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

TL;DR

This 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.

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.

Figures (1)

• Figure 1: (left): a. Flesch Reading Ease, ($2^{nd}$ from left): b. Coleman-Liau index, (middle): c. Automated readability index scores for $Q_{top}$ and $Q_{bottom}$ questions. ($2^{nd}$ from right): d. Average Clustering Coefficient, (right): e. Average Degree of AAG of $Q_{top}$ and $Q_{bottom}$.