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）平台因其能为用户提供快速查询响应而逐渐普及。这些响应的速度取决于查询特定因素和用户相关元素的混合。本文在六个高人气CQA平台的情境下审视这些促成因素，这些平台以其出色的回答速度脱颖而出。我们的研究揭示了首次响应时间与若干变量之间的相关性：元数据、问题的表述方式以及用户间的互动水平。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能迅速收到初始响应。