# On the Feasibility of Predicting Questions being Forgotten in Stack Overflow

arXiv: https://arxiv.org/abs/2110.15789 · HTML: https://mlsanity.com/paper/2110.15789/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 32 references that are in the map.
- First posted on arXiv 29 Oct 2021.

## Builds on (most cited first, 6 of 6)

- [Easy over Hard: A Case Study on Deep Learning](https://mlsanity.com/paper/1703.00133/) (2017)
- [An Empirical Study of Obsolete Answers on Stack Overflow](https://mlsanity.com/paper/1903.12282/) (2019)
- [Fit or Unfit : Analysis and Prediction of 'Closed Questions' on Stack Overflow](https://mlsanity.com/paper/1307.7291/) (2013)
- [SOTorrent: Studying the Origin, Evolution, and Usage of Stack Overflow Code Snippets](https://mlsanity.com/paper/1809.02814/) (2018)
- [Want a Good Answer? Ask a Good Question First!](https://mlsanity.com/paper/1311.6876/) (2013)
- [Chaff from the Wheat : Characterization and Modeling of Deleted Questions on Stack Overflow](https://mlsanity.com/paper/1401.0480/) (2014)

## Abstract (arXiv)

For their attractiveness, comprehensiveness and dynamic coverage of relevant topics, community-based question answering sites such as Stack Overflow heavily rely on the engagement of their communities: Questions on new technologies, technology features as well as technology versions come up and have to be answered as technology evolves (and as community members gather experience with it). At the same time, other questions cease in importance over time, finally becoming irrelevant to users. Beyond filtering low-quality questions, "forgetting" questions, which have become redundant, is an important step for keeping the Stack Overflow content concise and useful. In this work, we study this managed forgetting task for Stack Overflow. Our work is based on data from more than a decade (2008 - 2019) - covering 18.1M questions, that are made publicly available by the site itself. For establishing a deeper understanding, we first analyze and characterize the set of questions about to be forgotten, i.e., questions that get a considerable number of views in the current period but become unattractive in the near future. Subsequently, we examine the capability of a wide range of features in predicting such forgotten questions in different categories. We find some categories in which those questions are more predictable. We also discover that the text-based features are surprisingly not helpful in this prediction task, while the meta information is much more predictive.