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

arXiv: https://arxiv.org/abs/2309.05961 · HTML: https://mlsanity.com/paper/2309.05961/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 13 references that are in the map.
- First posted on arXiv 12 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [An Empirical Study of Obsolete Answers on Stack Overflow](https://mlsanity.com/paper/1903.12282/) (2019)
- [Duplicate Question Retrieval and Confirmation Time Prediction in Software Communities](https://mlsanity.com/paper/2309.05035/) (2023)
- [Joint Autoregressive and Graph Models for Software and Developer Social Networks](https://mlsanity.com/paper/2101.08729/) (2021)
- [Characterization and Prediction of Questions without Accepted Answers on Stack Overflow](https://mlsanity.com/paper/2103.11386/) (2021)
- [Is this bug severe? A text-cum-graph based model for bug severity prediction](https://mlsanity.com/paper/2207.00623/) (2022)
- [From Asking to Answering: Getting More Involved on Stack Overflow](https://mlsanity.com/paper/2010.04025/) (2020)

## Built on by (most cited first, 1 of 1)

- [Redefining Developer Assistance: Through Large Language Models in Software Ecosystem](https://mlsanity.com/paper/2312.05626/) (2023)

## Abstract (arXiv)

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.