On the Feasibility of Predicting Questions being Forgotten in Stack Overflow
This work addresses the challenge of maintaining concise and useful content on community-based Q&A sites like Stack Overflow by predicting forgotten questions, though it is incremental as it builds on existing data analysis and prediction methods.
The study tackled the problem of identifying questions on Stack Overflow that become irrelevant over time, analyzing over 18.1 million questions from 2008-2019 and finding that meta-information features are more predictive than text-based features for this task.
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.