Tag Dissolve – Give your users the power to forget
November 22nd, 2005Humans remember things in a fuzzy way. The connections we make between disparate objects happen because of what they share in common – in time, in space, in smell and sound. Burning leaves? Grandparents’ house. Trampoline? 8th grade pool party. Florida? Hanging chads.
I’ve read about techniques of running through your rolodex, or address book, occasionally and without reason, just to see the names and visualize each person. This has been shown to be very effective in helping to remember people’s names since we are more likely to remember what we have seen recently – and forget what we have not.
Reinforcement plays a large role in memory and recall.
If websites begin to implement a tag cloud epoch, they can begin to “forget” the stale tags in their system. Sites can begin to have their most visible tags dissolve in an organic, human way. As people do not continue to tag a certain thing a certain way, this thing should fade slowly from view. It should still be findable (and re-findable) through search and browse, but the tags describing it should count less and less when considering what hot lists to put the item on.
This arbitrary date of oldness, this epoch, should be customizable, of course. But it should be available and it should be prominent. Allow a user to define how far back the tags should be counted. Allow a user to define how old is too old and how recent is recent enough.
Folksonomy will be around for a while, I suspect. It has proven itself useful in many ways – probably some yet to be seen. Some of yesterday’s simple tags will seem quaint tomorrow. Give the user the power to decide whether quaint is signal or quaint is noise. Go implement the “Since” date filter on all your tag clouds today.
November 22nd, 2005 at 8:26 am
Why not re-request that the user re-tag the old tags (or old items tagged) in some semi-random, semi-oldest-job-first method?
Over time, people may associate a tag (or item tagged) differently, and this would allow them to migrate their perception, or conversely, re-emphasize how they’ve percieved it all along.
Perhaps only tangentally related, I’m an iTunes user, and I make sure my metadata (artist, album, genre, year, etc) as well as rating (0 – 5 stars) is complete for every song in my collection. Every once in a while, I’ll generate a set of 100 songs I haven’t heard in a while, and play them, checking for correct metadata as well as ensuring I still think that the song is worth 4 stars, instead of 5 or 3. I don’t change very many things (perhaps 5%), but it helps to keep my collection accurate and reflecting my recent opinions.
Could something like this be useful for tags (and tagged items), or am I barking up the wrong tree here?
January 5th, 2006 at 8:15 pm
folksonomy and tag clouds
Tagging have become major way of organising links and bookmars. We shed the directory approach for the more convenient tagging. In this way a single link can have several tags what gives us more accurate description and later faster discovery…
May 7th, 2006 at 2:09 pm
The tag dissolve is a very good idea. We should find out the average zeitgeist decay in “old” media (press, daily newspaper) to have an automatic algorithm for fading.
Don’t know if a user really can estimate correctly the time-frame. Don’t think so.
I would suggest a formula connected to Tag frequency and “construction shift”.
Every memory has to be (re)constructed within its time-frame. If the environmental parameters to complete the construction cease to exist, the relevance of the memory is fading. (This does not mean that a human being can’t remember a scenario perfectly but over the years the external parameters to recreate the remembered situation simply are not there anymore). So the interest in memories (Tags) drops.
How to calculate construction shift by an algorithm?
One option would be to use the “Mentions by Day” from technorati. For example it’s easy to see when people began to talk about iPod nano (http://www.technorati.com/chart/ipod%20nano?chartdays=360&language=n&authority=a1) and that after a while popularity of that topic began to drop.
This shows an individual Tag popularity for a specific tag (which could be compared to the user rated Tag Dissolve).