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Research Article | Open Access

Public perception of electric vehicles on reddit over the past decade

Department of Computer Science, University of Colorado Boulder, 430 UCB, Boulder, CO, 80309, USA
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Abstract

Understanding public perception of electric vehicles (EVs) is imperative for increasing EV adoption, which can significantly reduce greenhouse gas (GHG) emissions, thereby mitigating climate change and global warming. While most existing research characterizes public perception of EV in the context of surveys, questionnaires, or interviews, our study leverages Reddit online social network (OSN) data to capture EV public perception at a much larger scale. We have collected 3,437,917 Reddit posts (including 274,979 submissions and 3,162,938 comments) between January 2011 and December 2020 relevant to EVs and analyzed them along several axes to understand how EVs are perceived by the public on Reddit through the following research questions: (1) What EV-related topics have been discussed by Reddit users? Whether/how Reddit users' interest in different topics has changed during 2011–2020? (2) What sentiment do Reddit users hold towards EVs? Whether public sentiment on Reddit has shifted over the past 10 years? (3) Whether/how do various Reddit communities (i.e., subreddits) have different perceptions of EVs? Our analysis evinces the potential of utilizing a large-scale OSN dataset for demonstrating a much wider spectrum of topics that the public is interested in than previous studies show, reveals fringe communities including r/conspiracy have many (controversial) discussions on EVs, especially on the environmental impacts of EVs, and one political community (r/The_Donald) has similar patterns with fringe communities in both sentiment and topic aspects. By answering these research questions, we aim to develop a more comprehensive understanding of the public perception of EVs in the past decade.

References

 

Albuquerque, F.D., Maraqa, M.A., Chowdhury, R., Mauga, T., Alzard, M., 2020. Greenhouse gas emissions associated with road transport projects: current status, benchmarking, and assessment tools. Transport. Res. Procedia 48, 2018–2030.

 

Axsen, J., Langman, B., Goldberg, S., 2017. Confusion of innovations: mainstream consumer perceptions and misperceptions of electric-drive vehicles and charging programs in Canada. Energy Res. Social Sci. 27, 163–173.

 
Bauer, P.C., 2019. Conceptualizing and Measuring Polarization: A Review.
 
Baumgartner, J., Zannettou, S., Keegan, B., Squire, M., Blackburn, J., 2020. The pushshift reddit dataset. In: Proceedings of the International AAAI Conference on Web and Social Media, 14, pp. 830–839.
 

Blei, D.M., Ng, A.Y., Jordan, M.I., 2003. Latent dirichlet allocation. JMLR 3 (Jan), 993–1022.

 
Bockarjova, M., Rietveld, P., Knockaert, J., Steg, L., 2014. Dynamic Consumer Heterogeneity in Electric Vehicle Adoption. Technical report.
 

Bramson, A., Grim, P., Singer, D.J., Berger, W.J., Sack, G., Fisher, S., Flocken, C., Holman, B., 2017. Understanding polarization: meanings, measures, and model evaluation. Philos. Sci. 84 (1), 115–159.

 

Bunce, L., Harris, M., Burgess, M., 2014. Charge up then charge out? drivers' perceptions and experiences of electric vehicles in the UK. Transport. Res. Pol. Pract. 59, 278–287.

 
Carpenter, T., Golab, L., Syed, S.J., 2014. Is the grass greener? mining electric vehicle opinions. In: Proceedings of the 5th International Conference on Future Energy Systems, pp. 241–252.
 

Chaniotakis, E., Antoniou, C., Pereira, F., 2016. Mapping social media for transportation studies. IEEE Intell. Syst. 31 (6), 64–70.

 

Cook, D.L., 1962. The Hawthorne effect in educational research. Phi Delta Kappan 44 (3), 116–122.

 

Daziano, R.A., 2012. Taking account of the role of safety on vehicle choice using a new generation of discrete choice models. Saf. Sci. 50 (1), 103–112.

 

Debnath, R., Bardhan, R., Reiner, D.M., Miller, J., 2021. Political, economic, social, technological, legal and environmental dimensions of electric vehicle adoption in the United States: a social-media interaction analysis. Renew. Sustain. Energy Rev. 152, 111707.

 

del Barrio, E., Giné, E., Matrán, C., 1999. Central limit theorems for the Wasserstein distance between the empirical and the true distributions. Ann. Probab. 27 (2), 1009–1071.

 

Delmonte, E., Kinnear, N., Jenkins, B., Skippon, S., 2020. What do consumers think of smart charging? Perceptions among actual and potential plug-in electric vehicle adopters in the United Kingdom. Energy Res. Social Sci. 60, 101318.

 

Egbue, O., Long, S., 2012. Barriers to widespread adoption of electric vehicles: an analysis of consumer attitudes and perceptions. Energy Pol. 48, 717–729.

 

Feldman, R., 2013. Techniques and applications for sentiment analysis. Commun. ACM 56 (4), 82–89.

 

Glerum, A., Stankovikj, L., Thémans, M., Bierlaire, M., 2014. Forecasting the demand for electric vehicles: accounting for attitudes and perceptions. Transport. Sci. 48 (4), 483–499.

 

Greenberg, M.R., Weiner, M.D., 2014. Keeping surveys valid, reliable, and useful: a tutorial. Risk Anal. 34 (8), 1362–1375.

 

Grimmer, J., Roberts, M.E., Stewart, B.M., 2022. Text as Data: A New Framework for Machine Learning and the Social Sciences. Princeton University Press.

 
Guterres, A., 2020. Carbon Neutrality by 2050: the World's Most Urgent Mission.
 

Hackbarth, A., Madlener, R., 2013. Consumer preferences for alternative fuel vehicles: a discrete choice analysis. Transport. Res. Transport Environ. 25, 5–17.

 

Hamed, K.H., Rao, A.R., 1998. A modified Mann-Kendall trend test for autocorrelated data. J. Hydrol. 204 (1–4), 182–196.

 

Helveston, J.P., Liu, Y., Feit, E.M., Fuchs, E., Klampfl, E., Michalek, J.J., 2015. Will subsidies drive electric vehicle adoption? Measuring consumer preferences in the U.S. and China. Transport. Res. Pol. Pract. 73, 96–112.

 

Hidrue, M.K., Parsons, G.R., Kempton, W., Gardner, M.P., 2011. Willingness to pay for electric vehicles and their attributes. Resour. Energy Econ. 33 (3), 686–705.

 

Horne, M., Jaccard, M., Tiedemann, K., 2005. Improving behavioral realism in hybrid energy-economy models using discrete choice studies of personal transportation decisions. Energy Econ. 27 (1), 59–77.

 

Hu, Y., Boyd-Graber, J., Satinoff, B., Smith, A., 2014. Interactive topic modeling. Mach. Learn. 95 (3), 423–469.

 
Hutto, C., Gilbert, E., 2014. VADER: a parsimonious rule-based model for sentiment analysis of social media text. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 8.
 

Imai, K., 2018. Quantitative Social Science: an Introduction. Princeton University Press.

 
Isaac, M., 2021. Reddit, Acting against Hate Speech, Bans 'The_Donald' Subreddit.
 

Jensen, A.F., Cherchi, E., Mabit, S.L., 2013. On the stability of preferences and attitudes before and after experiencing an electric vehicle. Transport. Res. Transport Environ. 25, 24–32.

 

Karl, T.R., Melillo, J.M., Peterson, T.C., Hassol, S.J., 2009. Global Climate Change Impacts in the United States. Cambridge University Press.

 
Keen, P., Honnibal, M., Yankovsky, R., Karesh, D., et al., 2020. Textblob: Simplified Text Processing. Accessed on 11.13.2021.
 
Keith, L., Gabrielle, C., Jennifer, A.D., 2021. Biden Seeking Pledge for 40% of Car Sales to Be EV by 2030.
 

Khandakar, A., Rizqullah, A., Ashraf Abdou Berbar, A., Rafi Ahmed, M., Iqbal, A., Chowdhury, M.E., Uz Zaman, S., 2020. A case study to identify the hindrances to widespread adoption of electric vehicles in Qatar. Energies 13 (15).

 

Khasnis, A.A., Nettleman, M.D., 2005. Global warming and infectious disease. Arch. Med. Res. 36 (6), 689–696.

 

Kim, J., Rasouli, S., Timmermans, H., 2014. Expanding scope of hybrid choice models allowing for mixture of social influences and latent attitudes: application to intended purchase of electric cars. Transport. Res. Pol. Pract. 69, 71–85.

 

Li, R., Crowe, J., Leifer, D., Zou, L., Schoof, J., 2019. Beyond big data: social media challenges and opportunities for understanding social perception of energy. Energy Res. Social Sci. 56, 101217.

 

Liao, F., Molin, E., van Wee, B., 2017. Consumer preferences for electric vehicles: a literature review. Transport Rev. 37 (3), 252–275.

 

Liu, L., Tang, L., Dong, W., Yao, S., Zhou, W., 2016. An overview of topic modeling and its current applications in bioinformatics. SpringerPlus 5 (1), 1–22.

 

Lv, Y., Chen, Y., Zhang, X., Duan, Y., Li, N.L., 2017. Social media based transportation research: the state of the work and the networking. IEEE/CAA J Automatica Sinica 4 (1), 19–26.

 
McCallum, A.K., 2002. Mallet: a machine learning for language toolkit. http://mallet.cs.umass.edu.
 

Medhat, W., Hassan, A., Korashy, H., 2014. Sentiment analysis algorithms and applications: a survey. Ain Shams Eng. J. 5 (4), 1093–1113.

 
Mimno, D., Wallach, H., Talley, E., Leenders, M., McCallum, A., 2011. Optimizing semantic coherence in topic models. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 262–272.
 
Nielsen, F. Å., 2011. A New Anew: Evaluation of a Word List for Sentiment Analysis in Microblogs. arXiv preprint arXiv: 1103.2903.
 

Oh, J., Lee, H. -Y., Khuong, Q.L., Markuns, J.F., Bullen, C., Barrios, O.E.A., Hwang, S. -s., Suh, Y.S., McCool, J., Kachur, S.P., et al., 2021. Mobility restrictions were associated with reductions in COVID-19 incidence early in the pandemic: evidence from a real-time evaluation in 34 countries. Sci. Rep. 11 (1), 1–17.

 

Oreskes, N., 2004. The scientific consensus on climate change. Science 306 (5702), 1686–1686.

 
Pachauri, R.K., Allen, M.R., Barros, V.R., Broome, J., Cramer, W., Christ, R., Church, J.A., Clarke, L., Dahe, Q., Dasgupta, P., et al., 2014. Climate Change 2014: Synthesis Report. Contribution of Working Groups Ⅰ, Ⅱ and Ⅲ to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Ipcc.
 
Pew Research Center, 2021. Percentage of U.S. Adults Who Use Reddit as of February 2021, by Age Group.
 

Potoglou, D., Kanaroglou, P.S., 2007. Household demand and willingness to pay for clean vehicles. Transport. Res. Transport Environ. 12 (4), 264–274.

 

Rantala, S., Toikka, A., Pulkka, A., Lyytimäki, J., 2020. Energetic voices on social media? Strategic Niche Management and Finnish Facebook debate on biogas and heat pumps. Energy Res. Social Sci. 62, 101362.

 
Rehurek, R., Sojka, P., 2010. Software framework for topic modelling with large corpora. In: Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. Citeseer.
 

Ribeiro, F.N., Araújo, M., Gonçalves, P., Gonçalves, M.A., Benevenuto, F., 2016. Sentibench-a benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Sci. 5 (1), 1–29.

 
Ribeiro, M.H., Gligori'c, K., Peyrard, M., Lemmerich, F., Strohmaier, M., West, R., 2021. Sudden attention shifts on Wikipedia during the COVID-19 crisis. In: Proceedings of the International AAAI Conference on Web and Social Media, vol. 15, pp. 208–219.
 
Ruan, T., Kong, Q., Zhang, Y., McBride, S.K., Lv, Q., 2020. An analysis of Twitter responses to the 2019 Ridgecrest earthquake sequence. In: 2020 IEEE Intl Conf on Parallel & Distributed Processing with Applications, Big Data & Cloud Computing, Sustainable Computing & Communications, Social Computing & Networking (ISPA/BDCloud/SocialCom/SustainCom). IEEE, pp. 810–818.
 

Salganik, M.J., 2019. Bit by Bit: Social Research in the Digital Age. Princeton University Press.

 

Schneidereit, T., Franke, T., Günther, M., Krems, J.F., 2015. Does range matter? exploring perceptions of electric vehicles with and without a range extender among potential early adopters in Germany. Energy Res. Social Sci. 8, 198–206.

 

She, Z. -Y., Sun, Q., Ma, J. -J., Xie, B. -C., 2017. What are the barriers to widespread adoption of battery electric vehicles? A survey of public perception in Tianjin, China. Transport Pol. 56, 29–40.

 

Sintov, N.D., Abou-Ghalioum, V., White, L.V., 2020. The partisan politics of low-carbon transport: why democrats are more likely to adopt electric vehicles than Republicans in the United States. Energy Res. Social Sci. 68, 101576.

 

Tausczik, Y.R., Pennebaker, J.W., 2010. The psychological meaning of words: LIWC and computerized text analysis methods. J. Lit. Semant. 29 (1), 24–54.

 
The White House, 2021. FACT SHEET: President Biden Announces Steps to Drive American Leadership Forward on Clean Cars and Trucks.
 

Thomas, C.S., 2012. How green are electric vehicles? Int. J. Hydrogen Energy 37 (7), 6053–6062.

 

Tirado, M.C., Clarke, R., Jaykus, L., McQuatters-Gollop, A., Frank, J., 2010. Climate change and food safety: a review. Food Res. Int. 43 (7), 1745–1765.

 
United States Environmental Protection Agency, 2020. Fast Facts on Transportation Greenhouse Gas Emissions.
 

Valeri, E., Danielis, R., 2015. Simulating the market penetration of cars with alternative fuelpowertrain technologies in Italy. Transport Pol. 37, 44–56.

 

Vallender, S., 1974. Calculation of the Wasserstein distance between probability distributions on the line. Theor. Probab. Appl. 18 (4), 784–786.

 
van der Geest, K., de Sherbinin, A., Kienberger, S., Zommers, Z., Sitati, A., Roberts, E., James, R., 2019. The impacts of climate change on ecosystem services and resulting losses and damages to people and society. In: Loss and Damage from Climate Change. Springer, pp. 221–236.
 

Vassileva, I., Campillo, J., 2017. Adoption barriers for electric vehicles: experiences from early adopters in Sweden. Energy 120, 632–641.

 
Xing, Y., Wang, X., Qiu, C., Li, Y., He, W., 2022. Research on Opinion Polarization by Big Data Analytics Capabilities in Online Social Networks. Technology in Society, 101902.
 

Yuan, F., Li, M., Liu, R., 2020. Understanding the evolutions of public responses using social media: Hurricane Matthew case study. Int. J. Disaster Risk Reduc. 51, 101798.

 

Zayet, T.M., Ismail, M.A., Varathan, K.D., Noor, R.M., Chua, H.N., Lee, A., Low, Y.C., Singh, S.K.J., 2021. Investigating transportation research based on social media analysis: a systematic mapping review. Scientometrics 1–39.

 
Ziefle, M., Beul-Leusmann, S., Kasugai, K., Schwalm, M., 2014. Public perception and acceptance of electric vehicles: exploring users' perceived benefits and drawbacks. In: International Conference of Design, User Experience, and Usability. Springer, pp. 628–639.
Communications in Transportation Research
Article number: 100070
Cite this article:
Ruan T, Lv Q. Public perception of electric vehicles on reddit over the past decade. Communications in Transportation Research, 2022, 2(1): 100070. https://doi.org/10.1016/j.commtr.2022.100070

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Received: 04 April 2022
Revised: 31 May 2022
Accepted: 31 May 2022
Published: 18 June 2022
© 2022 The Author(s). Published by Elsevier Ltd on behalf of Tsinghua University Press.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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