Structured Media
Example of realworld applications in community journalism.
Content-as-Data in Journalism
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Content-as-data is a transformative approach in journalism that treats content not just as narrative but as structured, manipulable data. This paradigm shift enables content to be dynamically repurposed, analyzed, and integrated across various platforms and interfaces.
Key Features:
Structured Information: Content is organized into data, making it adaptable and machine-readable.
Data-Driven Storytelling: Journalists can leverage data analytics to create relevant and engaging content.
API Integration: Content can be dynamically retrieved and displayed, facilitating seamless exchange between platforms.
Real-Time Analysis: Media companies can analyze content performance in real-time to respond to audience feedback and trends.
Real-World Examples:
The New York Times: Uses Markdown for structured content in its Cooking section and offers a public API for recipe data1.
Quartz: Employs Markdown for article drafting and has developed Paloma, a content management system centered around content-as-data2.
The Guardian: Provides an Open Platform with APIs for accessing content, enabling external developers to build applications using their data2.
Community News Interoperability
Community news interoperability focuses on creating a symbiotic relationship between media organizations and the communities they serve, leveraging structured content to foster collaboration, engagement, and innovation in local journalism.
Key Features:
Collaborative Content Creation: Community members can contribute stories or data in a standardized format.
Enhanced Local Coverage: Media organizations can tap into community knowledge for more accurate reporting.
Personalized Content Delivery: Structured news enables tailored content based on user preferences.
Interoperability and Syndication: Facilitates content sharing across different media platforms.
Real-World Examples:
Vox Media's Chorus: Uses Markdown for content creation and treats content as data for efficient management across its news sites2.
ProPublica's Data Store: Offers datasets used in reporting, highlighting the role of data in journalism2.
Datawrapper: Allows journalists to create interactive visualizations, which can be exported as Markdown for integration into articles2.
Hierarchical Domains and Local Ontologies
The integration of hierarchical domains and local ontologies in journalism can lead to a more nuanced representation of community events and experiences, enhancing the way information is organized, interpreted, and shared.
Key Features:
Improved Information Retrieval: Users can navigate complex information systems more intuitively.
Contextual Understanding: Local ontologies provide a richer context based on community perspectives.
Adaptability and Scalability: The structure can evolve to accommodate new information and changing needs.
Collaboration and Knowledge Sharing: Facilitates the exchange of information within and between communities.
Real-World Examples:
BBC and The New York Times: Adopt Semantic Web technologies for improved content discovery and distribution3.
Google's Knowledge Graph: Enhances search results by providing contextual information and relationships between entities3.
Conclusion
The adoption of content-as-data and community news interoperability strategies represents a significant evolution in journalism. By embracing these concepts, news organizations can create more structured, interactive, and personalized experiences for their audiences, fostering a more informed and engaged public. However, this transformation requires significant technological and cultural shifts in newsroom operations and audience engagement.
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