Assessing the Performance of Question-and-Answer Communities Using Survival Analysis
This provides a more advanced evaluation tool for QA platform designers and managers, though it is incremental over existing metrics.
The paper tackled the problem of assessing performance in question-and-answer communities by applying survival analysis to Stack Exchange data, resulting in a methodology that identifies predictive factors and enables systematic comparisons across communities.
Question-&-Answer (QA) websites have emerged as efficient platforms for knowledge sharing and problem solving. In particular, the Stack Exchange platform includes some of the most popular QA communities to date, such as Stack Overflow. Initial metrics used to assess the performance of these communities include summative statistics like the percentage of resolved questions or the average time to receive and validate correct answers. However, more advanced methods for longitudinal data analysis can provide further insights on the QA process, by enabling identification of key predictive factors and systematic comparison of performance across different QA communities. In this paper, we apply survival analysis to a selection of communities from the Stack Exchange platform. We illustrate the advantages of using the proposed methodology to characterize and evaluate the performance of QA communities, and then point to some implications for the design and management of QA platforms.