In the past I have written about tags to which we attach some value. Once upon a time in the late 1990s I called these qualities – a means of describing the things that we find valuable in a resource that do not fall neatly into yes-no tag categories: funny, good for beginners, complex, helpful and so on. In recent years I’ve been calling them scalar tags, to reflect the fact that they carry a scalar value or weighting.
We need these things for a variety of reasons. In the first place, as it turns out, many of the binary-category, definitional, taxonomic tags that we use on many social systems are really nothing of the kind – they often relate to our opinions and feelings towards the things we tag which, by and large, are not black and white (they are also often about things that are relative to us and our context – such as ‘my family’ but that’s another issue). On some sites as many as 30% of all tags used can be about subjective qualities of things. Secondly, they have a really great practical value: if tags carry a value then they can be used to rate things in multiple dimensions. Instead of the traditional ‘I like this’ or star ratings that simply suggest something is more or less good or bad, we can use scalar tags to describe in what ways we like them. Thus, we create a kind of disembodied user model of the aspects of ourselves that are important in relation to what we are tagging. This, as it happens, is mighty useful in an educational setting because liking a learning object or rating it highly is not really the problem: we want to know why it is liked, in what circumstances, for what purposes. Standard high-low ratings can be useful for things about which we have fairly consistent feelings such as movies, books, music and so on but the thing about learning is that it changes us. This means that what we valued when we were learning something no longer has any value to us because we have already learnt it (at least, that is often the case).
It occurred to me yesterday as I was blogging about sets that there is a much nicer term for this kind of thing than scalar tags: these are actually fuzzy tags. Fuzzy tags are of course ideally suited to being considered in fuzzy sets. Fuzzy tags are not categorisations as such, they are ways of attaching a value to a tag that reflects its degree of membership within a set of such tags.
Fuzzy tags are not unproblematic. It is fiendishly hard to create an interface for them, they are highly susceptible to the cold start phenomenon, it is difficult to find uses that give people a non-altruistic motive for using them, and it is hard to get the right balance between counting the number of tags and the values attached to them when presenting resources that have been tagged that way. Do you give prime real estate to those with a higher average value and, if so, how do you balance it so that a resource that has been rated highly by one person does not override one that has been rated even higher by some people but lower by others? Not even considering other problems like the fact people rate things relatively differently, issues of tag ambiguity, personalisation factors and much else besides, it’s a wicked problem with many viable solutions.
If we can crack this problem (I have had a fair number of cracks at it already) then it opens the door to some interesting ways of looking at collectives. At the moment, we think of collectives as being a kind of human-machine cyberorganism that is formed from the actions of the crowd which are then processed to create something that has agency in a social system. What comes in the first page or two of a Google search is an implicit recommendation by the collective, because of the ingenious PageRank algorithm that underpins it. The big tags in tag clouds tell us what the collective finds interesting and suggests to us that we might find it interesting too (and we do – we are about 3-4 times more likely to click a big tag in a tag cloud than a small one).
Apart from in a few experimental systems (I think I’ve built most of them) collectives are a combination of machine intelligence and human intelligence, at least when they work well – they can equally combine mob stupidity and machine ignorance when they work badly. I think they can be a lot more useful when they also capture the affective stuff – human feelings and opinions as well as intelligence. This is about more than just good or bad feelings: it is about things that say what it is that affects us and how we are affected, as well as how much we are affected by them. Fuzzy tags can give us richer folksonomies that reflect more of the diversity, hopes, interests and intentions of the crowd than simple taxonomic tags. Plus, ‘fuzzy tag’ is a really catchy name 🙂
