IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v16y2023i1p305-d1309615.html
   My bibliography  Save this article

Demographic-Based Public Perception Analysis of Electric Vehicles on Online Social Networks

Author

Listed:
  • Tavishi Priyam

    (Department of Computer Science, University of Colorado Boulder, Boulder, CO 80309, USA
    These authors contributed equally to this work.)

  • Tao Ruan

    (Department of Computer Science, University of Colorado Boulder, Boulder, CO 80309, USA
    These authors contributed equally to this work.)

  • Qin Lv

    (Department of Computer Science, University of Colorado Boulder, Boulder, CO 80309, USA)

Abstract

Electric vehicles have gained significant popularity in the market, with sales increasing yearly. The introduction of new policies and reforms aimed at promoting environmental sustainability, coupled with the release of more advanced electric vehicles with higher driving ranges and technical specifications, has encouraged more people to consider switching to electric vehicles. However, there is still a lack of understanding of public perception and the factors influencing the decision to switch to electric vehicles, especially among people from different demographic groups. In this study, we leverage machine learning techniques to analyze public opinion about electric vehicles across different demographic groups on two online social networks (OSNs), namely Reddit and Twitter. Our analyses provide valuable insights into how users on these platforms perceive electric vehicles and the factors that influence their perception. This information can be used to inform market strategies and future policies aimed at promoting the adoption of electric vehicles.

Suggested Citation

  • Tavishi Priyam & Tao Ruan & Qin Lv, 2023. "Demographic-Based Public Perception Analysis of Electric Vehicles on Online Social Networks," Sustainability, MDPI, vol. 16(1), pages 1-16, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:305-:d:1309615
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/16/1/305/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/16/1/305/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Montgomery, James D, 1991. "Social Networks and Labor-Market Outcomes: Toward an Economic Analysis," American Economic Review, American Economic Association, vol. 81(5), pages 1407-1418, December.
    2. Grimmer, Justin & Stewart, Brandon M., 2013. "Text as Data: The Promise and Pitfalls of Automatic Content Analysis Methods for Political Texts," Political Analysis, Cambridge University Press, vol. 21(3), pages 267-297, July.
    3. Jake M. Hofman & Duncan J. Watts & Susan Athey & Filiz Garip & Thomas L. Griffiths & Jon Kleinberg & Helen Margetts & Sendhil Mullainathan & Matthew J. Salganik & Simine Vazire & Alessandro Vespignani, 2021. "Integrating explanation and prediction in computational social science," Nature, Nature, vol. 595(7866), pages 181-188, July.
    4. Tasnim M. A. Zayet & Maizatul Akmar Ismail & Kasturi Dewi Varathan & Rafidah M. D. Noor & Hui Na Chua & Angela Lee & Yeh Ching Low & Sheena Kaur Jaswant Singh, 2021. "Investigating transportation research based on social media analysis: a systematic mapping review," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6383-6421, August.
    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. Bernhardt, Lea & Dewenter, Ralf & Thomas, Tobias, 2023. "Measuring partisan media bias in US newscasts from 2001 to 2012," European Journal of Political Economy, Elsevier, vol. 78(C).
    2. Ntentas, Raphael, 2021. "Quantifying political populism and examining the link with economic insecurity: evidence from Greece," LSE Research Online Documents on Economics 112579, London School of Economics and Political Science, LSE Library.
    3. Rodrigo J. Harrison & Roberto Munoz, 2003. "Stability and Equilibrium Selection in a Link Formation Game," Game Theory and Information 0306004, University Library of Munich, Germany.
    4. Afridi, Farzana & Dhillon, Amrita & Sharma, Swati, 2015. "Social Networks and Labour Productivity: A Survey of Recent Theory and Evidence," Indian Economic Review, Department of Economics, Delhi School of Economics, vol. 50(1), pages 25-42.
    5. Jose Garcia‐Louzao & Marta Silva, 2024. "Coworker networks and the labor market outcomes of displaced workers: Evidence from Portugal," Industrial Relations: A Journal of Economy and Society, Wiley Blackwell, vol. 63(3), pages 389-413, July.
    6. Adnan Q. Khan & Steven F. Lehrer, 2013. "The Impact of Social Networks on Labour Market Outcomes: New Evidence from Cape Breton," Canadian Public Policy, University of Toronto Press, vol. 39(s1), pages 1-24, May.
    7. Lin, Annie E. & Young, Jimmy A. & Guarino, Jeannine E., 2022. "Mother-Daughter sexual abuse: An exploratory study of the experiences of survivors of MDSA using Reddit," Children and Youth Services Review, Elsevier, vol. 138(C).
    8. Brown, Alessio & Merkl, Christian & Snower, Dennis, 2015. "An Incentive Theory Of Matching," Macroeconomic Dynamics, Cambridge University Press, vol. 19(3), pages 643-668, April.
    9. Bramoullé, Yann & Goyal, Sanjeev, 2016. "Favoritism," Journal of Development Economics, Elsevier, vol. 122(C), pages 16-27.
    10. Steven N. Durlauf, 1996. "Statistical Mechanics Approaches to Socioeconomic Behavior," NBER Technical Working Papers 0203, National Bureau of Economic Research, Inc.
    11. Shao, Yan & Sun, Lingxia, 2021. "Entrepreneurs’ social capital and venture capital financing," Journal of Business Research, Elsevier, vol. 136(C), pages 499-512.
    12. Ponzo, Michela & Scoppa, Vincenzo, 2008. "The Use of Informal Networks in Italian Labor Markets: Efficiency or Favoritisms?," MPRA Paper 11764, University Library of Munich, Germany.
    13. Pierpaolo Parrotta & Dario Pozzoli & Mariola Pytlikova, 2014. "The nexus between labor diversity and firm’s innovation," Journal of Population Economics, Springer;European Society for Population Economics, vol. 27(2), pages 303-364, April.
    14. Oyer, Paul & Schaefer, Scott, 2011. "Personnel Economics: Hiring and Incentives," Handbook of Labor Economics, in: O. Ashenfelter & D. Card (ed.), Handbook of Labor Economics, edition 1, volume 4, chapter 20, pages 1769-1823, Elsevier.
    15. Pierre Cahuc & François Fontaine, 2009. "On the Efficiency of Job Search with Social Networks," Journal of Public Economic Theory, Association for Public Economic Theory, vol. 11(3), pages 411-439, June.
    16. Rybinski, Krzysztof, 2020. "The forecasting power of the multi-language narrative of sell-side research: A machine learning evaluation," Finance Research Letters, Elsevier, vol. 34(C).
    17. Rauh, Christian, 2015. "Communicating supranational governance? The salience of EU affairs in the German Bundestag, 1991–2013," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 16(1), pages 116-138.
    18. Grajzl, Peter & Murrell, Peter, 2021. "A machine-learning history of English caselaw and legal ideas prior to the Industrial Revolution I: generating and interpreting the estimates," Journal of Institutional Economics, Cambridge University Press, vol. 17(1), pages 1-19, February.
    19. Katherine M. O'Regan & John M. Quigley, 1996. "Teenage Employment and the Spatial Isolation of Minority and Poverty Households," Journal of Human Resources, University of Wisconsin Press, vol. 31(3), pages 692-702.
    20. Andreia Tolciu, 2010. "The Economics of Social Interactions: An Interdisciplinary Ground for Social Scientists?," Forum for Social Economics, Taylor & Francis Journals, vol. 39(3), pages 223-242, January.

    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:16:y:2023:i:1:p:305-:d:1309615. 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.