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Madison (2014)

The New York Times’s century-and-a-half news archive is a rich and under-utilized resource, not only for news events but also as a reflection of cultural history. While news events and reporting give us a glimpse of one aspect of our past, the advertisements that ran alongside those news articles allow us a very different view. They act as commentary on technology, fashion, economics, gender relations and more, often in ways that are fascinating, funny or poignant.

Madison is a crowdsourcing project designed to engage the public with historical ads from The New York Times archive. The digitization of our archives has primarily focused on news, leaving the ads with no metadata–making them very hard to find and impossible to search for them. Complicating the process further is that these ads often have complex layouts and elaborate typefaces, making them difficult to differentiate algorithmically from photographic content, and much more difficult to scan for text. This combination of fascinating cultural information with little structured data provided the perfect opportunity to explore how crowdsourcing could form a source of semantic signals.

Madison's user experience centers on four design principles for crowdsourcing:

These choices formed the basis of Madison, and also shaped the platform underneath it: Hive. Hive is a modular, open-source framework for building crowdsourcing projects like Madison with any set of assets.

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