  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.
