The PRODIGI project produced substantial theoretical, methodological, and empirical contributions across its four objectives.
On the theoretical front, the project developed a comprehensive framework for understanding how digital technologies affect political communication, participation, and policy-making, with issue definition as the central arena of contestation. This framework was applied to content moderation, showing how Trump's 2021 deplatforming transformed moderation from a technical practice into a politically polarized public issue. The project also advanced theoretical understanding of how narratives around generative AI shape research priorities and policy agendas, and documented how problem definition around AI is increasingly internalized within technical fields rather than driven by social science perspectives.
Methodologically, the project delivered two major innovations. First, it demonstrated that large language models outperform human crowd workers in text-annotation tasks and developed practical methods for fine-tuning open-source models. Second, the project built infrastructure for conducting controlled experiments in simulated social media environments with AI agents, replicating platforms like WhatsApp and Instagram through an easy-to-use configuration interface.
These tools enabled systematic substantive analysis of technology governance. The project showed that sustained negative media coverage predicted major changes in platform content moderation policies, while the actual electoral impact of generative AI has been overstated. In the European context, longitudinal analysis of news media revealed a shift from innovation-oriented to risk and ethics frames coinciding with the development of the EU AI Act, with framing varying systematically along ideological lines in the European Parliament.
Experimental work provided evidence on how digital communication shapes public opinion. Field experiments demonstrated that following reliable news outlets on social media increases knowledge and trust in journalists. Contrary to widespread concerns, existential AI risk narratives did not reduce attention to immediate harms, while labeling content as AI-generated triggered skepticism bias regardless of content quality. The project also found that media literacy interventions promoting reliable sources proved as effective as inducing skepticism toward false content, and that an LLM-based tool for identifying missing arguments successfully broadened perspectives in online discussions.
Results were disseminated through publications in leading peer-reviewed journals, conference presentations, university teaching, media engagement, and public talks. The methodological tools developed under the project are designed for broad applicability and continued use beyond the project's lifetime, with new grants secured to sustain the research program.
