Studying Media Events through Spatio-Temporal Statistical Analysis

Angelika Studeny 1, * Robin Lamarche-Perrin 2 Jean-Marc Vincent 1
* Corresponding author
1 MESCAL - Middleware efficiently scalable
Inria Grenoble - Rhône-Alpes, LIG - Laboratoire d'Informatique de Grenoble
Abstract : This report is written in the context of the ANR Geomedia and summarises the developement of methods of spatio-temporel statistical analysis of media events (delivrable 3.2). This documents presents on-going work on statistical modelling and statistical inference of the ANR GEOMEDIA corpus, that is a collection of international RSS news feeds. Central to this project, RSS news feeds are viewed as a representation of the information flow in geopolitical space. As such they allow us to study media events of global extent and how they affect international relations. Here we propose hidden Markov models (HMM) as an adequate modelling framework to study the evolution of media events in time. This set of models respect the characteristic properties of the data, such as temporal dependencies and correlations between feeds. Its specific structure corresponds well to our conceptualisation of media attention and media events. We specify the general model structure that we use for modelling an ensemble of RSS news feeds. Finally, we apply the proposed models to a case study dedicated to the analysis of the media attention for the Ebola epidemic which spread through West Africa in 2014.
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Angelika Studeny, Robin Lamarche-Perrin, Jean-Marc Vincent. Studying Media Events through Spatio-Temporal Statistical Analysis. [Research Report] INRIA Grenoble - Rhone-Alpes. 2015. ⟨hal-01246239⟩

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