Public Acceptance of Mobility Electrification in Germany and China: Insights from NLP and Social Media Analysis

This study investigates cross-cultural differences in public perception of mobility electrification by applying Natural Language Processing (NLP) techniques to social media discourse in Germany and China. Using a large language model, we conducted sentiment analysis and zero-shot thematic classification on over 10,000 posts to explore how citizens in each country engage with the topic of electric mobility. Results reveal that while infrastructure readiness is a dominant concern in both contexts, German discourse places greater emphasis on environmental impact, often reflecting skepticism toward sustainability claims. In contrast, Chinese discussions highlight technological advancement and infrastructure expansion, with comparatively limited focus on environmental concerns. Sentiment in Germany tends to be more reserved and analytical, whereas Chinese discourse appears more expressive and emotionally varied. These findings underscore the importance of culturally tailored policy and communication strategies in supporting the public acceptance of electric mobility. By demonstrating how large-scale social media data can be used to analyze public sentiment across linguistic and cultural contexts, this study contributes methodologically to the emerging field of computational social science and offers practical insights for mobility policy in diverse national settings.