Modeling of temporal fluctuation scaling in online news network with independent cascade model

Jan Chołoniewski , Julian Sienkiewicz , Leban Gregor , Janusz Hołyst

Abstract

We show that activity of online news outlets follows a temporal fluctuation scaling law and we recover this feature using an independent cascade model augmented with a varying hype parameter representing a viral potential of an original article. We use the Event Registry platform to track activity of over 10,000 news outlets in 11 different topics in the course of the year 2016. Analyzing over 22,000,000 articles, we found that fluctuation scaling exponents α depend on time window size ∆ in a characteristic way for all the considered topics – news outlets activities are partially synchronized for ∆ > 15min with a cross-over for ∆ = 1day. The proposed model was run on several synthetic network models as well as on a network extracted from the real data. Our approach discards timestamps as not fully reliable observables and focuses on co-occurrences of publishers in cascades of similarly phrased news items. We make use of the Event Registry news clustering feature to find correlations between content published by news outlets in order to uncover common information propagation paths in published articles and to estimate weights of edges in the independent cascade model. While the independent cascade model follows the fluctuation scaling law with a trivial exponent α = 0.5, we argue that besides the topology of the underlying cooperation network a temporal clustering of articles with similar hypes is necessary to qualitatively reproduce the fluctuation scaling observed in the data.
Author Jan Chołoniewski (FP / LPESS)
Jan Chołoniewski,,
- Center of Physics in Economics and Social Sciences
, Julian Sienkiewicz (FP / LPESS)
Julian Sienkiewicz,,
- Center of Physics in Economics and Social Sciences
, Leban Gregor
Leban Gregor,,
-
, Janusz Hołyst (FP / LPESS)
Janusz Hołyst,,
- Center of Physics in Economics and Social Sciences
Corporate authorWydział Fizyki Politechniki Warszawskiej (Wydz.Fizyki PW)
Journal seriesPhysica A-Statistical Mechanics and Its Applications, ISSN 0378-4371, (N/A 70 pkt)
Issue year2019
No523
Pages129-144
Publication size in sheets0.75
Keywords in Polish-
Keywords in EnglishFluctuation scaling , Complex systems , Complex networks , Online media , Agent-based modelling
Keywords in original languageFluctuation scaling , Complex systems , Complex networks , Online media , Agent-based modelling
ASJC Classification3104 Condensed Matter Physics; 2613 Statistics and Probability
Abstract in Polish-
Abstract in original languageWe show that activity of online news outlets follows a temporal fluctuation scaling law and we recover this feature using an independent cascade model augmented with a varying hype parameter representing a viral potential of an original article. We use the Event Registry platform to track activity of over 10,000 news outlets in 11 different topics in the course of the year 2016. Analyzing over 22,000,000 articles, we found that fluctuation scaling exponents α depend on time window size ∆ in a characteristic way for all the considered topics – news outlets activities are partially synchronized for ∆ > 15min with a cross-over for ∆ = 1day. The proposed model was run on several synthetic network models as well as on a network extracted from the real data. Our approach discards timestamps as not fully reliable observables and focuses on co-occurrences of publishers in cascades of similarly phrased news items. We make use of the Event Registry news clustering feature to find correlations between content published by news outlets in order to uncover common information propagation paths in published articles and to estimate weights of edges in the independent cascade model. While the independent cascade model follows the fluctuation scaling law with a trivial exponent α = 0.5, we argue that besides the topology of the underlying cooperation network a temporal clustering of articles with similar hypes is necessary to qualitatively reproduce the fluctuation scaling observed in the data.
DOIDOI:10.1016/j.physa.2019.02.035
URL https://arxiv.org/abs/1810.06425
ProjectReverse EngiNeering of sOcial Information pRocessing. Project leader: Hołyst Janusz, , Phone: 22 234 7133, application date 28-04-2015, start date 01-01-2016, end date 31-12-2019, 691152, Implemented
WF Horizon 2020 [Horyzont 2020]
Languageen angielski
File
1810.06425.pdf 1.31 MB
Score (nominal)70
Score sourcejournalList
ScoreMinisterial score = 70.0, 17-01-2020, ArticleFromJournal
Publication indicators Scopus Citations = 0; WoS Citations = 0; Scopus SNIP (Source Normalised Impact per Paper): 2016 = 1.324; WoS Impact Factor: 2018 = 2.5 (2) - 2018=2.464 (5)
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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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