AI 中文总结
该研究针对在线讨论论坛自由回复结构难挖掘洞见的问题，通过分析论坛讨论、构建框架与探针、访谈21名研究者，为LLM支持的社区意义建构工具提供设计建议。
AI 中文摘要
在线讨论论坛让不同背景的人们分享想法、反馈和观点，这些自发的讨论可帮助研究者理解社区的集体观点，但由于其自由形式的回复结构，往往难以从中挖掘洞见。大语言模型（LLM）支持定性文本分析，但可能与研究者的分析意图不一致，且会遗漏关键洞见。为了为论坛意义建构工具的设计提供考量，我们手动分析了一个论坛讨论，从相关文献中综合出探索性分析框架，构建了一个设计探针，并访谈了21名研究者，以揭示他们对集体讨论的LLM表示所感知到的机会和障碍。我们为社区意义建构工具提供了建议，以支持基于原始用户数据的灵活分析目标，实现后续研究流程，同时平衡匿名自由表达与评论者背景信息的需求。
英文摘要
Online discussion forums enable people from diverse backgrounds to share ideas, feedback, and perspectives. These organic discussions can help researchers understand communities' collective viewpoints, but insights are often difficult to uncover given their freeform reply structure. Large language models (LLMs) support qualitative text analysis but can misalign with researchers' analytical intent and miss key insights. To inform design considerations for forum sensemaking tools, we manually analyzed a forum discussion, synthesized an exploratory analysis framework from relevant literature, built a design probe, and interviewed 21 researchers to uncover perceived opportunities and barriers with LLM representations of collective discussions. We provide recommendations for community sensemaking tools to support flexible analytical goals grounded in raw user data and enable follow-up research processes, while balancing anonymous free expression with the desire for contextual information on commenters.
CommentsAccepted to ACM Collective Intelligence Conference, 2026
