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CoffeeReader - Collaborative Feed Reader

Social Technologies

Overview

In the spirit of Web 2.0, the proliferation of social bookmarking allows users to bookmark and tag web pages for their own use and simultaneously share their views and enhance the information on the web. CoffeeReader uses the same idea for Atom (and RSS) feeds-enabling them to be exposed, tagged, and used to create a social and collaborative experience out of feed reading.

Unlike bookmarks that generally point to static pages marked for future references, the essence of Atom (or RSS) feeds is dynamic. The social metadata of a feed is much richer than that of a bookmark. Not only do they let users bookmark or tag a page, but a reader can actually collect information about how often they follow the feed, do they cover all its posts, how do they rate the posts, and more. Sharing this metadata can significantly enhance the social experience of feed reading. While social bookmarking enables discovery, sharing feeds has the potential to solve an additional pain-point: coverage, and thus extend or transform a social application into a collaboration application. As feeds keep streaming new posts and as new feeds appear, the task of going over all posts in one's feed reader becomes overwhelming. The difficulty is twofold: for one, the sheer amount of posts may be very large; but on top of that, the quality of posts is not unified. Consequently, one is first faced with the task of filtering and then with the task of actually reading.

CoffeeReader (Collaborative Feed Reader) comes to address these issues. It enhances regular readers with social and collaborative aspects of feed reading.

CoffeeReader allows its users to:

  • Expose the feeds they have in their reader
  • Tag and rate feeds
  • Leave annotations and summaries
  • Send personal recommendations to peers
  • Ask for help - mark a feed as interesting and ask someone to read it and summarize it.

The exposure of feeds does not end with sharing the list of feeds. It also includes feed statistics and metadata, such as coverage percentage, reading frequency, or average rating. This ecosystem of users and feeds could create a valuable folksonomy of feeds, which can facilitate feed search.

Because readers may view the post read/unread status of their peers, a community can ensure coverage of a collection of relevant feeds, trusting that community members as a group will read all posts, share their thoughts, and highlight valuable feeds for the benefit of their peers.

Topic-based CoffeeReader communities can be formed by enrolling new members who, together, cover a set of feeds they collaboratively collect. Ad-hoc CoffeeReader communities may be formed by gathering people over a social network.

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