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Post-Doctoral position on data based opinion dynamics models in France


Big Data technologies are changing the informational environment in which people live, keeping traces of their activities, merging behavior and decision-making processes of social actors. Based on textual data obtained from different media, we will study emerging patterns in social actions, focusing on opinion diffusion and language evolution.

In the framework of this project, the team has access to the historical database of The New York Times, which gathers documents covering over more than 150 years. These can be combined with modern data sources like Twitter, in order to develop comparative studies on opinion and language dynamics.


By analyzing large bases of textual data, we aim at extracting patterns of relevant information, in order to construct data-based models of opinion formation and evolution. This implies addressing, among others, the following questions:

  • How can relevant information be obtained from the Big Data sources at disposal in order to build data-based models of opinion dynamics? What is the role of the metadata in this endeavor?
  • How can one infer the network of social contacts on the basis of these data?
  • How can one capture opinion dynamics? Which parameters characterize the dynamical process? How these parameters depend on the type of media where opinion diffuses?

More specifically, we are interested in understanding how scientific topics of high societal impact migrate from the channels restricted to scientists to the public media and how this diffusion contributes to fashion the public opinion on those particular topics. The focus of the research work will also be influenced by the profile of the selected candidate.

LabCergy-Pontoise University (west suburbs of Paris)
Duration/Period1 year (renewable)
Deadline15/05/2019 0:00
Keywordsopinion dynamics models; dynamical systems; opinion mining; social networks; natural language processing;

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