Scrollables is a stunning exploration that uses paper as an interface for digital content. Move the scroll, and control the content projected onto it:

Current is a real time data visualization of the life-cycle of internet memes, from birth to evolution and decline, in relation to the daily news cycle. By visually anthropomorphizing the capricious nature of public attention we are able to spotlight missed opportunities in news coverage, and, potentially, recover news readership that has been lost to more sensational sources. Download and run the application yourself!

Plus and Minus visualizes the progress of HIV/AIDS in the last 20 years and lets users see how changes they make can affect the future. It is based on data from 1990 through 2007 and lets one compare see possible outcomes of investment in different solutions like condoms, HIV screenings and education.

Mobile Logger is an iphone application that lets its users record location, direction, speed, altitude and sound level as they go off on a journey. This is especially designed for bikes, but can obviously be used in other “journey” situations. Check out the Gizmodo post.

Chat Roulette 1988 is a phone based system that allows you to chat with different people anytime, anywhere. Since the 80’s are back (or so claim the creators), you might as well give this a try! The project leverages the Asterisk Open Source telephony project.

SHORT++ is a pair of interactive robotic elevators shoes that are activated by an iPhone app. (There’s a new UP for that…) Check out Adi’s video:

Papel is a retail display that mixes paper art, illusions and human interaction. When a viewer is not present, the wall paper will radiate a soft glow. When the viewer stands in front of the installation, the glow fades out, and their image is back-projected onto the paper structure.

Thermogen is a cool project that converts heat from a toaster over to electricity. In essence, a cirtuit to be used where power is unavailable, but heat is plentiful. Additionally, David created not only electricity from the heat, but also yummy choc-chip cookies.

Three glowing screen are set amidst arching piles of ghostly pale books. One continuously reconfigures Twitter posts about reading, on another bouncing letters randomly settle into place, revealing pointed quotes about reading drawn from well-known books, while the third uses the viewer’s image to trace out a series of related passages.

Photos below:

more project information here.

Ironically enough, Bing sponsored a really cool lounge which included a number of interactive art pieces. This is were Dan Goods and I installed a variation the Aerogel installation. For TEDActive, we slightly altered its interactivity and the projected material:

Aerogel is a solid made up of 99.8% air and 0.2% of a smoky form of silicon, hence its other name: ’solid smoke’. While it is easily breakable, the material is super light and a fantastic heat insulator. Throughout the week, Dan would let people hold a piece of Aerogel on their hand while directing a blow-torch at it. When projected upon, it captures light in a stunning way:

NASA uses aerogel to capture dust particles in space. These particles vaporize on impact with solids and pass through gases, however can be trapped within the aerogel. Our installation dealt with this notion of capturing that which difficult to hold or grasp. As the conference progressed, the ideas that were raised and discussed during the talks were captured and projected on the aerogel pieces. At different times, a variety of topic would be projected within the aerogel pieces. When left by itself, the projections morphed between movement and colors. But as a person would move their hands in front of the installation, some of the most recent messages posted on Twitter about TED or TEDActive would explode within the projected space.

Here’s a video demonstrating the material’s amazing capability to capture light:

Here’s another video showing Dan interacting with the piece:

How did this happen and what prompted this message to spread?

#CheeringForTheYankeesIsLike

About an hour after Matt sent out his first message, one of his followers, @lizzieohreally, wrote the following message ‘@jaketapper? @abcdude? …Hoping someone w/ more Twitter than I can help popularize #CheeringForTheYankeesIsLike (via @mattsly)‘. Lizzie clearly understood that in order to get many others to play, she would have to get someone with a large set of followers to participate. Lizzie had only around 500 followers at the time, so posted this message in an attempt to seek @jaketapper or @abcdude’s attention.

Sure thing, some twenty minutes later, @abcdude see’s the message and adds his own variation to the meme: ‘#cheeringfortheyankeesislike pulling for Regina George in “Mean Girls.”‘ He enjoys it so much that he promptly posts another message and attaches the hashtag. @abcdude is a new york based correspondent for ABC news. He dubs himself a RedSox fan and a cosmic power broker. Not as cosmic as Lizzie had hoped, but still, he has some 7,000 followers, which could certainly help give the meme some traction. We see a small spike after @abcdude’s participation, and by now, some 3 hours after Matt sent the original message, there have been 34 different messages posted with this unique hashtag.

But it wasn’t until @jaketapper joined in that the conversation really took off. The hashtag came to Jake’s attention after @DetourJazz, whom he follows, participated. Jake reacted by posting:  ’RT @DetourJazz: #cheeringfortheyankeesislike rooting for “Craterface” in Grease to beat Danny (via @Laura_Martin)’. He then added a new message that he posted to his followers. Jake is a senior White House correspondent for ABC news with over 30,000 followers. Before he took part in this meme, new posts appeared at a frequency of one every 5 minutes. Immediately after he joined, we see a sharp rise in participation, with multiple messages from a variety of users every minute.

Seeing it Spread

1. Graphing the Network – Every user who participated in the meme is represented by a gray circle (Matt, whom first started the meme, is shown in yellow). Edges represents the person who most likely influenced the other to first participate.

2. Seeing the Flow – in this applet, a user is represented by their twitter icon. As the timeline moves forward, each profile lights up when they post a new message with the hashtag. Tthe moment that @jaketapper chose to participate is evident – there’s a clear, sudden spike in participation after his profile picture lights up.

3. Seeing the distance – the following applet highlights the total social distance that this hashtag traveled between users. Each user is represented by a circle, the more influence a user has, the larger their circle is drawn. Edges in this example represent the social ties – when there’s a follower/friend relationship between two users, a line is placed between their representation on the screen. The first column includes only Matt who first used the hashtag. The second row consists of only those people he directly influenced to participate (his followers). While there are a total of 9 columns, it is crystal clear that the most important phase happened in the second and third column, when a core cluster of users chose to participate, and a mini tipping point was reached.

Parsing the Data

#CheeringForTheYankeesIsLike lasted for a total of 9 hours that day, activated 271 different users and included around 500 messages in total. From looking at this meme, it is clear that on Twitter, there’s great advantage to having many followers if one intends to spread a message. It is also clear that having the right followers is key. If it were not for @lizzieohreally who knew to actively pass the message onwards to heavy Twitter users, the meme would never have spread out the way it did. In order to come to these conclusions it was necessary for me to look at social ties in addition to the semantics of the messages posted.

I used the Twitter API to discover the follower/friend relationships between all users who participated in this meme. This is extremely important  data, especially when modeling  the flow of participation and influence within this hashtag. For example, lets look at a simple case where user B follows user A. If user A first participates and is followed by user B participating, user A is rewarded some number of influence points – this is assuming user B saw the hashtag posted by user A, and decided to participate. Additionally, if a user is retweeted or ‘@’ messaged they are rewarded some number of influence points. Real life situations can easily become complicated, as user B might also be following user C, who participated in the meme as well. Now how do we know if user B was influenced by user A or user C? Hard to tell, but we can build an influence model that takes these situations into account, which is exactly what I did.

Translating the semantics and social ties from the dataset into a visual language that made sense was key to helping me understand this hashtag experiment. I am a big fan of visualization as a means to parse large datasets, however dealing with social, implicit data is tricky, and extremely challenging to represent visually. But when done right, these representations can shine a whole new light and hopefully help us better understand some of the dynamics at play.

What I’d like to see is a comparison of multiple people at the same time. I’d also like to minimize the timeline so that i can see the whole evening at a time, rather than have to scroll horizontally. I don’t like the aesthetic of circular profiles over a black background. The circular border has too much contrast – some alpha blurring would’ve done the trick nicely. Whats definitely missing most is the multiple channel view, especially now that people aren’t talking as much about the event. In any case, nice to see MTV investing in a Twitter viz. (click below to try it out)

]]> http://giladlotan.com/blog/2009/09/twitter-visualization-mtv-vma-tweet-tracker/feed/ 2 http://giladlotan.com/blog/2008/09/solid-smoke-my-interactive-installation-in-the-swiss-technorama-museum/ http://giladlotan.com/blog/2008/09/solid-smoke-my-interactive-installation-in-the-swiss-technorama-museum/#comments Sun, 14 Sep 2008 22:00:28 +0000 http://giladlotan.com/blog/?p=377 Solid Smoke is an interactive installation I’ve built in collaboration with the Pasadena based artist, Dan Goods, of directedplay. The installation has been recently installed in the the Swiss Technorama science museum.

The installation triggers colored patterns projected onto aerogel, a unique low-density material in which the liquid component of the gel has [...]]]> Solid Smoke is an interactive installation I’ve built in collaboration with the Pasadena based artist, Dan Goods, of directedplay. The installation has been recently installed in the the Swiss Technorama science museum.

The installation triggers colored patterns projected onto aerogel, a unique low-density material in which the liquid component of the gel has been replaced with gas. The result is an ultralight semi-transparent material which absorbs light project within it. Leveraging its visually stunning properties, we created a display which changed patterns and lights according to viewers hand movements. By moving her hand next to the piece, a viewer controls the projections and colors.

The installation runs on a mac-mini, using processing + additional java libraries. It analyzes the images bouncing off IR light project from the ground, triggering events only when viewers interact with the piece. The project will be running until July 2009.

Some tests with aerogel below: