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Tuesday, February 28, 2012

Observations: The Streamgraph

This graphic is a representation of the frequency of all the words associated with the word “signed” on Twitter January 1st, 2012 for the 1000 tweets between 14:30 and 15:02. The zero point runs through the center of the graph, and the further out a section is from the center represents a more frequent association with the search query. The max amplitude of a word on this graph is attained at 14:38 by the hash tag “#NDAA” (104) followed by a close second, “Obama” (~102).

The night before, on New Years Eve 2011, President Obama had signed the 2012 National Defense Authorization Act, a highly controversial bill undermining major components of our national infrastructure such as the writ of habeas corpus and the 4th amendment to the US constitution.

The input to this graph is a composite of tweets from hundreds of individuals all across the world connected only by an Enter key. With a bit of practice, it becomes easy to use this entirely decentralized source of information to find out what is going on in real time. As another step towards information access, livestreamers often tweet their broadcasts attached to relevant keywords, their streams normally available in the list of tweets visible underneath the streamgraph itself (not shown). The way I read it, a higher amplitude on the streamgraph is equivalent to a higher informational relevancy to our social system.

The application used to generate streamgraphs was created by Jeff Clark, a data visualization artist. Its not very accessible in its current state: users are unable to view a period of time other than the period across the past 1000 tweets, each graph takes 20-30 seconds to generate, and a lack of guides make it hard to gauge scale and quantity. The streamgraph also has a flaw which unless somehow remedied or amended may serve to be close to fatal to its ability to act as a source of live news: the input can consist of false reports. While following the eviction of Occupy Denver, that police were setting tents on fire pinged with the highest frequency on the streamgraph. It turned out to be the occupiers themselves. Whether the misinformation was intentional or not, it permeated the public opinion of what was actually happening at the eviction.

That given, as soon as I found this application it instantly became my primary source of real-time news for the Occupation. Searching the keywords “#occupy” or "#ows" a few times a day kept me current on all major events, evictions, actions and arrests related to the Occupation movement. It utilizes the amalgamated signal of information to build a visual representation in alignment with the systemic bias of “the public opinion” of the Twitterverse.

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