Community Question Answering (CQA) platforms steadily gain popularity as they provide users with fast responses to their queries. The swiftness of these responses is contingent on a mixture of query-specific and user-related elements. This paper scrutinizes these contributing factors within the context of six highly popular CQA platforms, identified through their standout answering speed. Our investigation reveals a correlation between the time taken to yield the first response to a question and several variables: the metadata, the formulation of the questions, and the level of interaction among users. Additionally, by employing conventional machine learning models to analyze these metadata and patterns of user interaction, we endeavor to predict which queries will receive their initial responses promptly.

翻译：社区问答（CQA）平台因能快速响应用户查询而日益普及。响应速度受查询特定因素和用户相关元素的共同影响。本文以六类响应速度突出的高人气CQA平台为背景，深入剖析这些关键因素。研究发现，问题获得首次回答所需时间与元数据、问题表述方式及用户互动程度等变量存在关联。此外，通过采用传统机器学习模型分析元数据与用户交互模式，我们致力于预测哪些查询能快速获得初始响应。