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：社区问答平台因其能够快速响应用户查询而日益受到欢迎。这些响应的迅速程度取决于查询特定因素和用户相关元素的结合。本文在六个通过其卓越回答速度识别出的高人气社区问答平台背景下，对这些促成因素进行了审视。我们的研究揭示了首次回答问题所需时间与多个变量之间的相关性：元数据、问题的表述方式以及用户之间的互动水平。此外，通过运用传统机器学习模型分析这些元数据及用户互动模式，我们致力于预测哪些查询能够迅速获得初步响应。