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提问而日益普及。这种响应速度取决于查询特定因素与用户相关要素的综合作用。本文以六个热门CQA平台为背景，基于其突出的回答速度特征，深入剖析了这些贡献因素。研究表明，问题获得首次回复所需时间与多项变量存在相关性：元数据、问题表述方式以及用户互动水平。此外，我们采用传统机器学习模型分析这些元数据和用户交互模式，旨在预测哪些查询将获得快速初始回复。