  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.

Information Retrieval;Computation and Language;Machine Learning