. Tan, H., Zhao, X., Yang, J.: Exploring the influence of anxiety, pleasure and subjective knowledge on public acceptance of fully autonomous vehicles. Comput. Hum. Behav. 131, 107187 (2022)

    Article  Google Scholar 

  3. Seter, H., Hansen, L., Arnesen, P.: Comparing user acceptance of integrated and retrofit driver assistance systems–a real-traffic study. Transp. Res. Part F Traffic Psychol. Behav. 79, 139–156 (2021)

    Article  Google Scholar 

  4. Hengstler, M., Enkel, E., Duelli, S.: Applied artificial intelligence and trust—the case of autonomous vehicles and medical assistance devices. Technol. Forecast. Soc. Change 105, 105–120 (2016)

    Article  Google Scholar 

  5. Raats, K., Fors, V., Pink, S.: Trusting autonomous vehicles: an interdisciplinary approach. Transp. Res. Interdisc. Perspect. 7, 100201 (2020)

    Google Scholar 

  6. Zhang, T., et al.: The roles of initial trust and perceived risk in public’s acceptance of automated vehicles. Transp. Res. Part C Emerg. Technol. 98, 207–220 (2019)

    Article  Google Scholar 

  7. Liljamo, T., Liimatainen, H., Pöllänen, M.: Attitudes and concerns on automated vehicles. Transp. Res. Part F Traffic Psychol. Behav. 59, 24–44 (2018)

    Article  Google Scholar 

  8. Choi, J.K., Ji, Y.G.: Investigating the importance of trust on adopting an autonomous vehicle. Int. J. Hum.-Comput. Interact. 31(10), 692–702 (2015)

    Article  Google Scholar 

  9. Xu, Z., et al.: What drives people to accept automated vehicles? Findings from a field experiment. Transp. Res. Part C Emerg. Technol. 95, 320–334 (2018)

    Article  Google Scholar 

  10. Nordhoff, S., van Arem, B., Happee, R.: Conceptual model to explain, predict, and improve user acceptance of driverless podlike vehicles. Transp. Res. Rec. 2602(1), 60–67 (2016)

    Article  Google Scholar 

  11. Nastjuk, I., et al.: What drives the acceptance of autonomous driving? An investigation of acceptance factors from an end-user’s perspective. Technol. Forecast. Soc. Chang. 161, 120319 (2020)

    Article  Google Scholar 

  12. Pink, S., et al.: Design anthropology for emerging technologies: trust and sharing in autonomous driving futures. Des. Stud. 69, 100942 (2020)

    Article  Google Scholar 

  13. Das, S., et al.: Extracting patterns from Twitter to promote biking. IATSS Res. 43(1), 51–59 (2019)

    Article  Google Scholar 

  14. Gkartzonikas, C., Gkritza, K.: What have we learned? A review of stated preference and choice studies on autonomous vehicles. Transp. Res. Part C Emerg. Technol. 98, 323–337 (2019)

    Article  Google Scholar 

  15. Guo, Y., Barnes, S.J., Jia, Q.: Mining meaning from online ratings and reviews: tourist satisfaction analysis using latent dirichlet allocation. Tour. Manage. 59, 467–483 (2017)

    Article  Google Scholar 

  16. Jing, P., et al.: Listen to social media users: mining Chinese public perception of automated vehicles after crashes. Transport. Res. F Traffic Psychol. Behav. 93, 248–265 (2023)

    Article  Google Scholar 

  17. Ding, Y., et al.: How are sentiments on autonomous vehicles influenced? An analysis using Twitter feeds. Transp. Res. Part C Emerg. Technol. 131, 103356 (2021)

    Article  Google Scholar 

  18. Sauer, V., et al.: Designing automated vehicle interiors for different cultures: evidence from China, Germany, and the United States. Ergon. Des. 30(3), 16–22 (2022)

    Google Scholar 

  19. Co, M.: From sci-fi to reality: autonomous driving in China (2023)

    Google Scholar 

  20. Li, D., et al.: Adding community and dynamic to topic models. J. Informet. 6(2), 237–253 (2012)

    Article  Google Scholar 

  21. Lafferty, J., Blei, D.: Correlated topic models. In: Advances in Neural Information Processing Systems, vol. 18 (2005)

    Google Scholar 

  22. Featherstone, J.D., et al.: Exploring childhood vaccination themes and public opinions on Twitter: a semantic network analysis. Telemat. Inform. 54, 101474 (2020)

    Article  Google Scholar 

  23. Mahoney, W.P., O’Sullivan, J.M.: Realizing the potential of vehicle-based observations. Bull. Am. Meteor. Soc. 94(7), 1007–1018 (2013)

    Article  Google Scholar 

  24. Planing, P.: Innovation Acceptance: The Case of Advanced Driver-Assistance Systems. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-658-05005-4

Download references

Author information

Authors and Affiliations

  1. Tongji University, Shanghai, China

    Jun Ma & Qinrui Yang

  2. College of Design and Innovation, Tongji University, Shanghai, China

    Xuejing Feng

Authors
  1. Jun Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuejing Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Qinrui Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xuejing Feng .

Editor information

Editors and Affiliations

  1. Purdue University, West Lafayette, IN, USA

    Vincent G. Duffy

  2. Technische Universitat Ilmenau, Ilmenau, Germany

    Heidi Krömker

  3. Smart Future Initiative, Frankfurt am Main, Germany

    Norbert A. Streitz

  4. Kyushu University, Fukuoka, Japan

    Shin'ichi Konomi

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ma, J., Feng, X., Yang, Q. (2023). The Evolution of Public Perceptions of Automated Vehicles in China: A Text Mining Approach Based Dynamic Topic Modeling. In: Duffy, V.G., Krömker, H., A. Streitz, N., Konomi, S. (eds) HCI International 2023 – Late Breaking Papers. HCII 2023. Lecture Notes in Computer Science, vol 14057. Springer, Cham. https://doi.org/10.1007/978-3-031-48047-8_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-48047-8_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-48046-1

  • Online ISBN: 978-3-031-48047-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation