NBCNews ran a story this morning about the movement in mosh pits at heavy metal concerts. Mosh pits often occur at some concerts where "random groups of people dance wildly and a bit violently" in response to the music. While certainly not appealing to everyone, the article points out that the actions in those dance circles or pits is often not as random and unpredictable as it seems. Graduate students as Cornell University determined that the actions actually followed a concept called "Collective Motion", which is similar to a ripple movement which can be observed and often predicted.
While this behavioral observation certainly has application in other highly dynamic events like emergencies (ex: fire evacuations), it also has application to how emergency managers understand social media. Many emergency managers and public safety officials are overwhelmed by the amount of information on social media and often choose to ignore it with the attitude that it is simply an overload and unpredictable. However, as noted author Clay Shirky states, "It's not information overload, it's filter failure". Much like the researchers who investigated the mosh pit, emergency management professionals must begin to look for ways to filter and predict relevant information on social media.
Fortunately, there is an ever growing collection of statistical and analytical research in this area as well as a plethora of free resources that begin to allow filtering and aggregation by keywords, hashtags, and other relevant text. Examples include Monitter, TweetGrid, TrendsMap, SocialMention, Google Alerts, and many more. Here is a link to a site that reviews 12 different tools, but there are many more out there. We have to begin to utilize these tools in everyday ways much like the other resources and equipment available during disaster management and response.