Predicting Question Quality on StackOverflow with Neural Networks
This addresses the challenge of filtering irrelevant content for users of QA communities like Stack Overflow, though it appears incremental as it applies existing neural network methods to this specific domain.
The paper tackled the problem of identifying relevant information on Stack Overflow by evaluating neural network models to predict question quality, achieving 80% accuracy and showing that the number of layers significantly impacts performance.
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.