# Analysing Social Media Discourse on Electric Vehicles with Machine Learning
## 1 Introduction  
Electric vehicles (EVs) have gained an important place among sustainable transportation alternatives in recent years [1]. Many governments and automotive companies are investing heavily in this technology because of its environmental benefits, such as reducing dependence on fossil fuels, lowering carbon emissions, and reducing air pollution [2; 3; 4]. However, society's adaptation and acceptance of this new technology vary depending on several factors. Social acceptance of electric vehicles is based on many factors, such as individuals' perceptions of the technology, their level of trust, cost analysis, and environmental awareness. At this point, social media platforms have become an important source where individuals freely share their opinions, express their experiences, and shape social perceptions [5]. This study aims to analyse the public's perspective on electric vehicles on the X platform (formerly Twitter). Social media is a valuable source of data that reveals what individuals think about current events and technologies. Within the scope of the study, 6000 English and 891 Turkish tweets posted in the last month were collected and analysed. Frequent discussions on social media reveal critical challenges in the adoption of EVs, such as insufficient charging infrastructure, high initial costs, concerns over battery life, and limited government incentives. Range anxiety and the availability of reliable and fast charging stations are recurring issues among users. Additionally, uncertainties regarding battery replacement costs and the lack of a standardised charging network across regions continue to raise concerns. These topics reflect the real-world hesitations that shape public discourse and perceptions regarding EVs. Using techniques such as text mining, sentiment analysis, and topic modelling, individuals' positive, negative, and neutral views on EVs were identified. The focus of this study is to reveal the differences between Turkish- and English-speaking consumers (electric vehicle users), to identify the prominent themes about EVs according to their moods, and to make important inferences about the adaptation process of society to this new technology. Social media platforms, which have been of great importance in marketing communication in recent years, provide an environment where individuals express and discuss their views on EVs. Individuals contribute to the shaping of social perception by sharing their experiences, expectations, and concerns about EVs on social media. Determining societal attitudes towards EVs is critical for the future of this technology. Therefore, analysing the opinions shared on social media offers great potential to reveal social interest, concerns, and expectations for EVs. While various studies have examined EV perceptions using surveys or interviews, very few have employed real-time social media data across different linguistic contexts [6; 7]. This study uniquely contributes by combining sentiment analysis and topic modelling on a bilingual dataset, revealing differences in perception between Turkish and English-speaking users. Thus, the study fills an important gap by providing a cross-cultural perspective using machine learning techniques applied to public social media discourse. This study analyses the real-time comments of users on the X platform to reveal current debates and trends about EVs. Moreover, through a comparative analysis of Turkish and English tweets, differences in perceptions in different cultural and linguistic contexts are also examined. In this respect, the study makes important contributions not only to the acceptance process of EVs but also to consumer perception research based on social media analysis. The comparative analysis also aims to highlight how cultural and linguistic factors may influence consumer sentiment, offering valuable implications for industry and policymakers.