TWIN4DEM interview WP3 | Using Computational Text Analysis to Detect Democratic Backsliding

TWIN4DEM interview WP3 | Using Computational Text Analysis to Detect Democratic Backsliding

What happens when democratic institutions begin to weaken, and how can we detect it early?

In this TWIN4DEM interview, the Work Package 3 leader discusses how computational text analysis and AI-driven methodologies can help identify patterns of executive aggrandisement, democratic backsliding, and societal responses across Europe.

WP3 explores large-scale political and public discourse through innovative computational approaches, contributing to the development of evidence-based tools for safeguarding democracy, transparency, and the rule of law.

The interview covers:
• The objectives and scope of WP3
• Computational approaches to democracy research
• Detecting executive aggrandisement through text analysis
• Monitoring political and societal reactions
• The role of AI and digital methods in social sciences