Title
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 characterise the habits and communication patterns of a given platform and/or community. We apply the proposed framework on four popular websites: Slashdot, Barrapunto (a Spanish version of Slashdot), Meneame (a Spanish Digg-clone) and the article discussion pages of the English Wikipedia. Our results provide significant insight into understanding how discussion cascades grow and have potential applications in broader contexts such as community management or design of communication platforms.
Year
DOI
Venue
2013
10.1007/s11280-012-0162-8
World Wide Web
Keywords
DocType
Volume
discussion threads,online conversations,information cascades,preferential attachment,novelty,maximum likelihood,Slashdot,Wikipedia
Journal
16
Issue
ISSN
Citations 
5-6
1386-145X
17
PageRank 
References 
Authors
0.70
44
4
Name
Order
Citations
PageRank
Vicenç Gómez137237.88
Hilbert J. Kappen2834103.74
Nelly Litvak325925.53
Andreas Kaltenbrunner461350.64