The development of efficient methods for searching and browsing large assets of video content has been considered by the academia and content owners for long. Different approaches that range from manual structured annotations, to unstructured metadata collected from several sources, as well as multimedia processing for automatic description of the content, can be identified. The growth on the number of hours of video content put online in video sharing platforms has however shown that video retrieval is still quite inefficient as rich contextual data that describes the content is most of the times still not available. Additionally, metadata is usually not linked to timed moments of content, making direct access to the most relevant moments not possible. In this paper, an approach for making web videos available in the YouTube platform more accessible is presented. The solution is based on a collaborative process presented as a game that enables collecting metadata from the crowd while implementing mechanisms that remove erroneous information usually encountered in this type of information. Metadata, exported to YouTube in the form of captions and descriptions, contributes to enhance video retrieval, guaranteeing a better user experience and exposure of the content.
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