More Twitter Rankings

I cannot help it but I love rankings on Twitter, although I have no clue yet about what they all mean. But it is a kind of visualization of Twitter data. This morning I found out through Twitfluence that on my @artgrrl/@twesis accounts I have 121,673/2,046,152 second-order followers, my Velocity is 857/61,979 second-order followers a day, my Social Capital is 1,022.5 -0.6 Low Average/3,040.3 +1.7 High and Centralization is 29.64%/12.18% both 0.0 Average – Resilient. On average my reach is 121,673/2,046,152, and rank is #18,330/#1,962 (92%). This 92% means means that I have a higher reach than 92% of the other twitterers twInfluence has analyzed. Why do people build these rankings and ratings anyway? On his site Guy Hagen (who build this site) argues;

twInfluence is a simple tool using the Twitter API to to measure the combined influence of twitterers and their followers, with a few social network statistics thrown in as bonus.

‘Reach’ could work as a marketing tool, and in short velocity is about ‘the more followers you get, the faster you get them, and the faster your reach builds through sort of a “snowball” effect’. It’s interesting that from data of followers conclusions can be made where there are breaking points where people do not follow you anymore. So there can be a plan on how to get followers and increase your twInfluence apparently.

Words, Wordle and Data Visualization

Now that we’re used to tag clouds in the realm of web 2.0, it’s time to move to the next level and do more with words. It’s fun to pay less attention to actual sentences but to words only, the more a certain word is used indicates the importance in the text without seeing the whole context of the original text. There are a few website where people can create an alternative tag cloud, or a cluster of words. Wordle takes words from a text or site and transforms these into a cloud that can be transformed into different shapes and colors, check this example from my own blog;

Like Jeff Clark argues on his blog, ‘critical drawback of tag clouds is that the words are scrambled (or sometimes positioned strictly by frequency) and one cannot tell from the cloud which words were actually used together in the original text’.[1] There’s no meaning left anymore, except the interpretation of the viewer.

Jeff Clark also created the wonderful Twitter Spectrum which is an analytical project that uses keywords from Twitter and compares them. The fun part here is that I created one about thee weeks ago and coffee was one of the main keywords and today it’s gone.

Other projects I find fascinating are portraits made out of words or a TextArc of ‘Alice in Wonderland‘. A TextArc is a ‘visual represention of a text—the entire text (twice!) on a single page. A funny combination of an index, concordance, and summary; it uses the viewer’s eye to help uncover meaning’. It’s a ‘tool designed to help people discover patterns and concepts in any text by leveraging a powerful, underused resource: human visual processing. It compliments approaches such as Statistical Natural Language Processing and Computational Linguistics by providing an overview, letting intuition help extract meaning from an unread text’. [2] The last example uses words from a book while Wordle and Twitter Spectrum make use of data from databases. Most content we watch on the Internet like on Twitter or Flickr and YouTube and all MySQL websites like blogs and newsgroups and discussion boards all make use of pulling data from databases and displaying this data onto the computer screen. So is text always a visualization then if text can be visualized too?

Playing with Words

The words that came to me through twitter (thanks to @pareidoliac) could be or could not be describing Twitter. The first word is pareidolia, which basically is a psychological phenomenon and the tendency to interpret a coincidental visual stimulus as something already known to the viewer. The word comes from the Greek para (here meaning wrong) and eidolon (image).^[1] Pareidolia is also related to paraphasia, disordered speech in which words are substituted for another word. Common examples are seeing faces in cars or clouds. Pareidolia is related to the paranoiac-critical method developed by Salvador Dali in the 1930s, in which artist find new ways to view the world and objects around them. Objects have no meaning of it’s own but when viewed our unconscious perceives a phantom image, like the interpretation of subliminal messages. Our brain links things that are not linked in the first place. When objects are being perceived as real, or having a personality or even a face, how does this phenomenon relate to the machinic phylum, a concept of Gilles Deleuze, Félix Guattari and Manuel de Lanza? And how does our brain handle new concepts, new technologies?

At the turn of the last century the French philosopher Henri Bergson wrote a series of texts where he criticized the inability of the science of his time to think the new, the truly novel. The first obstacle was, of course, a mechanical and linear view of causality and the rigid determinism that it implied. Clearly, if all the future is already given in the past, if the future is merely that modality of time where previously determined possibilities become realized, then true innovation is impossible. To avoid this mistake, he thought, we must struggle to model the future as truly open ended, truly indeterminate, and the past and present as pregnant not only with possibilities which become real, but with virtualities which become actual. Unlike the former, which defines a process in which one structure out of a set of predefined forms acquires reality, the latter defines a process in which an open problem is solved in a variety of different ways, with actual forms emerging in the process of reaching a solution.[2]

These arguments remind me of the phrase that everything has already been written or done before, nothing new can be created anymore. Although new contexts are being created. So all new technologies would have a component of remediation in them as they have been used before too.

According to de Landa the notion of a machinic phylum blurs the distinction between organic and non-organic life. This would mean a distinction between human life and technology. De Landa also mixes the term machinic phylum with Deleuzes body without organs, which I try to apply on Twitter here, when I take the assemblage of tweets as a single mass. Taking this further “(post)humanity will begin to coevolve (or at least to share its ecology) with new systems of autonomous robots and software agents.”^[3] This is what I think is happening on Twitter, when people get involved so deeply when writing down all aspects of their every day lives. “Nature in this notion is determinate by neither subjects nor objects. It is above all about nonlinear relations, open-ended connections of partially actualized bodies encompassing distinct levels of organization (biological, cultural, technological). [...] Indeed, a body never corresponds to a unity, a whole, an organism, or a system.”^[4] The distiction of online life and offline life intertwines and also the distiction between organic life and non-organic life becomes smaller as argued before. The bodies of users mix with technology through the use of the site of external applications.

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1. <http://cherylbernstein.blogspot.com/2008/11/paranoiac-critical-method.html>

2. Deleuze, Gilles. Bergsonism. Zone Books, New York 1988: p. 97. Found on <http://framework.v2.nl/archive/archive/node/text/.xslt/nodenr-70071>

3. Johnston, John. A Future For Autonomous Agents: Machinic Merkwelten and Artificial Evolution <http://muse.jhu.edu/journals/configurations/v010/10.3johnston.html>

4. Parisi, Luciana. ‘Information Trading and Symbiotic Micropolitics.’ Social Text. 22.3 (2004): p. 25-49.

Data Visualization I

Yesterday on the picnic 2008I ‘ve been watching a small lecture on the stream of Picnic of Aaron Koblin, he creates software and architectures to transform social and infrastructural data into artwork. He started with a graphic of air traffic in America so the viewer could see when and where the most flights were taking place. Every flight was a thin white line and more flights meant a brighter and thicker line. Like between Washington and New York showed the most white and in the middle of the United States it stayed kind of dark. This visualization got my attention as at first It was unclear what this was about but the picture was strikingly beautiful, see for yourself;