Predicting Question Quality on StackOverflow with Neural Networks

TL;DR

This paper evaluates neural network models for predicting question quality on Stack Overflow, demonstrating improved accuracy over baseline models and highlighting the influence of network depth on performance.

Contribution

It introduces neural network-based methods for assessing question quality on Stack Overflow, showing their effectiveness over traditional machine learning approaches.

Findings

Neural networks achieved 80% accuracy in predicting question quality.

Deeper neural networks significantly improved prediction performance.

Neural models outperformed baseline machine learning models.

Abstract

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.