Monthly Archives: July 2011

The entertaining platform for technology and design, TED, posted a talk by Area/Code chairman and co-Founder Kevin Slavin, entitled “How algorithms shape our world”:

There are a couple of interesting examples and ideas in there and the analogy between finance algorithms and the larger processing of “culture” is well argued. A fun 15 minutes – there’s even explosions in there!

While scholars often underline their commitment to non-deterministic conceptions of “effects”, models of causality in the human and social sciences can still be a bit simplistic sometimes. But a more subtle approach to causality would have to concede that, while most often cumulative and contradictory, lines of causation can sometimes be quite straightforward. Just consider this example from Commensuration as a Social Process, a great text from 1998 by Espeland and Stevens:

Faculty at a well-regarded liberal arts college recently received unexpected, generous raises. Some, concerned over the disparity between their comfortable salaries and those of the college’s arguably underpaid staff, offered to share their raises with staff members. Their offers were rejected by administrators, who explained that their raises were ‘not about them.’ Faculty salaries are one criterion magazines use to rank colleges. (p.313)

This is a rather direct effect of ranking techniques on something very tangible, namely salary. But the relative straightforwardness of the example also highlights a bifurcation of effects: faculty gets paid more, staff less. The specific construction of the ranking mechanism in question therefore produces social segmentation. Or does it simply reinforce the existing segmentation between faculty and staff that lead college evaluators to construct the indicators the way they did in the first place? Well, there goes the simplicity…

Simondon’s Du mode d’existence des objets techniques from 1958 is a most wondrous book. It is not only Simondon’s theory of technology in itself that fascinates me, but rather the intimate closeness with particular technical objects that resonates  through the whole text and marks a fundamental break with the greek heritage of thinking about technology as a unified and coherent force. When Simondon reasons over numerous pages on the difference between a diode and a triode, he accords significance to something that was considered insignificant by virtually every philosopher in history. By conferring a sense of dignity to technology, a certain profoundness, he is able to see heterogeneity and particularity where others before him just saw the declinations of the singular principle of techné. In a distinctly beautiful passage, Simondon argues that “technological thinking” itself is not totalizing but fragmenting:

“L’élément, dans la pensée technique, est plus stable, mieux connu, et en quelque manière plus parfait que l’ensemble ; il est réellement un objet, alors que l’ensemble reste toujours dans une certaine mesure inhérent au monde. La pensée religieuse trouve l’équilibre inverse : pour elle, c’est la totalité qui est plus stable, plus forte, plus valable que l’élément.” (Simondon 1958, p. 175)

And my translation:

“In technological thinking, it is the element that is more stable, better known and – in a certain sense – more perfect than the whole; it is truly an object, whereas the whole always stays inherent to the world to a certain extend. Religious thinking finds the opposite balance: here, it is the whole that is more stable, stronger, and more valid than the element.”

Philosophical thinking, according to Simondon, should strive to situate itself in the interval that separates the two approaches, technological thinking and religious thinking, concept and idea, plurality and totality, a posteriori and a priori. Here, the question of How? is not subordinate to the question of Why? because it is the former that connects us to the world that we inhabit as physical beings. Understanding technology means understanding how the two levels relate and constitute a world. There are two forms of ethics and two forms of knowledge that must be combined both intellectually and practically. Simondon obviously strives to do just that. I would argue that Philip Agre’s concept of critical technical practice is another attempt at pretty much the same challenge.

When it comes to analyzing and visualizing data as a graph, we most often select only one unit to represent nodes. When working with social networks, nodes commonly represent user accounts. In a recent post, I used Twitter hashtags instead and established links by looking at which hashtags occurred in the same tweets. But it is very much possible to use different “ontological” units in the same graph. Consider this example from the IPRI project (a click gives you the full map, a 14MB png file):

Here, I decided to mix Twitter user accounts with hashtags. To keep things manageable, I took only the accounts we identified as journalists that posted at least 300 tweets between February 15 and April 15 from the 25K accounts we follow. For every one of those accounts, I queried our database for the 10 hashtags most often tweeted by the user. I then filtered the graph to show only hashtags used by at least two users. I was finally left with 512 user accounts (the turquoise nodes, size is number of tweets) and 535 hashtags (the red nodes, size is frequency of use). Link strength represents the frequency with which a user tweeted a hashtag. What we get, is still a thematic map (libya, the regional elections, and japan being the main topics), but this time, we also see, which users were most strongly attached to these topics.

Mapping heterogeneous units opens up many new ways to explore data. The next step I will try to work out is using mentions and retweets to identify not only the level of interest that certain accounts accord to certain topics (which you can see in the map above), but the level of echo that an account produces in relation to a certain topic. We’ll see how that goes.

In completely unrelated news, I read an interesting piece by Rocky Agrawal on why he blocked tech blogger Robert Scoble from his Google+ account. At the very end, he mentions a little experiment that founder Joshua Schachter did a couple of days ago: he asked his 14K followers on Twitter and 1.5K followers on Google+ to respond to a post, getting 30 answers the former and 42 from the latter. Sitting on still largely unexplored click data for millions of urls posted on Twitter, I can only confirm that Twitter impact may be overstated by an order of magnitude…

In the beginning, it was all about the algorithm. PageRank and its “no humans involved” mantra dominated Google since its inception. In recent years however, Google has started to expand the role of “conceptual” knowledge in different areas of its services. The main search bar and its capacity to do all kinds of little tricks is a good example, but I was really quite astounded how seamless concept integration has become on my last trip to Google Translate:

There are many different ways of making sense of large datasets. Using network visualization is one of them. But what is a network? Or rather, which aspects of a dataset do we want to explore as a network? Even social services like Twitter can be graphed in many different ways. Friend/follower connections are an obvious choice, but retweets and mentions can be used as well to establish links between accounts. Hashtag similarity (two users who share a tag are connected, the more they share, the closer) is yet another method. In fact, when we shift from interactions to co-occurrences, many different things become possible. Instead of mapping user accounts, we can, for example, map hashtags: two tags are connected if they appear in the same tweet and the number of co-occurrences defines link strength (or “edge weight”). The Mapping Online Publics project has more ideas on this question, including mapping over time.

In the context of the IPRI research project we have been following 25K Twitter accounts from the French twittersphere. Here is a map (size: occurrence count / color: degree / layout: gephi with OpenOrd) of the hashtag co-occurrences for the 10.000 hashtags used most often between February 15 2011 and April 15 2011 (clicking on the image gets you the full map, 5MB):

The main topics over this period were the regional elections (“cantonales”) and the Arab spring, particularly the events in Libya. The japan earthquake is also very prominent. But you’ll also find smaller events leaving their traces, e.g. star designer Galliano’s antisemitic remarks in a Paris restaurant. Large parts of the map show ongoing topics, cinema, sports, general geekery, and so forth. While not exhaustive, this map is currently helping us to understand which topics are actually “inside” our dataset. This is exploratory data analysis at work: rather than confirming a hypothesis, maps like this can help us get a general understanding of what we’re dealing with and then formulate more precise questions from there.