Description
Date depot: 1 janvier 1900
Titre: Opinion Dynamics and Political Conflicts in the Media - A Complex Network Perspective
Directrice de thèse:
Clémence MAGNIEN (LIP6)
Encadrant :
Robin LAMARCHE-PERRIN (LIP6)
Domaine scientifique: Sciences et technologies de l'information et de la communication
Thématique CNRS : Non defini
Resumé:
This PhD position will be conducted in the Complex Networks team of the Computer Science Laboratory of Paris 6 (LIP6, UMR 7606), and in collaboration with other partners of the European H2020 ODYCCEUS project 'Opinion Dynamics and Cultural Conflicts in European Space' that started in January 2017 (https://www.odycceus.eu/).
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Social media and the digitisation of news are having far-reaching effects on the way individuals and communities communicate, organise, and express themselves. Can the information circulating on these platforms be exploited to better understand and analyse the enormous problems facing our contemporary society? Could this help us to better monitor the growing number of social crises due to cultural differences and diverging world-views? Studying the structure of debates in the public sphere requires sophisticated methods for the analysis of information flows between individuals. How is information shaped and broadcasted by mass media? How to describe the way opinions are discussed in social media? Debates are often represented as complex entanglements of such social interactions, embedded in space and time, and displaying a multilevel structure: From individual to institutional discourses; From regional to international matters; From the fast dynamics of media 'buzzes' to the slower dynamics of social controversies.
To address these challenging issues, this PhD position aims at developing new methods for the analysis of multidimensional and multilevel networks in social sciences. First, by building on recent work in {dynamical graph theory} regarding the 'link stream' representation of evolving networks [4], which provides a novel and intuitive formalism for the spatio-temporal description of social interactions by focusing on their causal structure (who interacts with whom, when) and concealing for a moment their content (how, why, about what). Second, by integrating recent developments in {graph compression} [3], which builds on information-theoretical data compression [2], to provide a macroscopic perspective on such interaction structures and thus achieve a global understanding of complex interaction patterns.
Doctorant.e: Botterman Hong-Lan