Transition due to preferential cluster growth of collective emotions in online communities

Anna Chmiel , Janusz Hołyst

Abstract

We consider a preferential cluster growth in a stochastic model describing the dynamics of a binary Markov chain with an additional long-range memory. The model is driven by data describing emotional patterns observed in online community discussions with binary states corresponding to emotional valences. Numerical simulations and approximate analytical calculations show that the pattern of frequencies depends on a preference exponent related to the memory strength in our model. For low values of this exponent in the majority of simulated discussion threads both emotions are observed with similar frequencies. When the exponent increases an ordered phase emerges in the majority of threads, i.e., only one emotion is represented from a certain moment. Similar changes are observed with increase of a single-step Markov memory value. The transition becomes discontinuous in the thermodynamical limit when discussions are infinitely long and even an infinitely small preference exponent leads to ordered behavior in each discussion thread. Numerical simulations are in a good agreement with the approximated analytical formula. The model resembles a dynamical phase transition observed in other Markov models with a long memory where persistent dynamics follows from a transition to a superdiffusion phase. The ordered patterns predicted by our model have been found in the Blog06 dataset although their number is limited by fluctuations and sentiment classification errors. © 2013 American Physical Society.
Author Anna Chmiel (FP / LPESS)
Anna Chmiel,,
- Center of Physics in Economics and Social Sciences
, Janusz Hołyst (FP / LPESS)
Janusz Hołyst,,
- Center of Physics in Economics and Social Sciences
Journal seriesPhysical Review E, ISSN 1539-3755
Issue year2013
Vol87
No2
Pages022808-1-022808-9
Publication size in sheets0.3
Keywords in EnglishA transitions; Analytical formulas; Approximate analytical; Binary state; Cluster growth; Dynamical phase transition; Emotional patterns; Emotional valences; Long memories; Long-range memory; Markov model; Memory strength; On-line communities; Ordered behavior; Ordered phase; Sentiment classification; Single-step; Superdiffusion; Thermodynamical limit, Computer simulation; Digital storage; Dynamics; Markov processes; Online systems; Websites, Phase transitions
ASJC Classification3104 Condensed Matter Physics; 2613 Statistics and Probability; 3109 Statistical and Nonlinear Physics
DOIDOI:10.1103/PhysRevE.87.022808
URL http://www.scopus.com/record/display.url?eid=2-s2.0-84874525718&origin=resultslist&sort=plf-f&src=s&st1=Transition+due+to+preferential+cluster+growth+of+collective+emotions+in+online+communities&sid=2368C1034DE8C57C58BFC2458F28FF9C.mw4ft95QGjz1tIFG9A1uw%3a40&sot=b&sdt=b&sl=105&s=TITLE-ABS-KEY%28Transition+due+to+preferential+cluster+growth+of+collective+emotions+in+online+communities%29&relpos=0&relpos=0&citeCnt=3&searchTerm=TITLE-ABS-KEY%28Transition+due+to+preferential+cluster+growth+of+collective+emotions+in+online+communities%29#
ProjectCollective Emotions in Cyberspace . Project leader: Hołyst Janusz, , Phone: 22 234 7133, start date 01-02-2009, end date 31-07-2013, FP7 Grant Agreement 231323, Completed
WF 7 Framework Programme (7 FP) [7 Program Ramowy (7 PR)]
LanguageEnglish
File
3.pdf 2.13 MB
Score (nominal)35
Score sourcejournalList
ScoreMinisterial score = 35.0, 24-09-2020, ArticleFromJournal
Ministerial score (2013-2016) = 35.0, 24-09-2020, ArticleFromJournal
Publication indicators WoS Citations = 3; GS Citations = 8.0; Scopus Citations = 3; Scopus SNIP (Source Normalised Impact per Paper): 2014 = 1.123; WoS Impact Factor: 2013 = 2.326 (2) - 2013=2.302 (5)
Citation count*8 (2020-09-01)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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