Answers or no answers: Studying question answerability in Stack Overflow

Some questions posted in community question answering sites (CQAs) fail to attract a single answer. To address the growing volumes of unanswered questions in CQAs, the objective of this paper is two-fold. First, it aims to develop a conceptual framework known as the Quest-for-Answer to explain why some questions in CQAs draw answers while others remain ignored. The framework suggests that the answerability of questions depends on both metadata and content. Second, the paper attempts to empirically validate the Quest-for-Answer framework through a case study of Stack Overflow. A total of 3000 questions divided equally between those answered and unanswered were used for analysis. The Quest-for-Answer framework yielded generally promising results. With respect to metadata, asker’s popularity, participation and asking time of questions were found to be significant in predicting if answers would be forthcoming. With respect to content, level of details, specificity, clarity and the socio-emotional value of questions were significant in enhancing or impeding responses.

Extended truncated Inverse Gaussian–Poisson model

The inverse Gaussian–Poisson mixture model is very useful when modelling highly skewed non-negative integer data in fields as diverse as linguistics, ecology, market research, bibliometry, engineering and insurance. When using this statistical model on the frequency of word or species frequency data, one typically truncates its sample space at zero to accommodate for the ignorance about the number of words or species that are not observed. In this paper, we show that by truncating the sample space of the inverse Gaussian–Poisson model, one is allowed to extend its parameter space and in that way improve its fit when the frequency of one is larger and the right tail is heavier than is allowed by the unextended model. By fitting the extended model to word frequency count data, we find many instances where the maximum likelihood estimates fall in the extension of the parameter space.