Point process analysis of geographical diffusion of news in Argentina

TL;DR

This study analyzes how news spreads across Argentine provinces using point process models, revealing rapid diffusion patterns and limitations of traditional causality measures in non-stationary, fast-changing media data.

Contribution

It introduces a point process approach to quantify news diffusion across regions and compares it with traditional causality measures, highlighting the method's effectiveness in non-stationary contexts.

Findings

Identifies rapid, almost simultaneous news spread between provinces.

Traditional measures like Granger Causality often give conflicting directions.

Point process measures effectively capture diffusion speed and patterns.

Abstract

The diffusion of information plays a crucial role in a society, affecting its economy and the well-being of the population. 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 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. Our analysis unveils how fast the information diffusion process is, showing pairs of provinces with very similar and almost simultaneous temporal variations of media attention. On the other hand, we also calculate other measures computed from the raw time series, such as Granger Causality and Transfer Entropy, which do not perform well in this context because they often return opposite directions of information transfer. We interpret this as due to different factors such as the characteristics of the data, which is highly non-stationary and the features of the information diffusion process, which is very fast and probably acts at a sub-resolution time scale.