Last week I posted something on Educating Time. Of course one of my particular interests is information seeking and there to, temporality in search is a really interesting issue.
Searching for Temporal Information
Time is of course a crucial element of evaluation for some claims (e.g. the number of women who have won a nobel prize changes over time). Equally though, it is not terribly sophisticated to hold all claims as unstable (e.g. the number of women who had won a nobel prize by 2013 will not change). The ways that users evaluate temporality, and search engines a) present temporal information and b) use it to index and rank claims is fascinating. Think, for example, of the different temporal needs of crisis management situations (a balance of immediacy and credibility in foregrounding recent info) versus relatively stable claims (some historical accounts, searches for specific information or theories, etc.).
Quantifying Search (& Slow Search)
There’s something else interesting around quantification of search, for example search engines (and websites) give figures about number of hits or searches made. Google Trends/Insights gives info on query quantification over time, so we can see that particular queries were made more at particular times; they use this to give us the Google Zeitgeist, the spirit of the time.
But there are other ways of quantifying search that involve how long people spent looking for information – did they engage in a quick search (factual retrieval?) or did they spend rather a long time. The latter can indicate ‘lostness’ or at least a difficulty in finding information. But it can also indicate the seeking (and assimilation of) more complex information.
Jamie Teevan’s work
Basically if you’re interested in any of this, you should read Jamie Teevan’s work & I particularly like the term “slow search” to describe the kind of searching above, where (her description) “the quality of the experience – not the speed – is what matters”. This focus on speed really matters because currently search engines build speed (qua fastness) into their algorithms (and speed matters for good reasons).
I discussed a combination of the previous point, and the notion of sequence a while ago, when I talked about how we ‘cluster’ queries under a particular information need. Of course, in wider scope, we might be interested not only in the use of temporal information to cluster queries, but also how those queries (and results) relate to each other. That is, we might be interested in how query ‘a’ (“What is a plantain?”) leads to query ‘b’ (“Plantain recipes”), and the types of knowledge building activities (or, misconceptions) undertaken through a sequencing of query-navigation-query-navigation, etc.