The mining of crowd-sources
While some are wondering why scientists appear not to appreciate tools like Twitter to communicate , there is more proof for the value of the meta-information that can be plucked from the stream of micro-utterances.
Roughly two years ago we speculated about possibilities to extract (useful) crowd-information. Increasing mentioning of umbrellas/rain - together with localization -, for example, could give valuable input to the weather forecast. As we put in 'Meta Mining':"If the noise of individual utterances will be systematically analyzed for overlying macro-structures and for phase-transitions from the purely random to the organized, there will be more information gained than individually and knowingly put in. The sheer boundless chatter of Twitter and alike corresponds to the cells, the web is the organism." We were encouraging to step back and look at structures rather than the individual tweets.
In a recent report in "The American Journal of Tropical Medicine and Hygiene" that is reviewed in Nature, scientists show how analysis of Twitter-messages would have been a quick way to detect and track the deadly cholera outbreak in Haiti - simply by looking at the number of 'cholera' posts on Twitter. They found a stunning correlation between the official number of cases and the volume of chatter related to that.
This is only one more - scientifically proven - example for the potential of the data deluge.
It is a matter of time until publicly available analysis-tools mine crowd-sources like twitter (or even de-personalized sms…) for real-time input to forecasting tools.