Public perception of electric vehicles on Reddit and Twitter: A cross-platform analysis
Tài liệu tham khảo
Ahmed, 2021, Using Twitter as a data source an overview of social media research tools (2021), Impact Soc. Sci. Blog
Albuquerque, 2020, Greenhouse gas emissions associated with road transport projects: Current status, benchmarking, and assessment tools, Transp. Res. Procedia, 48, 2018, 10.1016/j.trpro.2020.08.261
Alghamdi, 2015, A survey of topic modeling in text mining, Int. J. Adv. Comput. Sci. Appl.(IJACSA), 6
Asadi, 2021, Factors impacting consumers’ intention toward adoption of electric vehicles in Malaysia, J. Clean. Prod., 282, 10.1016/j.jclepro.2020.124474
Auffhammer, 2018, Quantifying economic damages from climate change, J. Econ. Perspect., 32, 33, 10.1257/jep.32.4.33
Auxier, 2021, Social media use in 2021, Pew Res. Center, 1, 1
Baumeister, 2001, Bad is stronger than good, Rev. General Psychol., 5, 323, 10.1037/1089-2680.5.4.323
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, Vol. 14. pp. 830–839.
Blei, 2003, Latent dirichlet allocation, JMLR, 3, 993
Breschi, 2022, Fostering the mass adoption of electric vehicles: A network-based approach, IEEE Trans. Control Network Syst., 9, 1666, 10.1109/TCNS.2022.3164969
Broadbent, 2021, Electric vehicle uptake: Understanding the print media’s role in changing attitudes and perceptions, World Electr. Veh. J., 12, 174, 10.3390/wevj12040174
Camacho, 2022
Chang, 2020, ConvoKit: A toolkit for the analysis of conversations, 57
Chaniotakis, 2016, Mapping social media for transportation studies, IEEE Intell. Syst., 31, 64, 10.1109/MIS.2016.98
Chen, 2021, Using data from Reddit, public deliberation, and surveys to measure public opinion about autonomous vehicles, Public Opin. Q., 85, 289, 10.1093/poq/nfab021
Christopherson, 2007, The positive and negative implications of anonymity in Internet social interactions:“On the Internet, Nobody Knows You’re a Dog”, Comput. Hum. Behav., 23, 3038, 10.1016/j.chb.2006.09.001
Cook, 1962, The Hawthorne effect in educational research, Phi Delta Kappan, 44, 116
Daziano, 2014, Forecasting adoption of ultra-low-emission vehicles using Bayes estimates of a multinomial probit model and the GHK simulator, Transp. Sci., 48, 671, 10.1287/trsc.2013.0464
Debnath, 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, 10.1016/j.rser.2021.111707
Devika, 2016, Sentiment analysis: A comparative study on different approaches, Procedia Comput. Sci., 87, 44, 10.1016/j.procs.2016.05.124
Ding, 2021, How are sentiments on autonomous vehicles influenced? An analysis using Twitter feeds, Transp. Res. C, 131, 10.1016/j.trc.2021.103356
Dixon, 2021
Egbue, 2012, Barriers to widespread adoption of electric vehicles: An analysis of consumer attitudes and perceptions, Energy Policy, 48, 717, 10.1016/j.enpol.2012.06.009
Freberg, 2012, Intention to comply with crisis messages communicated via social media, Public Relat. Rev., 38, 416, 10.1016/j.pubrev.2012.01.008
García, 2015
Greenberg, 2014, Keeping surveys valid, reliable, and useful: A tutorial, Risk Anal., 34, 1362, 10.1111/risa.12250
Grimmer, 2022
Guo, 2021, Disparities and equity issues in electric vehicles rebate allocation, Energy Policy, 154, 10.1016/j.enpol.2021.112291
Hansen, 2013, Assessing “dangerous climate change”: Required reduction of carbon emissions to protect young people, future generations and nature, PLoS One, 8, 10.1371/journal.pone.0081648
Hofman, 2021, Integrating explanation and prediction in computational social science, Nature, 595, 181, 10.1038/s41586-021-03659-0
Horne, 2005, Improving behavioral realism in hybrid energy-economy models using discrete choice studies of personal transportation decisions, Energy Econ., 27, 59, 10.1016/j.eneco.2004.11.003
Imai, 2018
Kanouse, 1987, Negativity in evaluations
Keith, 2021
Kester, 2018, Policy mechanisms to accelerate electric vehicle adoption: A qualitative review from the Nordic region, Renew. Sustain. Energy Rev., 94, 719, 10.1016/j.rser.2018.05.067
Li, 2018, Youtube av 50k: An annotated corpus for comments in autonomous vehicles, 1
Li, 2020, Do policy mix characteristics matter for electric vehicle adoption? A survey-based exploration, Transp. Res. Part D: Transp. Environ., 87, 10.1016/j.trd.2020.102488
Liao, 2017, Consumer preferences for electric vehicles: A literature review, Transp. Rev., 37, 252, 10.1080/01441647.2016.1230794
Ling, 2021, Determining the factors that influence electric vehicle adoption: A stated preference survey study in Beijing, China, Sustainability, 13, 11719, 10.3390/su132111719
Luarn, 2014, Speech or silence: The effect of user anonymity and member familiarity on the willingness to express opinions in virtual communities, Online Inf. Rev., 10.1108/OIR-03-2014-0076
Lukito, 2020, Coordinating a multi-platform disinformation campaign: Internet research agency activity on three US social media platforms, 2015 to 2017, Political Commun., 37, 238, 10.1080/10584609.2019.1661889
Lutsey, 2019, Update on electric vehicle costs in the United States through 2030, Int. Counc. Clean Transp., 12
Lv, 2017, Social media based transportation research: The state of the work and the networking, IEEE/CAA J. Autom. Sin., 4, 19, 10.1109/JAS.2017.7510316
Mabit, 2011, Demand for alternative-fuel vehicles when registration taxes are high, Transp. Res. Part D: Transp. Environ., 16, 225, 10.1016/j.trd.2010.11.001
Medhat, 2014, Sentiment analysis algorithms and applications: A survey, Ain Shams Eng. J., 5, 1093, 10.1016/j.asej.2014.04.011
Molin, E., van Stralen, W., van Wee, B., 2012. Car Drivers’ Preferences for Electric Cars. Tech. Rep..
Monroe, 2008, Fightin’words: Lexical feature selection and evaluation for identifying the content of political conflict, Political Anal., 16, 372, 10.1093/pan/mpn018
Nielsen, 2011
Pal, 2022, Social media based public opinion analysis, Int. Res. J. Mod. Eng. Technol. Sci., 2097
Pew Research Center, 2021
Pfeffer, 2022
Rehurek, 2010, Software framework for topic modelling with large corpora
Ribeiro, 2016, Sentibench-a benchmark comparison of state-of-the-practice sentiment analysis methods, EPJ Data Sci., 5, 1, 10.1140/epjds/s13688-016-0085-1
Rozin, 2001, Negativity bias, negativity dominance, and contagion, Pers. Soc. Psychol. Rev., 5, 296, 10.1207/S15327957PSPR0504_2
Ruan, 2022, Public perception of electric vehicles on reddit over the past decade, Commun. Transp. Res., 2, 10.1016/j.commtr.2022.100070
Salganik, 2019
Singh, 2020, A review and simple meta-analysis of factors influencing adoption of electric vehicles, Transp. Res. Part D: Transp. Environ., 86, 10.1016/j.trd.2020.102436
Tarei, 2021, Barriers to the adoption of electric vehicles: Evidence from India, J. Clean. Prod., 291, 10.1016/j.jclepro.2021.125847
Tausczik, 2010, The psychological meaning of words: LIWC and computerized text analysis methods, JLS, 29, 24
The White House, 2021
Vaish, 2008, Not all emotions are created equal: The negativity bias in social-emotional development, Psychol. Bull., 134, 383, 10.1037/0033-2909.134.3.383
Vayansky, 2020, A review of topic modeling methods, Inf. Syst., 94, 10.1016/j.is.2020.101582
Wang, 2022, Consumers’ attitudes and their effects on electric vehicle sales and charging infrastructure construction: An empirical study in China, Energy Policy, 165, 10.1016/j.enpol.2022.112983
Wang, 2019
Wang, 2017, Future extreme climate changes linked to global warming intensity, Sci. Bull., 62, 1673, 10.1016/j.scib.2017.11.004
Zayet, 2021, Investigating transportation research based on social media analysis: A systematic mapping review, Scientometrics, 1
Zhang, 2020, Data-driven computational social science: a survey, Big Data Research, 21, 100145, 10.1016/j.bdr.2020.100145
Zinnari, 2021, Electrification potential of fuel-based vehicles and optimal placing of charging infrastructure: A large-scale vehicle-telematics approach, IEEE Trans. Transp. Electrif., 8, 466, 10.1109/TTE.2021.3114497