Thematic report
This report examines what research tells us about changes in legacy and online news media, and what can be done to promote information integrity and a democratic public sphere.
The analysis covers news media market and platformization, motivations for mis- and disinformation, trust in legacy and online news outlets, news consumption habits, and the role of information weaponization in polarization.
It shows how:
Thematic reports
This chapter begins with an introduction that frames the central themes of the report, covers the key concepts and definitions, delves into the challenges facing democracies focusing on mis- and disinformation, acknowledges the limitations of the report and provides an outline of the report.
What does research tell us about changes in legacy and online news media and what can be done to promote information integrity and a democratic public sphere?
What does research tell us on the properties of AI systems (machine learning algorithms), their role in content governance, and internationally protected human rights?
What does research tell us on the power of big tech companies and approaches to governing data extraction and use and influences on political deliberation?
What does research tell us about changes in legacy and online news media and what can be done to promote information integrity and a democratic public sphere?
What does research tell us about changes in legacy and online news media and what can be done to promote information integrity and a democratic public sphere?
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2025 - Observatory on Information & Democracy
This map represents a statistical summary of the thematic content of the report. The network graph represents relations between the words in the report, placing them closer to each other the more they are related. The bigger the node, the more present the word is, signalling its role in defining what the report is about. The colors represent words that are closely related to each other and can be interpreted as a topic.
The map is generated by the OID using GarganText – developed by the CNRS Institute of Complex Systems –on the basis of the repot’s text. Starting from a co-occurrence matrix generated from report’s text, GarganText forms a network where words are connected if they are likely to occur together. Clustering is conducted based on the Louvain community detection method, and the visualisation is generated using the Force Atlas 2 algorithm.