A likelihood-based framework for the analysis of discussion threads

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.

31 pages, 12 figures, journal

References in corpus (9)

• Emergence of scaling in random networks
• The Dynamics of Viral Marketing
• Novelty and Collective Attention
• Measuring preferential attachment for evolving networks
• Information diffusion epidemics in social networks
• Modeling the structure and evolution of discussion cascades
• Structure and evolution of online social relationships: Heterogeneity in warm discussions
• Directedness of information flow in mobile phone communication networks
• Stratification in the Preferential Attachment Network

Cited by in corpus (10)

• The anatomy of Reddit: An overview of academic research
• Conspiracy vs science: A large-scale analysis of online discussion cascades
• Modelling structure and predicting dynamics of discussion threads in online boards
• Emergence of online communities: Empirical evidence and theory
• The dynamic nature of conflict in Wikipedia
• Differences in structure and dynamics of networks retrieved from dark and public web forums
• Wikipedia edition dynamics
• Learning the mechanisms of network growth
• Action selection in growing state spaces: Control of Network Structure Growth
• Negative Ties Highlight Hidden Extremes in Social Media Polarization