Welcome to the new innovation policy platform for the cultural and creative industries
- POLICY AREAS
AI and Cultural and Creative Industries
New European Bauhaus – CCIs enabling green transition
Immersive Media
Crafts-led Innovation
Platformisation of the Music Industry
Inclusivity in Video Game Industry
Cross-Innovation with Performing Arts
Fashion Transition: Eco-Design for Circularity
Cultural Heritage Institutions within Open Innovation Ecosystems
New funding models for creativity and innovation
Spaces, infrastructures, and ecosystems for CCIs
ekip Tools and methods
Data Acquisition Process
For the social media listening, we gather data from various social media platforms, including YouTube, Instagram, Twitter and Behance, using open APIs. This structured and adaptable approach ensures seamless data retrieval while enabling integration of new sources to keep pace with evolving social media trends.
Community Creation
The social media listening process begins with the development of a dynamic observation community. Updated annually, this community includes profiles of early adopters, EU policy-related users and European creatives identified on Behance. The process involves rigorous filtering to exclude spamming profiles, ensure content quality and maintain authenticity, focusing on European-based users.
Key Steps in Community Formation:
- User Detection: Selection from Nextatlas’ early adopters, supplemented by EU policy-related and Behance profiles.
- Filtering: Excludes spammers and excessive sponsored content, prioritising active and meaningful contributors.
- Regional Analysis: Profiles are balanced across European macro-regions to reflect geographical and cultural diversity.
- Industry Analysis: Users are categorised by their cultural and creative industry affiliations.
- Fine Tuning: Gender balance and representation of diverse industry scales are ensured, forming a community of over 10,000 profiles.
Content Analysis
The content generated by the community is analysed using advanced Natural Language Processing (NLP) and Computer Vision models. This dual approach captures both textual and visual elements, enabling a holistic analysis of user-generated content.
Clustering Methodology
Content is categorised using two complementary clustering approaches:
- Top-down Clustering: Groups content based on predefined keywords and policy areas to support systematic exploration.
- Bottom-up Clustering: Identifies spontaneous themes by grouping content with shared keywords or topics, uncovering emerging organic trends.
In-depth Analysis of Emerging Themes
The clustered data undergoes thorough analysis to identify the drivers of significant trends. This includes examining regions, industries and demographics associated with key topics. Insights are further refined through human curation to ensure alignment with organisational objectives and to offer a deeper understanding of the evolving social media landscape.
