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

TL;DR

This paper investigates the fluctuation scaling law in online news activity, modeling it with an augmented independent cascade model that incorporates viral potential, and analyzing real-world data from over 10,000 outlets.

Contribution

It introduces a novel independent cascade model with a hype parameter to replicate observed fluctuation scaling in online news dissemination.

Findings

Fluctuation scaling exponents depend on time window size.

News outlets show partial synchronization for windows larger than 15 minutes.

The model reproduces the observed scaling law when including temporal clustering of articles.

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 $\alpha$ depend on time window size $\Delta$ in a characteristic way for all the considered topics -- news outlets activities are partially synchronized for $\Delta>15\mathrm{min}$ with a cross-over for $\Delta=1\mathrm{day}$. 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 $\alpha=0.5$, 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.