I feel like I’ve just seen a meteor shower – an unexpected but surprisingly satisfying event took place last night. The micro-blogging site Twitter receives mixed reviews. Many people love it, are perhaps even a little addicted to it, others fail to see what all the fuss is about. Whatever your views there is no doubt that it has spawned a number of interesting discussions about the future directions of blogging and social networking.
Several tools have formed around twitter which aim to apply some form of structuring to the naturally unstructured twitter message (a tweet). One such tool is #hashtags which filters all tweets that contain a hashtags pattern (a word that begins with a hash character) and presents them as a chronological list of tweets collected under the heading of the given hashtag thus enabling communities of twitterers to follow a stream of conversations about a common topic. There is no organization to all of this, but rather a community that feeds off a number of sites that make use of this data such as twitterfall.com, search.twitter.com, twist.flaptor.com and hashtags.org itself.
Last night a tag formed to discuss the amount and location of snow that was falling in the UK. Although the conversation was informative it was often difficult to make use of the information because tweeters were inconsistent in declaring their location and the amount of snow they were experiencing. At some point in the evening one twitter user decided to build a mashup that took the #uksnow stream of tweets and parsed their content for the sequence
#uksnow postcode score/10
Where postcode is replaced by the first portion of the tweeters UK postcode and score is a rating (out of ten) used to indicate the amount of snow that is currently falling in that postcode region. The mashup combined this information with google maps, plotting in white regions with the heaviest amounts of snow. It was observed by one twitter user just how consistent this map was with the MET Office radar but with the advantage of being updated in real time.
Clearly there are obvious weaknesses, such as the subjective nature of a zero to ten snow scale (although the, once popular, Beaufort scale for measuring wind speed springs to mind) and the influence of the natural differences in population density which would bias the influence of the tweets in favour of heavily (twitter) populated areas of the UK, but I can’t help feeling that I witnessed something that was both spontaneous and surprisingly useful.