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

arXiv: https://arxiv.org/abs/2307.01420 · HTML: https://mlsanity.com/paper/2307.01420/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 44 references that are in the map.
- First posted on arXiv 4 Jul 2023.

## Builds on (most cited first, 8 of 8)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Identifying Unclear Questions in Community Question Answering Websites](https://mlsanity.com/paper/1901.06168/) (2019)
- [TagRec: Automated Tagging of Questions with Hierarchical Learning Taxonomy](https://mlsanity.com/paper/2107.10649/) (2021)
- [Expert Finding in Legal Community Question Answering](https://mlsanity.com/paper/2201.07667/) (2022)
- [Community Question Answering Entity Linking via Leveraging Auxiliary Data](https://mlsanity.com/paper/2205.11917/) (2022)
- [Is Stack Overflow Overflowing With Questions and Tags](https://mlsanity.com/paper/1508.03601/) (2015)
- [Topic Community Based Temporal Expertise for Question Routing](https://mlsanity.com/paper/2207.01753/) (2022)
- [Simplifying Sparse Expert Recommendation by Revisiting Graph Diffusion](https://mlsanity.com/paper/2208.02438/) (2022)

## Abstract (arXiv)

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