It looks like Tagging is a hot topic for educators! There are currently around 140 social bookmarking sites on the web with various interpretations. Here is a little pot pourri of resources to complement Jason's elluminate session today:
A way to socially network and socialise using tagging:
Hear about the new tools for bookmarking from Rachel Bridgewater, and begin to understand how approaching this task with these new tools can become a means for social networking with your user.
Peerworks is an open source project that is building content classification tools to help online browsing, collaboration, and social discovery. Q: What is Peerworks?
Peerworks is a project focused on building content classification tools to improve online browsing, collaboration, and social discovery. We plan to make our technology open source, and to work with existing websites and content management systems that want to implement this technology. We also plan to work with researchers on enhancing the technology and for better understanding of how people use it.
Q: What is the goal of Peerworks?
We want to help communities organize themselves, and help individuals learn what they want to know. Ideally, a site using Peerworks technology would be able to show each user all the items — and only the items — that are useful or interesting to them. To do this, our technology lets users indicate their interests through individualized tagging. Once users have indicated their individual interests, we can find those users who share interests.
Based on these shared interests, users could form broader relationships and more community structure with each other. This kind of structure can remain fluid and can change as users’ interests and judgments evolve.
Q: What is tagging, and why are we using it?
Tagging simply means labeling items of content with personally meaningful words or phrases. Any user can tag any item of content with any number of tags. Tagging is a very flexible way of organizing content because, unlike traditional hierarchical organization, categories can overlap; an item of content can belong to as many categories as a tagger likes.
There are two approaches to tagging — consensus and individualized. Consensus tagging requires that all users of a site use the same tags, and use them consistently. This is very difficult to enforce, and creates a lot of tension between people who have different interests or tagging styles. We have chosen to use individualized tagging. This is not a new idea — some major sites use it already, and others are considering it — but we take it to a new level by learning each user’s tagging preferences.
Q: What is individualized tagging, and how do we support it?
Individualized tagging lets each user decide on the meaning of the tags they use, rather than having to track a consensus meaning. In our approach, individuals express their interests by inventing tags, or choosing ones that already exist, and tagging a few items with each tag.
Using these examples, our software builds a definition of what that tag means to that individual — we call that definition a “classifier.” Then our software can look at all the other items on the site, and decide fairly accurately how the user would tag those items — it “classifies” all the items. The user can correct (train) the software at any time, making its definition more accurate.
Once our software has tagged all the content, the user can view it filtered and sorted by tags, to show the items they are interested in, organized in ways that work well for them.
Q: How can individualized tagging help the whole community?
First, let’s mention a point that may not be obvious. Since each individual is tagging for their own benefit, they have an incentive to tag items with as much detail as needed to filter and organize the content the way they want. Since the software will feed back its understanding of what they mean, they have a continuing incentive to train the software to understand their meaning correctly. This implies that the software tends to build up an increasingly accurate set of definitions for each user’s interests and preferences. It turns out that it is fairly easy to compare two tag definitions and see how similar they are. Based on this, we can find users with similar interests and preferences.
This is different from existing systems that judge similarity based on tag names alone. For example, if a financier and a limnologist both use the tag name "bank," an existing system might think they are talking about the same thing. However our software will build very different definitions for the two users: the financier's definition will be about institutions that control money, and the limnologist's definition will be about earth that channels water. So by comparing definitions, we can identify users who share interests. More generally we can look at the full range of definitions across an entire site, and track the shifting range of topics that interest users of that site. We can cluster topics together based on shared interest, automatically generate discussion groups based on overlapping topics, etc.
We explicitly accept (and celebrate!) that the users of individualized tags will be a diverse population, each with different interests and ways of classifying the world. Our goal is to accommodate the real diversity of the user population, while also giving people ways to adopt each others’ perspectives, and to collaborate when they want to.
Q: Can this approach help people who don’t do their own tagging?
"Consensus" tag spaces can be created by clustering similar definitions. A site can provide views that are organized according to the consensus tags to help everyone see the big picture. These views can also provide a friendly way for new members of an online community to get up to speed on the existing topics. And people who don't do their own tagging can simply view items using the consensus tags.
The consensus tag space will automatically evolve over time as personal tagging decisions shift. This helps to avoid a straitjacket of fixed topics that have to be changed manually. Emerging issues and changes in community views, etc. will automatically show up in the consensus tag space.
Q: How can individualized tagging improve online communities?
There are lots of ways, we have only thought of a few:
People can easily find others "near" them who have similar interests in a given topic, and begin exchanging messages or form a discussion group.
People can benefit from the tagging decisions of those with similar tags, essentially collaborating on refining the tag definition.
If people get interested in a given topic or way of thinking, they can find the people who are most involved with that and see how they've organized their knowledge.
People can “stand in the shoes” of others by adopting their views and tags.
Individualized tagging uses the knowledge and judgment of thousands to drive the creation of a "social landscape," which becomes a map of the interests of the community as a whole. Right now, people have to make these judgments anyway for their own purposes, but they can't easily feed them back into the community and contribute to the commonwealth. Learning individual preferences through tagging lets everyone benefit from these individual choices.
Commentator David Weinberger says a growing trend allows us all to have a say on how to organize the Internet. 'Just as the internet allows users to create and share their own media, it is also enabling them to organize digital material their own way, rather than relying on pre-existing formats of classifying information.' A December 2006 survey has found that 28% of internet users have tagged or categorized content online such as photos, news stories or blog posts. On a typical day online, 7% of internet users say they tag or categorize online content. The report amd subsequent podcast link features an interview with David Weinberger, a prominent blogger and fellow at Harvard's Berkman Center for Internet & Society.