Thursday, May 3, 2012

A Twitter Anatomy of a Protest

Here's a visualization of mid and lower Manhattan on MayDay, 2012, plotting the when and where of tweets containing the keywords, MayDay and Occupy (representing a healthy mix of supporters, detractors, and everybody in-between).  The visual coordination of three dimensions of data: location, time, and topic, provides an up-to-the-second profile of a social event as it forms, moves, and dissipates.


Social media, in this case a sampling from Twitter, can provide excellent insight into the anatomy of an event as it trends in space and time.  In addition to the ability to analyze an event after it has happened, and of course the general benefit of seeing a movement as it is happening, sometimes the combination of these visual dimensions may allow a viewer to reasonably extrapolate or predict some organic movements of  social phenomena before they happen.


This visualization (and the series of insets below) shows the Bryant Park staging area as tweets converge around the meeting point, displays the tight linear trend of the march's progress south to the business district, and finally the dissipation cloud around the NYSE that evening.  The time profile, meanwhile, shows the buildup of tweets using these keywords, plateauing at the time of overall movement, peaking at the end of the workday, then smoothly dropping off in frequency.

A gathering of topically related (#MayDay, #Occupy) tweets cluster around the Bryant Park, the physical meeting point of the march.  The timeline shows the number of tweets growing with activity.

These event tweets show a strong linear path, relating to the march proceeding south along Broadway.  Tweet volume plateaus.


Finally, the related tweets coalesce around Wall Street, the final destination of the protesters, where the tweet volume peaks then gradually diminishes.



 

2 comments:

  1. very interesting. How was this information developed? can "non-techie" capture tweets & plot activity?
    Thanks, Ralph

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    1. Thanks, Ralph! Yes, this data comes from the Twitter API, which is a tremendous foundation. It was visualized in IDV's Visual Fusion.

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