AUTHOR=Patel Naisarg , Sharma Rajesh , Lingasamy Prakash , Sundararajan Vino , Lulu Sudhakaran Sajitha , Modhukur Vijayachitra 
  
TITLE=Understanding user perceptions of DeepSeek: insights from sentiment, topic and network analysis using a Reddit-based study
  
JOURNAL=Frontiers in Artificial Intelligence
  
VOLUME=Volume 8 - 2025
  
YEAR=2026
  
URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2025.1703949
  
DOI=10.3389/frai.2025.1703949
  
ISSN=2624-8212
  
ABSTRACT=IntroductionThe launch of DeepSeek, a Chinese open-source generative AI model, generated substantial discussion regarding its capabilities and implications. The r/deepseek subreddit emerged as a key forum for real-time public evaluation. Analyzing this discourse is essential for understanding the sociotechnical perceptions shaping the integration of emerging AI systems.MethodsWe analyzed 46,649 posts and comments from r/deepseek (January–May 2025) using a computational framework combining VADER sentiment analysis, Hartmann emotion classification, BERTopic for thematic modeling, hyperlink extraction, and directed network analysis. Data preprocessing included cleaning, normalization, and lemmatization. We also examined correlations between sentiment/emotion scores and dominant topics.ResultsSentiment was predominantly positive (posts: 47.23%; comments: 44.26%), with neutral sentiment comprising ~30% of content. The most frequent emotion was neutrality, followed by surprise and fear, indicating ambivalent user reactions. Prominent topics included open-source AI models, DeepSeek usage, device compatibility, comparisons with ChatGPT, and censorship concerns. Hyperlink analysis indicated strong engagement with GitHub, Hugging Face, and DeepSeek’s own services. Network analysis revealed a fragmented but active community, depicting Open-Source AI Models as the most cohesive cluster.DiscussionCommunity discourse framed DeepSeek as both a technical tool and a geopolitical issue. Enthusiasm centered on its performance, accessibility, and open-source nature, while concerns were voiced about censorship, data privacy, and potential ideological influence. The integrated analysis shows that collective perception emerged through decentralized, dialogic engagement, reflecting broader sociotechnical tensions related to openness, trust, and legitimacy in global AI development.