Many governments have begun to adopt aggressive targets for electric vehicles. However, studies of the drivers of electric vehicle (EV) adoption are scarce. Social media interactions can provide a new data-driven vantage point to explore such drivers. This study uses data from 36,000 public posts on Facebook to investigate intersectionality in EV-communication as per the Political, Economic, Social, Technological, Legal and Environmental (PESTLE) categories. A computational social science methodology was adopted using a mixed-method application of social network analysis and machine learning-based topic modelling through Latent Dirichlet Allocation algorithm on a 600,000-text corpus extracted from the Facebook posts. Results showed that political, economic, and legal posts had dense clusters around the technology policy of EV, the institutional discourse of electrification of the federal vehicle fleet, and tax and credit framework politics. The environmental and social dimensions had a higher discourse for social justice, clean air, and better health and well-being. A market shift towards EV as a service industry was observed in the technology and economics-related posts. These findings can help policymakers, and planners design contextualised energy policy for influencing EV adoption in the U.S. and other countries.