Epistemic Power and Discursive Coalitions in EU Digital Regulation: Explaining Techno-Optimism Beyond Big Tech
This article examines the construction of techno-optimistic narratives about algorithmic responses to information manipulation through a mixed-methods analysis of the EU consultations on disinformation and platform regulation (2018–2024). The article combines qualitative discourse analysis with discourse network analysis to map how actors, concepts and policy preferences cluster into discursive communities. Contrary to expectations of a polarised conflict between Big Tech and its opponents, the findings reveal a more complex landscape in which actors from civil society, media organisations and industry converge around optimistic understandings of the positive implications of technical (technology-enhanced or regulatory) solutions to disinformation. The combination of both methods suggests that Big Tech’s influence does not primarily derive from the primacy of its arguments in policy deliberations but rather from the overall orientation of the debate towards problems for which technical solutions appear to be the more effective response. This process constitutes a form of epistemic capture related to agenda setting, providing further evidence for the mechanisms of epistemic capture. However, the article is limited because of the non-inclusion of the consultations on the AI Act and Code of Practice, which future research should expand upon. The article contributes to scholarship on digital governance by linking discourse network analysis with field theory and theories of discursive capture, showing how epistemic authority emerges through coalition structures rather than simple lobbying success.
Authors
- Luis Bouza García (ORCID: https://orcid.org/0000-0002-0486-2340)
Institutions
- Universidad Autónoma de Madrid (ES)
Publication Details
- Journal
- Social Sciences
- Published
- 2026-10-13
- DOI
- https://doi.org/10.3390/socsci15100704
- Primary Topic
- Misinformation and Its Impacts
- Type
- article
- Field-Weighted Citation Impact
- 0.00