Language Use Matters: Analysis of the Linguistic Structure of Question Texts Can Characterize Answerability in Quora
This addresses the issue of unanswered questions for Quora users, though it is incremental as it builds on prior work with specific improvements.
The paper tackled the problem of predicting whether questions on Quora will be answered by analyzing linguistic structure, achieving accuracies of 76.26% for a 1-month period and 68.33% for a 3-month period.
Quora is one of the most popular community Q&A sites of recent times. However, many question posts on this Q&A site often do not get answered. In this paper, we quantify various linguistic activities that discriminates an answered question from an unanswered one. Our central finding is that the way users use language while writing the question text can be a very effective means to characterize answerability. This characterization helps us to predict early if a question remaining unanswered for a specific time period t will eventually be answered or not and achieve an accuracy of 76.26% (t = 1 month) and 68.33% (t = 3 months). Notably, features representing the language use patterns of the users are most discriminative and alone account for an accuracy of 74.18%. We also compare our method with some of the similar works (Dror et al., Yang et al.) achieving a maximum improvement of ~39% in terms of accuracy.