Gilad Lotan » design

The Anatomy of a Viral Tweet

gilad — Wed, 15 Jun 2011 15:31:05 +0000

Here’s the crib of my 140#conf NYC talk, given on June 15th at the 92nd st Y:

I’m here to talk to you about my work on digital audiences, with a focus on information flows. I’m sure that to this crowd I don’t have to stress the potential that social media is unlocking. Whether you’re a brand, knitting circle or just an individual surfing the web, social media is an invaluable medium to seek and disseminate important information in realtime.

We are all part of the emerging information economy, building and using applications that create overflowing streams of information. Social network sites create compelling spaces, where social interactions act as lubricants, accelerating the flow of information. Users are encouraged to respond, add to, consume and redirect content. As information flows by, some may grab a piece when it is most relevant, valuable, entertaining or insightful, and at times, choose to pass it onwards.

Attention = Power

While the threshold to publishing nears zero, attention has become the bottleneck. One cannot demand attention anymore, or expect to have it at certain times of the day. We all need to understand the preferences and behavior of our respective audiences, and adapt our own behavior in order to attract the attention of others. The ability to attract attention is power, and in this 140-character economy, understanding how people manage their attention is incredibly powerful.

Because information spreads through people, networks of friends, fans and followers, by understanding information flows we have the ability to unlock insight about where people place their attention. Some data spread at an unpredictable viral speed, while the majority are only seen by a handful. In order for messages to propagate, people along the way must be attentive: notice them at the right time, and pass them onwards. How this happens is the million dollar question. Here are some examples:

Gaining your Network’s Trust

This is a visualization from a recent study we published about the spread of the tweet on the Osama Bin Laden operation. Media that monday morning was focused on the story that “Twitter broke the news”. Over an hour before the formal white house announcement, people on twitter had figured out that it was Bin Laden. There was much speculation on why the presidential announcement had to take place on sunday night. Some were on the Gaddafi side, and others, Bin Laden.

It was a single tweet that triggered an in credibly fast information cascade. A single tweet from Keith Urbahn, Donald Rumsfeld’s chief of staff, drew 80 retweets within a minute, and generated over 300 within two. This message spread like wildfire. (see study for more detail)

Before May 1st, not even the smartest of machine learning algorithms could have predicted Keith Urbahn’s likelihood to spread information on this topic, or his potential to spark an incredibly viral information flow. While politicos “in the know” certainly knew him or of him, his previous interactions and size and nature of his social graph did little to reflect his potential to generate thousands of people’s willingness to trust within a matter of minutes.

Tight Knit Network or “Tribe”

Another example of an interesting information spread is that of Urban Outfitters vs. the NYC crafters community. In this case, the ‘I heart NYC’ necklace design was ripped off an independent designer by UO. The artists put up a blog post, and Amber Karnes published the following post to Twitter:

This led to an avalanche of reactions, to the level that her username was trending in LA, Portland, New York, Toronto and then the United States.

In an incredibly insightful post, Amber wrote:

I am not a Twitter celebrity by any means. I barely had over 1,000 followers when the day began and I’m pretty sure about 200 of those are spam-bots. What I do have – and the reason that my call for a boycott on Urban Outfitters spread so fast and wide – is a tribe. A tight knit group of independent artists and crafters that follow me. My cause resounded with them. They spread it, and their friends spread it, and a few big influencers on Twitter spread it, and then it was gone.

Topic + Network + Timing

We see this over and over again: The right social-professional networked audience, along with a relevant piece of information, all at the right time, led to an explosion of public affirmation, many times, unexpected by the author.

Paradox of Social Networks

While networked sociality promises us equal opportunity, the ease and frictionless connection to literally anyone across the globe. But what plays out is in effect very different. As James Gleik notes in his seminal book ‘The Information’: “The structure of the social web stands upon a paradox. Everything is close, and everything is far at the same time.”

These small world networks usually offer 4 degrees of separation. And even though the distances between people may seem short, finding the right route that will provide us with the wanted outcome is extremely difficult. This is why cyberspace can feel not just crowded but lonely .You can drop a stone into a well and never hear a splash. But alternatively, you can be received with a flood of water. And while the latter is less common, the more people spend their time in SNS, we’re seeing that happen again and again.

the promise of data

Whether you’re interested in socializing or in selling a product, understanding your network’s habits around information consumption and production is imperative to attaining people’s attention, and building an engaged audience. We all build these mental models in our heads, imagining our invisible audiences; the people who give us attention. But as long as its all in our heads, it doesn’t scale. We need to build and use tools that drive insight and help us find effective ways to makes sense of all the digital breadcrumbs left by our online audiences.

Psyched to be working at the heart of this.

SocialFlow – 140confNYC

Seeing your Invisible Audience

gilad — Thu, 02 Dec 2010 19:42:21 +0000

::Making Sense of the Ebbs and Flow of Social Data

Below are notes + slides of my presentation at the BRANDSconf. I’d like to acknowledge Hunter Whitney. Portions of this content were based on a discussion and an upcoming article he is writing on this topic (link coming shortly):

I’m extremely passionate about data analysis and design. My work focuses on the intersection of the two. I play with data, and figure out ways to make it more accessible to people. I’m here to talk about why the art of making sense of massive amounts of social data is critical not only for geeks like me, but any professional using Twitter. And my goal is to get YOU all excited about the opportunity that understanding data unveils for us.

Whether you’re a multi-national enterprise, a local deli or a mah-jong meetup, the proliferation of social network services like Twitter have created an expectation that you interact with your customers, users and followers. There’s an expectation to connect rather than broadcast. We’ve been hearing this over and over this morning – you are a brand. And as a brand you are expected to interact with your audience like a person would interact with others. You need to engage in conversations, provide and receive feedback, network, create hype, and do all this in a timely manner.

But how can we be expected to interact with an ever growing and diverse group of people when we can’t really “see” them?

Giving Shape to our Audience

Judith Donath of Harvard’s Berkman Center talks about human signaling and how that translates to digital spaces. I get a variety of signals from merely standing in front of you all – your age, what you’re wearing, how you’re feeling, whose smiling and whose already fallen asleep. Being here, with you, part of this event, I have context that helps me understand how best to interact with you all. I’ll happily switch to speaking Hebrew, but obviously that will not be helpful. Even the little bit that I know about you helps me make some useful assumptions – speak English, tune down the analytics/mathematics terms, tune up the user experience/brand jargon.

Social network spaces are fueled by social interactions. Think of people’s interactions online as digital breadcrumbs, trails of connections, likes, thoughts and opinions. By piecing together these crumbs we can start making sense of the people giving us attention on Social Network sites. We must use as much of the tools available to mine the data about our audience – location, time of day, language, interests. In order to interact with an audience we need to be able to sense it.

There are a variety of tools that give us this opportunity to mine content. This is only the first step. We need to put an emphasis on looking at the connections between people, and not only the content that is being published.

The Social Graph

Social Graph is a term that I’m certain you all will hear more and more as social network spaces become a fundamental component of our lives. A social graph is a dataset that represents people and their inter-connections within a group. Mark Zuckerberg is known for popularizing the term in his description of the value that Facebook Connect brings to websites. Facebook’s social graph is made up of you all who I’m sure have accounts, and all your connections. Additionally, that graph distinguishes between types of connections – whether colleagues, friends or family.

Twitter’s social graph is different. Its a directed, which means that connections have directions. The person who you follow does not necessarily follow you back. Twitter’s social graph is fascinating because it maps people’s interests; what people are willing to give their attention to. By understanding people’s interests over time as well as their interconnections, we have the ability to identify we can reveal valuable points such as (1) bridges: people who connect two distinct communities (2) influencers: those who can get their audience to participate (3) experts: people who specialize on a specific topic (4) hustlers: culture creators.

While it is fairly straightforward to aggregate large datasets, we are still challenged by making sense of graph based data. These constantly changing graph indexes are massive at scale and may require complex queries in realtime: whats the shortest path between person A and person B, whats the intersection between group C and D or whats the clustering coefficients amongst group E. Once calculated, these results reflect on the intricacies of people’s relationships, and shedding light on properties that directly affect their behavior: influence, trust, authority and personal preference.

Understanding information flows

In the social web, information spreads through people, networks of friends, fans and followers. Social network sites create compelling spaces where users feel comfortable to hang out, interact, consume, poke and publish. Social interactions lubricate the flow of information within these spaces, creating a plethora of dynamics. These spaces are filled with endless streams of content, encouraging users to participate, add to, consume from and redirect content. As information flows by, users grab content when it is most relevant, valuable, entertaining or insightful, and at times, choose to pass it on.

Because information flows through networks of people, attention has become a scarce commodity. This is truly a game changer. Media companies no longer control people’s attention, but are rather fighting for a smaller section of the pie. True power lies in understanding how information flows and its effect of where people choose to focus their attention. In order for messages to propagate through social networks, people along the way must be attentive to the pieces of information, see them at the right time, and pass them onwards.

Whether you’re interested in socializing or in selling a product, understanding people’s habits around information consumption and production is imperative to attaining people’s attention and building an audience. By leveraging the publicly available data around people’s practices, we can create services that shed a light on people’s habits and preferences. Additionally, by mining this data over time, we can infer their value in affecting information flows.

::demo:: seeing a Twitter Hashtag Spread

I’ve been following @jeffpulver for a while now and know that he’s quite generous in terms of attention. A great time to catch Jeff is in the morning (wherever he is), as he sends out a ‘good morning’ Tweet, there tend to be reciprocal pings and messages. I also know Jeff is interested in new developments in the Israeli startup scene. If I have any juicy piece of information on that topic, I’d make sure to post it, possibly with a /cc/ to Jeff, and ideally around his morning time. I have a mental model in my head, around Jeff’s practices in consuming and producing content.

We all do this, but can only capture so much in our heads. We need tools that scale and capture our networks as a whole and not just individuals. Remember, its not necessarily about the size of an audience or someone’s number of followers, but rather who they are and who they’re connected to.

That all sounds really great, but in effect, representing large graph datasets can easily get out of hand, however loved by geeks, usually becomes a tangled mass of lines and dots. We must remember that this data is beneficial only if people are able to make sense of it. We need to think about interfaces that will let us play with the data; slice and dice the parts that we deem relevant or interesting. In addition to an intuitive interface, we need controls that will help us dive into and observe patterns or connections that would have otherwise been hidden.

Closing

There are three points I want to make sure you all come out of this talk thinking about:

1) Mine Digital Breadcrumbs – use the exiting tools to get a sense for how our audience looks and its segmentation (I’ve made a oneforty kit here)

2) Social Graphs are Extremely Useful – yet complex to aggregate and mine.

3) understanding information flows is Powerful – especially as we’re shifting from broadcast mode to that of engagement

Social network analytics tools may fundamentally change the way we engage with our online audiences. We need to build better tools that do the above mentioned tasks. But I need people like you all to be vocal about your needs and frustrations. As we’re building out these technologies, we want to make sure they are tailored to real needs. We’re only at the start of the journey, and I’m super excited to be a part of it!

Aerogel Installation at TEDActive

gilad — Sat, 20 Feb 2010 04:37:47 +0000

I was fortunate to be invited this year as one of the contributing artists at TEDActive. This is the simulcast event that happens in Palm Springs at the same time that the main TED event takes place in Long Beach. The organizers frame it not as “TED jr.” but rather a more intimate version of TED; in essence, what it used to be like before it became a 1500 person event.

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:

Seeing a Twitter #Hashtag Spread

gilad — Wed, 20 Jan 2010 20:51:05 +0000

#CheeringForTheYankeesIsLike is a hashtag created by @mattsly the morning of October 26th. He submitted the following snarky message – ‘Go Phillies. #CheeringForTheYankeesIsLike hoping investment bankers get really huge bonuses of at least 8 figures‘ – hoping to entertain his friends, and possibly get others to participate. Matt had 182 followers at the time, not sizeable by any means on Twitter. Little did he expect that some 9 hours later, 271 different users, most of whom have no connection to him whatsoever, would participate, posting around 500 messages in total.

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.

Twitter Visualization: MTV VMA Tweet Tracker

gilad — Tue, 15 Sep 2009 15:26:34 +0000

New Twitter visualization of the MTV video music awards by Stamen design. I enjoy using the well-designed interface for “re-experiencing” the series of events as they evolved that evening. I like the simplicity of the timeline at the bottom, and just how easy it is to navigate between times. What I also really like is the sparkline-esque graph showing the person’s popularity throughout the evening.

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)

Flickr Clock

Mon, 09 Mar 2009 19:12:14 +0000

There’s something really wonderful about using the flickr clock interface to browse through people’s moments from around the world. The clock displays only video content and lets you lean back and just click to view another short clip which someone uploaded somewhere around the world.

flickr clock

I love the horizontal timeline below the image slices and I think the general browsing experience is well done. Its always an interesting challenge lining up events horizontally on one level. For example, when browsing through this clock, I first encounter a clip from the San Francisco Pillow Fight. However, when I click on the arrow I am shown a cat sitting by a windowsill. All these events happen at the same time, and are as important for the person who placed them on the clock.

flickr clock timeline

Applications like these make me wonder how we will be consuming news in the future. Will we have a super-set of timelines streaming real-time video people are taking from events around the world? How easy will it be to switch from my personal channel (i.e. my cat on the windowsill) to my relevant events (for example places I’ve been to, people I follow or more general newsworthy events).

[tags] visualization, timeline, clock, flickr,video,news,events,pnn[/tags]

Making round DIVs

Mon, 22 Sep 2008 21:52:27 +0000

Here’s the image-based method I used to make rounded DIVs. Use the following HTML code along with the CSS properties and .gif files. The results are a lot of fun and look quite good:

[tags]html,code,programming,round,div,css[/tags]

Solid Smoke – my interactive installation in the Swiss Technorama museum

Sun, 14 Sep 2008 22:00:28 +0000

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:

[tags] aerogel, interactive, exhibit, installation, technorama, science, museum, switzerland, gilad, lotan, dan, goods [/tags]

Ma’ariv Site Redesign

Tue, 13 May 2008 21:57:42 +0000

Ma’ariv, one of Israel’s most popular newspapers (both print + web), has gone through a comprehensive site re-design. Its about time they changed their horrid color palette (reds and yellows), to a much cleaner set (grays) with a well structured UI, which actually feels somewhat like nytimes.com.

[tags]hebrew,news,web,design,nrg,maariv [/tags]

Visual Delights for Browsing

Sat, 08 Mar 2008 06:39:51 +0000

Whoa. In the past couple of days I’ve discovered fresh web-based visual effects that give a lot of joy:
1) I am a big supporter of Gmail labels and filters. Since it is my main email box, I use it to subscribe group lists, bills, google alerts as well as receiving personal emails. Labels and filters are extremely useful to help sift through all the messages and for sub-queues – one for personal emails, another for this list, a third for my alerts, etc… Several days ago I noticed that gmail added a coloring scheme (this might be old news?) which lets you set labels to colors – this way I can visually browse through my queue and get a sense for whats waiting for me.

2) Netvibes just came out with a completely new design – visually and conceptually. They provide their users with a private page (what I’ve been using for years) to aggregate RSS feeds, as well as their new ‘ginger’ public interface. Finally they are starting to do interesting things with their user-base. So this is my public page, well equipped with my blog feed, my flickr&twitter feeds, my facebook badge and yet another ‘wall’ for you all to scribble on ::groan:: I know, am way over that. They include a followers/activities tab which just might help make this service catch – its extremely useful for your own use when organizing RSS feeds, but can be extremely helpful when this metadata is shared among a social group. Let people organize the data according to their interests => provide a platform for them to share amongst each other => own a “catchy”, highly used service => … make money ?

Don’t know about the last part, but I’m definitely enjoying playing around with the designs:

3) Piclens is a fantastic way to browse through images. Installs as a browser-app. into firefox and allows to creates a horizontal for viewing images from websites. Cool, slick and intuitive interface. This is how my flickr stream looks through the app:

[tags]visual,design,browsing,netvibes,ginger,gmail,piclens[/tags]