I am someone who is constantly grappling with a TMI problem -- Too Much Information. I have cobbled together a patchwork of solutions in response. For example, I have commented in the past about how I select people to follow in Twitter who function as filters and present me with new ideas and information that would be difficult to obtain working on my own (see: Extracting Value from Twitter Messages Using Filters). Recently and in order to cope with my TMI problem, I have begun to use my Palm Pre to quickly scan incoming email during my odd downtime intervals. Email browsing is so easy on this device and it is so readily accessible that I can compress this activity into this previously unused time. When I return to my desktop PC, the remaining email in my inbox has been filtered and thus merits more serious attention.
A recent article in the New York Times presents a TMI problem of a different type that is being addressed by Twitter in a clever way (see: Refining the Twitter Explosion). Here's the nub of the Twitter TMI problem. When there is a disaster like the one that recently struck at Fort Hood, there is a rapid influx of Tweets from a large number of putative on-site, real-time observers. Many of these messages are short, written under duress, and do not provide reliable information about their geographic origin. Twitter has come up with a means to group Tweets by their point source using the GPS geolocation feature present on many cellphones. Read more about this below in an extract of the article:
Simply put, there is way too much information on Twitter — lately, it defies navigation. In January, there were 2.4 million tweets a day....Until lately, the main way to make sense of an urgent outpouring of tweets on a particular subject was to use text searches: look for the phrase “Fort Hood,” for example, or maybe an agreed-upon label [tag], “#fthood,” within tweets. Yet during events like the shootings on Thursday at Fort Hood that left 13 people dead, this method is useless. Hundreds of “relevant” tweets pop up every minute, most repeating the same news reports over and over again or expressing concern from far away. Which is why a new feature that Twitter says it could unveil in the next few weeks — “geolocation” — holds such potential to make the Twitter rapids navigable. The idea is to take advantage of global positioning systems on cellphones to allow Twitter users to include a precise location with each tweet. Users would be able, right off the bat, to limit their searches to tweets from a particular location. “Proximity can be this proxy for relevance,” said Ryan Sarver, the director of the Twitter platform, who led a “fairly small team” of programmers who after a few months are close to completing the geolocation project. “We are about delivering the right information to the right people.” After limiting searches to those from within 15 miles of Killeen, Tex., a town near the Army post, you easily find messages sent by soldiers describing what it is like to be on lockdown or worrying about their children at school on the post....Because GPS will provide the ability to become very “granular” with locations, you could mimic through Twitter the banter at the local diner or a barbershop, by limiting a search of tweets to a two-block radius.
The use of geolocation serves a couple of purposes. First of all, it provides reliable information about the location of the reporter even if he or she does not provide it. Secondly, it provides some degree of validation that the reporting party is in the vicinity of the event being referred to in order to differentiate the message from someone who is spoofing. Geolocation of Tweets does not, however, solve the major problem of many short messages generated from disaster sites. Many of them are garbled and may provide misinformation, often because the observer reports rumors or is only observing a limited portion of the event. Information scientists developers are now working on this problem and you can expect some sort of solution in the near future. They are developing software that "ingests" myriad fragmentary real-time Tweets about an event and then develops some semblance of "truth" by parsing multiple messages, analyzing them syntactically, and looking for common themes and scenarios within them.
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