# Language Use Matters: Analysis of the Linguistic Structure of Question Texts Can Characterize Answerability in Quora

## TL;DR

This study analyzes linguistic features of questions on Quora to predict answerability, demonstrating that language use patterns can effectively forecast whether questions will be answered within specific time frames.

## Contribution

It introduces a linguistic analysis approach to predict question answerability on Quora, highlighting the predictive power of language use features and outperforming prior methods.

## Key findings

- Language use features predict answerability with 76.26% accuracy at 1 month.
- User language patterns alone achieve 74.18% accuracy.
- Method outperforms similar approaches by up to 39% in accuracy.

## Abstract

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.