  The diffusion of information plays a crucial role in a society,
characterizing the diffusion process is challenging because it is highly
non-stationary and varies with the media type. To understand the spreading of
newspaper news in Argentina, we collected data from more than 27000 articles
published in six main provinces during four months. We classified the articles
into 20 thematic axes and obtained a set of 120 time series that capture daily
newspaper attention on different topics in different provinces. To analyze the
data we use a point process approach. For each topic, $n$, and for all pairs of
provinces, $i$ and $j$, we use two measures to quantify the synchronicity of
the events, $Q_s(i,j)$, which quantifies the number of events that occur almost
simultaneously in $i$ and $j$, and $Q_a(i,j)$, which quantifies the direction
of news spreading. We also analyze the dataset using well-known measures to
detect correlations and dependencies, computed from the raw time series:
undirected measures (linear cross-correlation, $CC$, and nonlinear mutual
information, $MI$) and directed measures (linear Granger causality, $GC$, and
nonlinear Transfer entropy, $TE$). Our analysis unveils how fast the
information diffusion process is, as high values of $Q_{s}$, $CC$, and $MI$
reveal pairs of provinces with very similar and almost simultaneous temporal
variations of media attention. On the other hand, $GC$ and $TE$ do not perform
well in this context because they often return opposite directions of
information transfer. We interpret this as due to three main factors: the
characteristics of the data, which is highly non-stationary, the
characteristics of the information diffusion process, which is very fast and
probably acts at a sub-resolution time scale, and the action of large media
companies that act as global, external drivers of information dissemination.
