Network Effect is a real-time multimedia sculpture to experiment in a browser.

Yoga Perdana, a graphic desingner from Indonesia that achieves the perfect graphic synthesis.
June 12, 2019
Brand Positioning: The Crucial Code Top Technology Brands Must Master.
June 17, 2019
Show all

Introducing Network Effect, a multimedia sculpture in real time that you can experience in a browser, on your computer.

Network Effect uses a variety of open-source tools and publicly-available data.

The frontend is written in Javascript (using John Resig’s elegant JQuery) and WebGL (using Ricardo Cabello’s excellent Three.js), with GLSL shader code from Felix TurnerAltered Qualia, and Iñigo Quílez. The font used is Gotham.

The backend is written in Python, with hosting on Google App Engine and Google Cloud Storage. The video and audio handling is done with a homespun adaptation of the excellent FFMPEG library.

The data comes from YouTubeTwitterGoogle Books Ngrams, and Google News, with lots of help from Amazon Mechanical Turk to automate many different human tasks. The mapping of IP addresses to countries is provided by Google App Engine, and the life expectancy data is based on a combination of UN and WHO datasets from Wikipedia.

To start the experience click here

They began by choosing a list of 100 canonical human behaviors — basically, a long list of verbs (eat, sleep, stare, etc.). They tried to choose behaviors that were corporeal instead of cerebral (e.g. pointing not thinking) and that were temporally universal (i.e. not dependent on any particular moment in history). With this list in hand, They set about gathering data for each of the 100 behaviors.

To collect the videos, They built a framework to allow Amazon Mechanical Turk workers to find two-second examples of each of the 100 behaviors within existing YouTube videos, and They built a system for reviewing their submissions, until They had gathered 10,000 acceptable videos clips — 100 for each behavior. They adapted the FFMPEG library to automate the process of trimming and cropping the videos, and to double the speed of each video, reducing it from two seconds to one second, to create a frantic and more abstracted depiction of human life. Each clip is credited by title and author, and viewers can access the underlying videos on YouTube through the Credits section of the website. They paid each worker $.25 for each video collected, costing $2,500 in all.

The credits section displays thumbnail images of all 10,000 YouTube video clips that appear in the project, organized by behavior, with each video annotated by title and author. Each thumbnail is hyperlinked to its full original video on YouTube.

To collect the audio, They created a system to query the Twitter API for sentences that mention each of the 100 behaviors, following certain language conventions (e.g. “I swim because…”, “Swimming is…”, etc.). They manually reviewed the resulting sentences, selecting 100 for each behavior. They then created a system to feed these sentences into Amazon Mechanical Turk, asking workers to read them aloud while recording their voice with their cell phone, and then to submit the resulting MP3 files via an HTML form. In this way, They collected 10,000 spoken sentences — 100 for each behavior. They then used our FFMPEG library to merge these sentences into abstracted audio soundscapes, reminiscent of cocktail party chatter (but chatter in which everyone is talking about the same thing). These multilayered sound montages are paired with the videos to create 100 strange and overwhelming audio-visual environments.

To collect the brands data, They created an Amazon Mechanical Turk survey, asking workers to name the top five brands they would associate with each of the 100 behaviors. They ran this survey 100 times per behavior, discarded brands with less than two guesses, and then took the top ten results based on the number of votes. In the world of technology, this kind of exercise is known as a “Wisdom of the Crowds” survey, in which a large group of non-expert participants are asked to guess a response to a question that would otherwise be difficult to answer. The Wisdom of the Crowds theory posits that such a crowd possesses a kind of collective intelligence, capable of producing answers that are close to the truth.

To collect the definitions data, They wrote a system to query the Twitter API for sentences containing each of the 100 behaviors, immediately followed by the word “is” (e.g. “Swimming is…”). In this way, They were able to gather a large and ever-growing corpus of definitions for each behavior, crowdsourced from tweets by thousands of individuals. Based on the local time of each tweet, They were able to create an hourly histogram of definitions, showing, for instance, definitions of drinking offered at 10AM versus 10PM. The definitions data continues to update hourly.

To collect the gender data, They wrote a system to query Google Books Ngrams (a corpus of millions of digitized books — about 6% of all books ever printed) for each of the 100 behaviors from 1900–2008. Each year, They compared the relative prevalence of male pronouns (he, him, his) with female pronouns (she, her, hers) in close proximity to each behavior’s different verb forms (e.g. run, running, ran). In this way, They were able to deduce the gender breakdown for each of the 100 behaviors over the past century. The data communicates the strong female bias of certain activities (e.g. knit, shop, cry) and the strong male bias of others (e.g. shoot, sweat, puke), as well as the various gender shifts occurring over time.

To collect the news data, They wrote a system to crawl Google News for news headlines mentioning any of the 100 behaviors, anytime from January 2004 to the present day. The news stories can be filtered by month and year using an interactive histogram. In this way, They were able to assemble a news archive for human behavior, with strange groupings like news about kissing, pointing, and staring. The news data continues to update hourly.

To collect the people data, They created an Amazon Mechanical Turk survey, asking workers to guess how many people in the world were doing each of 100 behaviors right at that moment. They ran this survey 100 times for each behavior, discarded the top and bottom 5% of guesses, and then averaged the rest to arrive at a number. In this way, They were able to venture a reasonable guess at the relative prevalence of each activity in the world at any moment in time, using a method that is, if not scientific, then at least systematic. In the world of technology, this kind of exercise is known as a “Wisdom of the Crowds” survey, in which a large group of non-expert participants are asked to guess a response to a question that would otherwise be difficult to answer. The Wisdom of the Crowds theory posits that such a crowd possesses a kind of collective intelligence, capable of producing answers that are close to the truth.

To collect the reasons data, They wrote a system to query the Twitter API for sentences containing each of the 100 behaviors, immediately preceded by the word “I” and immediately followed by the word “because” (e.g. “I swim because…”). In this way, They were able to gather a large and ever-growing corpus of reasons for each behavior, crowdsourced from tweets by millions of individuals. Based on the local time of each tweet, They were able to create an hourly histogram of reasons, showing, for instance, reasons people “touch” at noon versus midnight. The reasons data continues to update hourly.

To collect the usage data, They wrote a system to query Google Books Ngrams (a corpus of millions of digitized books — about 6% of all books ever printed) for each of the 100 behaviors from 1900–2008. For each year, They tallied the prevalence of different verb forms for each behavior (e.g. run, running, ran). In this way, They were able to create a kind of historical stock market for human behavior, showing the rise and fall of different activities over the course of a century. The data communicates the overall dominance of giving, the rise of talking, smiling, staring, and panicking, and the decline of crying and writing, among many other trends.

To collect the words data, They wrote a system to query Google Books Ngrams (a corpus of millions of digitized books — about 6% of all books ever printed) for each of the 100 behaviors from 1900–2008, including each behavior’s various verb forms (e.g. run, running, ran). For each behavior, They tallied the overall prevalence of different neighboring words, organized by part of speech (i.e nouns, verbs, adjectives, adverbs). In this way, They were able to construct a statistical linguistic landscape for each behavior. For instance, the data communicates that They mainly fight war, battles, urges, and tears, and that the ways in which we fight are often bravely, desperately, manfully, and alone.

To compute the time limit for each viewer, They wrote a system to approximate their country of origin based on their IP address, and then used a life expectancy database to find the average life expectancy for people living in that particular country. They then translate years to minutes (e.g. 79.5 years = 7.95 minutes = 7:57), to determine how long each viewer will have to explore the project, before being blocked for twenty-four hours. The time limit is intended to induce a feeling of panic and anxiety, consistent with the feeling of Internet culture itself.

While watching a given behavior’s video clips, if the viewer presses and holds the mouse button, the “Chatter” movement appears, revealing the words, “MORE MORE.” Repeated mouse presses cause additional MOREs to be added to the sequence, eventually overtaking the screen with a potentially infinite number of MOREs.

CREDITS

JONATHAN HARRIS

Jonathan Harris is an artist and computer scientist, known for his work with data poetics and storytelling. He is the creator of classic interactive projects like We Feel Fine10×10The Whale Hunt, and I Love Your Work. A Webby winner, Fabrica fellow, Hemera fellow, and World Economic Forum Young Global Leader, his TED talks have been viewed millions of times, and his artwork is in the permanent collection of MoMA.

GREG HOCHMUTH

Greg Hochmuth is an artist and engineer specializing in data science. He studied computer science and design at Stanford University, and then worked as a product manager, engineer, and data analyst at GoogleInstagram, and Facebook. Greg also operates DADA, an agency focused on data engineering and insights.

 

Facebook
Twitter
Linkedin0
Google+0
GMail0
SMS0
Whatsapp0
Positive SSL