EUMEPLAT
A project for: European Union (Horizon 2020 Programme)
BRIEF
The Challenge
The EUMEPLAT project aims to analyse the role of media platforms in fostering or dismantling a ´European identity´.
We assume that ´European identity´ is not a dominant identity in the new media age. People increasingly express themselves on global social media platforms owned by non-European technology companies. This study seeks to explore what the knock-on effects are of these social media platforms on European culture.
The main research question is whether or not new platforms are making European culture more or less ´European´.
FOCUS
The Approach
EUMEPLAT explores the role of media platforms in fostering or dismantling European identity and analyses indicators related to production, consumption and representation and looks for patterns on national, regional and European levels. Specifically, the project will:
• Furnish detailed knowledge about the evolution of the European media landscape;
• Come up with an operational definition of “Europeanisation”, and provide policy-makers with guidelines for addressing it as a strategic priority;
• Identify best practices in cross-European cultural circulation;
• Analyze the representation of sensitive issues (gender and immigration) in ten countries;
• Suggest counter-measures for tackling negative externalities of platformization, with an emphasis on anti-European fake news;
• Drawing on all tendencies detected, to come out with an indication of the problems to be prioritized in the future agenda.
CONCLUSIONS
The Envisioned Result
PREDICTBY
Our Role
PredictBy is responsible for multiple tasks in this project as experts in DATA SCIENCE and MACHINE LEARNING.
Our role in the project consists of conducting the following tasks:
Leading the analysis of media representations of gender and immigration using social media data by assessing how the topics of migration and gender are represented across Europe.
Coordinating the work of the 10 countries represented in the consortium.
Using social media post data samples to train Machine Learning algorithms to automatically code a larger sample of social media posts.
Using the larger sample of coded data to conduct quantitative and qualitative analysis to assess the similarities and differences in how the topics of gender and migration are represented on social media across Europe.