Reuse scenarios overview
Content from Wikimedia projects is reused in many environments, from search engines and AI assistants to mobile apps, games and press. Each of these contexts presents unique challenges for attribution: Familiar visual signals aren’t possible in audio, conversational agents often blend and blur sources of information, and immersive formats like games can obscure the origin of knowledge to preserve user engagement.
In face of those challenges, the attribution framework provides flexible, consistent, and feasible attribution practices across the most impactful ways people encounter Wikimedia content.
Explore the scenarios
Learn about the reuse scenarios where Wikimedia content has the greatest reach.
Search
Search results displaying encyclopedic content, images, or structured data.
AI assistants
Integrating text, media, or data from Wikimedia projects into AI-driven responses.
Social media
Sharing content from Wikimedia projects in online social contexts, such as online forums, chat and messaging services, and social networking platforms.
Games and rich media
Incorporating text, images, or data from Wikimedia platforms in educational and interactive environments.
Media and publications
Using text, images, or data from Wikimedia projects in articles, journals, blogs, etc.
Audio [Under review]
Audio media or voice assistants using content where source citation must remain transparent.
Technology and reuse practices are changing quickly. The framework is designed to be adaptable. New scenarios may be added in the future, and attribution signals may be updated as best practices and new insights emerge.
What to expect
Each scenario includes:
- The context in which content from Wikimedia projects is typically reused
- Why the reuse scenario matters
- The required and recommended attribution signals that apply to each specific context, with data sources and links to the sections that provide implementation guidance
The framework is designed to make it easy for reusers to identify applicable scenarios, discover requirements and recommendations, and adapt relevant signals to the unique affordances and constraints of each medium.