Analyzing Answers in Threaded Discussions using a Role-Based Information Network

TL;DR

This paper introduces a role-based network model to analyze threaded discussions, classifying message types and user roles to identify the most useful answers, achieving a correlation with human rankings.

Contribution

It presents a novel network approach that captures message and user roles in online discussions to improve answer quality identification.

Findings

Achieved an MRR score of 0.67 in ranking answers.

Effectively classified message roles and user intent.

Identified influential messages using B-centrality measures.

Abstract

Online discussion boards are an important medium for collaboration. The goal of our work is to understand how messages and individual discussants contribute to Q&A discussions. We present a novel network model for capturing in-formation roles of messages and discussants, and show how we identify useful answers to the initial question. We first classify information seeking or information providing roles of messages, such as question, answer or acknowledgement. We also identify user intent in the discussion as an information seeker or a provider. We capture such role information within a reply-to discussion network, and identify messages that answer seeker questions and how answeres are acknowledged. Message influences are analyzed using B-centrality measures. User influences across different threads are combined with message influences. We use the combined score in identifying the most useful answer in the thread. The resulting ranks correlate with human provided ranks with an MRR score of 0.67.