  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.
