This paper focus primarily on descriptive metadata which
identifies and functions to organize information based on its
intellectual content.
While professionally created metadata are often considered of
high quality, it is costly in terms of time and effort to
produce. This makes it very difficult to scale and keep up with
the vast amounts of new content being produced, especially in
new mediums like the World Wide Web.
User created metadata is a third approach, and this paper
focuses on grassroots community classification of digital
assets.
What seems to be relatively new and different
is the emphasis on user added keywords as a fundamental
organizational construct. These keywords, which are referred to
as “tags” on the site, allow users to describe and organize
content with any vocabulary they choose.
A primary difference between Delicious and Flickr is that
while the tags on Delicious are primarily from the users of web
documents that were written by another party, Flickr is
primarily used by individuals to manage their own digital
images, and the majority of the tags are users tagging photos
they created themselves. This is not absolute; the system does
have the option of allowing users designated as friends or
family to tag a users’ photos. Additionally, users can and do
enter images others created into the system, often from web
sites. This use of the system is much more like Delicious, but
seems to be a small fraction of the use.
The organic system of organization
developing in Delicious and Flickr was called a “folksonomy” by
Thomas Vander Wal in a discussion on an information architecture
mailing list (Smith, 2004). It is a combination of “folk” and
“taxonomy.”
An important aspect of a folksonomy is that is comprised of
terms in a flat namespace: that is, there is no hierarchy, and
no directly specified parent-‍child or sibling relationships
between these terms.
Two tags of particular interest are “cute,” and “me.” This
will be elaborated upon later, but I think these two terms
reflect the dual nature of these systems: the compulsion to
share - what is the Internet if not a venue for
sharing cute photographs? - and conversely the importance of
individuality and ego for these systems to work.
In a folksonomy the set of terms is a flat
namespace: there are no clearly defined relations between the
terms in the vocabulary.
Perhaps the most important strength
of a folksonomy is that it directly reflects the vocabulary
of users.
As discussed
earlier, a folksonomy represents a fundamental shift in that
it is derived not from professionals or content creators,
but from the users of information and documents. In this
way, it directly reflects their choices in diction,
terminology, and precision.
Some classification schemes are disjoint from the
vocabulary of the users. In “Metadata for the Masses,” Peter
Merholz argues that a folksonomy can be quite useful in that
it reveals the digital equivalent of “desire lines”
(Merholz, 2004). Desire lines are the foot-‍worn paths that
sometimes appear in a landscape over time. Merholz notes, “A
smart landscape designer will let wanderers create paths
through use, and then pave the emerging walkways, ensuring
optimal utility. Ethnoclassification systems can similarly
‘emerge.’ Once you have a preliminary system in place, you
can use the most common tags to develop a controlled
vocabulary that truly speaks the users’ language.”
Although the Delicious tags on Merholz’s article are only
one example, a folksonomy, with its uncontrolled nature and
organic growth, has the capability to adapt very quickly to
user vocabulary changes and needs. There is no significant
cost for a user or for the system to add new terms to the
folksonomy. The problem is that while the disparate user
vocabularies and terms enable some very interesting browsing
and finding, the sheer multiplicity of terms and
vocabularies may overwhelm the content with noisy metadata
that is not useful or relevant to a user.
“Aside: I think the lack of hierarchy, synonym
control and semantic precision are precisely why it
works. Free typing loose associations is just a lot easier
than making a decision about the degree of match to a
pre-‍defined category (especially hierarchical ones). It’s like
90% of the value of a ‘proper’ taxonomy but 10 times simpler.”
(Butterfield, 2004)

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