# Understanding user perceptions of DeepSeek: insights from sentiment, topic and network analysis using a Reddit-based study

## TL;DR

This study analyzes Reddit discussions about DeepSeek, a Chinese open-source AI model, to understand public sentiment and topics of interest.

## Contribution

The paper provides new insights into user perceptions of DeepSeek through combined sentiment, topic, and network analysis of Reddit data.

## Key findings

- Sentiment analysis showed predominantly positive and neutral reactions to DeepSeek.
- Key topics included open-source AI, device compatibility, and comparisons with ChatGPT.
- Network analysis revealed a fragmented but active community centered on open-source AI models.

## Abstract

The 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.

We 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.

Sentiment 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.

Community 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.