# Predicting Question Quality on StackOverflow with Neural Networks

arXiv: https://arxiv.org/abs/2404.14449 · HTML: https://mlsanity.com/paper/2404.14449/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 16 references that are in the map.
- First posted on arXiv 20 Apr 2024.

## Builds on (most cited first, 2 of 2)

- [500+ Times Faster Than Deep Learning (A Case Study Exploring Faster Methods for Text Mining StackOverflow)](https://mlsanity.com/paper/1802.05319/) (2018)
- [Support-BERT: Predicting Quality of Question-Answer Pairs in MSDN using Deep Bidirectional Transformer](https://mlsanity.com/paper/2005.08294/) (2020)

## Abstract (arXiv)

The wealth of information available through the Internet and social media is unprecedented. Within computing fields, websites such as Stack Overflow are considered important sources for users seeking solutions to their computing and programming issues. However, like other social media platforms, Stack Overflow contains a mixture of relevant and irrelevant information. In this paper, we evaluated neural network models to predict the quality of questions on Stack Overflow, as an example of Question Answering (QA) communities. Our results demonstrate the effectiveness of neural network models compared to baseline machine learning models, achieving an accuracy of 80%. Furthermore, our findings indicate that the number of layers in the neural network model can significantly impact its performance.