A likelihood-based framework for the analysis of discussion threads

TL;DR

This paper introduces a likelihood-based generative model framework to analyze the structure and evolution of online discussion threads, capturing factors like popularity, novelty, and bias to reply to the originator.

Contribution

It presents a novel probabilistic approach to model discussion thread growth, enabling detailed analysis of communication patterns and platform-specific behaviors.

Findings

Effective characterization of discussion dynamics

Quantitative insights into reply biases

Framework applicable to various social platforms

Abstract

Online discussion threads are conversational cascades in the form of posted messages that can be generally found in social systems that comprise many-to-many interaction such as blogs, news aggregators or bulletin board systems. We propose a framework based on generative models of growing trees to analyse the structure and evolution of discussion threads. We consider the growth of a discussion to be determined by an interplay between popularity, novelty and a trend (or bias) to reply to the thread originator. The relevance of these features is estimated using a full likelihood approach and allows to characterize the habits and communication patterns of a given platform and/or community.