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）平台因其能够为用户查询提供快速响应而日益普及。响应的速度取决于查询特定要素与用户相关要素的综合作用。本文在六个通过其突出回答速度甄选出的高人气CQA平台背景下，审视了这些影响因素。我们的研究揭示了问题获得首次答复所需时间与若干变量之间的关联，这些变量包括：元数据、问题表述方式以及用户间的互动程度。此外，通过运用传统机器学习模型分析这些元数据和用户互动模式，我们试图预测哪些查询将迅速获得初步回应。