While SOPA and PIPA are no laughing matter (join the strike), the reaction from those on Twitter who don't know what's going on is great entertainment. Do a search on 'wtf wikipedia' for tweets from confused individuals who are trying to find information on stuff. I'm just going to leave Twitter trackers Revisit and Spot, by Moritz Stefaner and Jeff Clark, respectively, open all day. "OMG I'm doing homework and Wikipedia is blacked out wtf !!!!!!!!!!!!!!!!!!!!!"

Whenever you look at an item on Amazon, the site recommends related items that you might be interested in. So in a way, these items are connected by how people buy. Artist and designer Christopher Warnow uses the metaphor to create a network of Amazon products, where each node represents an item, and connections, or edges, represent common bonds of recommendations. Simply enter an Amazon link, and Warnow's software generates a network.

For example, the image above is the network for Edward Tufte's Visual Display of Quantitative Information, although Stephen Few's Information Dashboard Design seems to have more connections for some reason. My quick guess is that book's that are less niche have more connections, because when I entered Visualize This, the network was pretty small. Although I would've thought that Tufte's book would have a larger network than Few's.

In any case, the application and Processing code is free to play with. Warnow uses Gephi for network connections and grouping. Or if you don't feel like downloading a 60mb file, you can just watch it in action in the video below.

You might also be interested in Yasiv. It's a web app with a similar idea, but not quite as slick of an implementation.

[Christopher Warnow via Datavisualization]

Twitter is an organic online location, full of retweets, conversations, and link sharing. Jeff Clark tries to show these inner workings with his newest interactive, Spot. Enter a query in the field on the bottom left, and Spot retrieves the most recent 200 tweets. You then can choose among five views: group, words, timeline, users, and source.

Each tweet is represented by the tweeter's profile picture, and they rearrange themselves as you switch between views. The latter three views, timeline, users, and source, arrange tweets into bar charts. Fairly straightforward.

Spot gets interesting with the first two views though, groups and words. Tweets are arranged based on the words they use.

Above, for example, is the word view on the search "flowingdata." Tweets cluster around words like world and data. Below is the same search, but with the groups view. Users who tweeted similar text (usually retweets) are grouped together. What jumped out at me was the group on the bottom with a single user's face. That turned out to be a spammer.

Give it a try for yourself here.

[Neoformix]

While we're on the topic of academic papers and how they're linked, Johan Bollen et. al used clickstream data to draw detailed maps of science, from the point of view of those actually reading the papers. That is, instead of relying on citations, they used log data on how readers request papers, in the form of a billion user interactions on various web portals.

Maps of science derived from citation data visualize the relationships among scholarly publications or disciplines. They are valuable instruments for exploring the structure and evolution of scholarly activity. Much like early world charts, these maps of science provide an overall visual perspective of science as well as a reference system that stimulates further exploration. However, these maps are also significantly biased due to the nature of the citation data from which they are derived: existing citation databases overrepresent the natural sciences; substantial delays typical of journal publication yield insights in science past, not present; and connections between scientific disciplines are tracked in a manner that ignores informal cross-fertilization.

Cross-fertilization. Saucy.

Each circle represents a journal and edges represent connections between journals, according to Johan Bollen et. al's clickstream model. Circles are color-coded by journal classifications from the Getty Research Institute's Art and Architecture Thesaurus.

So you have most of the engineering and physical sciences on the perimeter, medical-related areas to the left, and liberal arts is that middle cluster. Statistics is towards the top left, mixed in with demographics, philosophy, and sociology. There aren't many surprises in the clusters, but there are interesting, albeit weaker, links in the open spaces, such as religion and chemistry or music and ecology.

[PLoS ONE | Thanks, @drewconway]

From Autodesk Research, Citeology is an interactive that visualizes connections in academic research via paper citations:

The names of each of the 3,502 papers published at the CHI and UIST Human Computer Interaction (HCI) conferences between 1982 and 2010 are listed by year and sorted with the most cited papers in the middle. In total, 11,699 citations were made from one article to another within this collection. These citations are represented by the curved lines in the graphic, linking each paper to those that it referenced.

The interactive repsonds slowly to clicks and only works in Firefox for me, but it's interesting to play around even if you aren't familiar with CHI and HCI papers. It works better if you select one to three generations instead of all. Click on a specific paper and you get citations for that paper on the right (brown) and the papers that the selected cited on the left (blue).

Color-coding for categories, authors, or subject could add another level of meaning to this. For example, do we see the subject evolve? Do papers that focus on a certain subject site outside of the main topic?

[Citeology via infosthetics]

Food flavors across cultures and geography vary a lot. Some cuisines use a lot of scallion and ginger, whereas another might use a lot of onion and butter. Then again, everyone seems to use garlic. Yong-Yeol Ahn, et al. took a closer look at what makes food taste different, breaking ingredients into flavor compounds and examining what the ingredients had in common. A flavor network was the result:

Each node denotes an ingredient, the node color indicates food category, and node size reflects the ingredient prevalence in recipes. Two ingredients are connected if they share a significant number of flavor compounds, link thickness representing the number of shared compounds between the two ingredients. Adjacent links are bundled to reduce the clutter.

Mushrooms and liver are on the edges, out on their lonesome.

[Nature | Thanks, Elise]

During the riots in London this past summer, a lot of information spread quickly about what was going on. Some of that information was true and some was not so true. The Guardian explores this spread of information on Twitter, and how fact and fiction seem to reveal themselves on their own:

A period of unrest can provoke many untruths, an analysis of 2.6 million tweets suggests. But Twitter is adept at correcting misinformation - particularly if the claim is that a tiger is on the loose in Primrose Hill.

Other rumors include when rioters cooked their own food at McDonald's (false), London Eye was set on fire (false), and Miss Selfridge was set on fire (true).

Each bubble represents a tweet and is sized by number of followers the tweeter has. The big one is usually the orignal tweet and the small ones that cluster around are retweets. Then the colors represent tweets that support, oppose, question, or comment. So when you play the animation for each rumor, bubbles swiftly pop up at the rumor peaks and then settle at true or false.

You can also use the scroll to move to a certain point in time, and roll over bubbles to see the tweets.

Really nice graphic and worth a look.

[Guardian via @jakeporway]

Hilary Mason, chief scientist at bitly, examined links to 600 science pages and the pages that those people visited next:

The results revealed which subjects were strongly and weakly associated. Chemistry was linked to almost no other science. Biology was linked to almost all of them. Health was tied more to business than to food. But why did fashion connect strongly to physics? And why was astronomy linked to genetics?

The interactive lets you poke around the data, looking at connections sorted from weakest (fewer links) to strongest (more links), and nodes are organized such that topics with more links between each other are closer together.

Natural next step: let me click on the nodes.

[Scientific American via @hmason]

As the Eurozone crisis develops, the BBC News has a look at what country owes what to whom:

Europe is struggling to find a way out of the eurozone crisis amid mounting debts, stalling growth and widespread market jitters. After Greece, Ireland, and Portugal were forced to seek bail-outs, Italy - approaching an unaffordable cost of borrowing - has been the latest focus of concern.

But, with global financial systems so interconnected, this is not just a eurozone problem and the repercussions extend beyond its borders.

Simply click on a country, whose arc length represents how much they owe, and arrows show debt.

[BBC News | Thanks, Eugene]

If you don't watch the candidate debates — and let's face it, that's just about everyone — you pretty much miss everything, except for stuff like Rick Perry forgetting agency names. Politilines, by Periscopic, lets you see what the candidates talked about each night.

The left column lists top issues, the middle shows words used, and the right column shows candidates. Roll over any word or name to see who talked about what or what was talked about by whom.

The method:

We collected transcripts from the American Presidency Project at UCSB, categorized them by hand, then ranked lemmatized word-phrases (or n-grams) by their frequency of use. Word-phrases can be made of up to five words. Our ranking agorithm accounts for things such as exclusive word-phrases - meaning, it won't count "United States" twice if it's used in a higher n-gram such as "President of the United States."

While still in beta, the mini-app is responsive and easy to use. The next challenge, I think, is to really show what everyone talked about. For example, click on education and you see Newt Gingrich, Ron Paul, and Rick Perry brought those up. Then roll over the names to see the words each candidate used related to that topic. You get some sense of content, but it's still hard to decipher what each actually said about education.

[Politilines]