Evaluating the Ebb and Flow: An In-depth Analysis of Question-Answering Trends across Diverse Platforms

TL;DR

This study analyzes factors influencing response times on popular Community Question Answering platforms, examining metadata, question formulation, and user interaction, and employs machine learning to predict prompt responses.

Contribution

It provides an in-depth analysis of response time determinants and introduces predictive models for response speed based on platform data.

Findings

Correlation between response time and question metadata

Impact of question formulation on answer speed

Machine learning models can predict prompt responses

Abstract

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.