This project is based on the automatic analysis of news related to food security issues in French language media (national and local).
Text-mining network analysis tools are used to identify the key themes discussed in the press at a given period. Each article is then associated with these automatically reconstructed topics whether they correspond to concerns expressed at the local level, or general statements and action at national/international level.
These news items are also geo-located both by the origin of the story and the places mentioned in the story enabling to map how a given theme or issue is distributed over the world.
Moreover, themes identified at successive time steps are reconnected into streams of content. A stream visualization illustrates how topics articulate through time.
An online interface allows to visualize these maps, themes and news entries and to answer questions such as : Is an issue – concerning for example the impact of climatic change on food security – attracting more attention with time? How this specific issue relates with contiguous subjects (use of biofuel for example) ? Does the climatic change issue observed at a given time stem from, possibly various, past issue framing or is it a completely emergent topic ?