Problem
Research questions and friction points this paper is trying to address.
cross-language analysis
game reviews
multilingual content analysis
cultural differences
Deciphering the Babel of Play: A Human-AI Collaborative Approach for Large-Scale Cross-Language Analysis of Game Reviews
🤖 AI Summary
研究使用人机协作框架和多语言大模型分析了1700万条跨语言游戏评论，揭示了不同语言社区在游戏评价方面的差异和共识。
📝 Abstract
We present a large-scale cross-language analysis of game reviews using a human-AI collaborative framework that combines quantitative screening with multilingual large language models (LLMs). Starting from 17 million Steam reviews across 30 languages and 2,000 top-selling titles, we select 28 games with notable cross-language rating patterns. We then apply LLM-assisted content analysis to 442,162 reviews spanning 17 languages, with human researchers guiding codebook development and interpreting the results. Our findings reveal differences in both the aspects language communities prioritize and how they evaluate them, highlighting the roles of narrative expectations, game mechanics and stability, localization quality, cultural proximity, and perceptions of developers and publishers. We also identify rare cases of cross-language consensus. This work offers empirical insights into cross-cultural game evaluation and a scalable methodological approach to multilingual content analysis that preserves human interpretation.
Innovation
Methods, ideas, or system contributions that make the work stand out.
human-AI collaborative framework
multilingual large language models (LLMs)
cross-language analysis
game reviews
content analysis
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