sarken : writings

Collaborative Tagging In Social Networks: A Tagging & Scoring Abstraction

Summary

Collaborative scoring sites like Reddit and Digg demonstrate the editorial power of having user-scored data.  Tagging sites like Flickr, delicious and LibraryThing show the power of having user-created categories (aka folksonomies).  Social networking sites like LiveJournal and MySpace show the power of having user-managed relationships. Creating or enhancing a social network with collaboratively scored tagging of user contributions will dramatically increase its value.

Introduction

This paper aims to demonstrate an abstraction that covers both collaborative scoring and tagging implementations, but also allows for a combination of collaboratively scored tagging.  In the pages that follow I’ll demonstrate how this could be used to enhance existing social networking sites.

Base Representation

The key abstraction has been used in graph theory before but hasn’t yet been applied to the collaborative or tagging networks.  There are three components:

1. Thing
2. Relation
3. Score

Relations occur between Things.  This is similar to vertices and edges in graph theory. Two Things could have multiple Relations; in graph theory the vertices could have multiple edges. A Relation could occur between two identical Things;  in graph theory this is known as a loop. A Score is a measure associated with a Relation between Things.  This is similar to weighted edges.

Let’s look at some examples.

Flickr:

MySpace:
Reddit:

In a more general sense, this can be viewed as follows:

Representational Advantages

Currently delicious is the only social site that comes close to using all the features described above.  Delicious has a bookmarked page (Thing) which can be tagged (Identity Relation) and shows the count of the page across its userbase.  The score in the case of delicious is not tied to the tag, but rather the page itself.

By attaching the score to the Relation, rather than the Thing, this method provides the ability to score different aspects of the Thing.

For example, in a search for ‘Toronto’ on Flickr provides the following pages;

In the ‘Most Relevant’ listing we see some canonical photos of Toronto, but a number of other photos that are much less ‘Toronto-ish’ and seem to be better matches for band and purple.

In the ‘Most Interesting’ listing we see few canonical photos of Toronto, but are much lower in the display ranking.  A number of the photos returned have no inherent Toronto-ness.

By allowing users to score the tags on the images (or Score the Relations on Things) canonical results for a search on Toronto could appear.
Some pictures may exhibit high Toronto-ness and high Blue-ness, while others that currently exhibit Toronto-ness could be downgraded and exhibit high Bono-ness, and medium Cowboy Hat-ness.

Similarly, by implementing the ability to score relationships in a social networking site like MySpace users can choose degrees of relations, as well as add new relations beyond simple friendship.  In the social arena, raw numbers may not be as user-friendly, but degrees of friend relations between two people could become scored like long-term friend, close friend, new friend; or friendship relations expanded with family, co-worker, college chum.

Sites like Reddit could do away with distinct sub-reddits (reddits devoted to a single topic - like programming and web 2.0) and instead auto-generate sub-reddits as pages that have highly-scored tags (Things with highly-Scored Relations).  Delicious could implement their version of sub-reddits by using the scores on the tags as opposed to the count of the bookmarks.

This implementation could also be used to enhance Netflix, Amazon, iTunes and AllMusic genre listings.  A movie wouldn’t be simply in film noir and sci-fi but could be extremely sci-fi, moderately film noir and poor family-friendliness.  A song might be 75% rock / 25% pop or 3-stars electronic, 5-stars ambient.

Conclusion

The Thing-Relation-Score representation model represents the next logical step in social networking tools.  It leverages techniques that have proven successful in other social networks and provides no less functionality that what any of the sites currently implement.  When presented to the user in a non-intrusive way this functionality could help maximize the value of folksonomies.

Notes

1. One aspect of graph theory that is not addressed is directed vs undirected edges.  It’s feasible that Relations between Things could be directed, but it is my belief that this would be too confusing for a collaborative site.
2. In terms of UI design care must be taken to not overwhelm the user with data.  Just as the majority of users don’t tag items, the majority of taggers may not score.  However, taggers that do score can increase usefulness of the social site for all users.
3. See http://www.sarken.org/bettr/bettrflickr.html for a UI mock up of this abstraction applied to Flickr.

Acknowledgments