Modeling Tag Prediction based on Question Tagging Behavior Analysis of CommunityQA Platform Users

TL;DR

This paper analyzes user tagging behavior across multiple community QA platforms to develop a neural network model that accurately predicts both popular and specific tags for questions, improving information organization.

Contribution

It introduces a comprehensive analysis of tagging behavior and a flexible neural architecture for improved tag prediction across diverse communities.

Findings

The model effectively predicts both popular and granular tags.

Tagging behavior exhibits common properties across communities.

Experimental results demonstrate high prediction accuracy.

Abstract

In community question-answering platforms, tags play essential roles in effective information organization and retrieval, better question routing, faster response to questions, and assessment of topic popularity. Hence, automatic assistance for predicting and suggesting tags for posts is of high utility to users of such platforms. To develop better tag prediction across diverse communities and domains, we performed a thorough analysis of users' tagging behavior in 17 StackExchange communities. We found various common inherent properties of this behavior in those diverse domains. We used the findings to develop a flexible neural tag prediction architecture, which predicts both popular tags and more granular tags for each question. Our extensive experiments and obtained performance show the effectiveness of our model