Leveraging Textual Features for Best Answer Prediction in Community-based Question Answering

TL;DR

This paper introduces ACQUA, a browser plugin that predicts the best answer in CQA sites using textual features, outperforming traditional methods by discretizing features for real-time accuracy across multiple platforms.

Contribution

The paper presents a novel content-based approach for best answer prediction that matches rating-based methods without relying on community feedback, enabling real-time application.

Findings

Achieved 84% average precision in answer prediction.

Demonstrated effectiveness across 21 StackExchange sites.

Enabled real-time predictions without needing user ratings.

Abstract

This paper addresses the problem of determining the best answer in Community-based Question Answering (CQA) websites by focussing on the content. In particular, we present a system, ACQUA [http://acqua.kmi.open.ac.uk], that can be installed onto the majority of browsers as a plugin. The service offers a seamless and accurate prediction of the answer to be accepted. Previous research on this topic relies on the exploitation of community feedback on the answers, which involves rating of either users (e.g., reputation) or answers (e.g. scores manually assigned to answers). We propose a new technique that leverages the content/textual features of answers in a novel way. Our approach delivers better results than related linguistics-based solutions and manages to match rating-based approaches. More specifically, the gain in performance is achieved by rendering the values of these features into a discretised form. We also show how our technique manages to deliver equally good results in real-time settings, as opposed to having to rely on information not always readily available, such as user ratings and answer scores. We ran an evaluation on 21 StackExchange websites covering around 4 million questions and more than 8 million answers. We obtain 84% average precision and 70% recall, which shows that our technique is robust, effective, and widely applicable.