解读游戏评论的巴别塔：一种用于大规模跨语言游戏评论分析的人机协作方法

中文摘要 AI 辅助

我们提出了一种使用人机协作框架对游戏评论进行大规模跨语言分析的方法，该框架将定量筛选与多语言大语言模型（LLMs）相结合。我们从涵盖30种语言、2000款畅销游戏的1700万条Steam评论出发，筛选出28款具有显著跨语言评分模式的游戏。随后，我们应用LLM辅助的内容分析来处理跨越17种语言的442,162条评论，由人类研究人员指导编码表的制定并解读结果。我们的研究发现，不同语言社区在优先关注的方面以及评价方式上均存在差异，凸显了叙事期望、游戏机制与稳定性、本地化质量、文化亲近性以及对开发商和发行商的看法所起的作用。我们还识别出罕见的跨语言共识案例。这项工作为跨文化游戏评价提供了实证见解，并提供了一种保留人类解读的可扩展多语言内容分析方法论。

英文摘要

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.

发表机构

• Boston University（波士顿大学）

机构由 AI 辅助整理，请以论文原文为准。