media

The false promise of the digital humanities

Curated on May 3, 2014August 3, 2018 by Stefaan Verhulst

Adam Kirsch in the New Republic: “The humanities are in crisis again, or still. But there is one big exception: digital humanities, which is a growth industry. In 2009, the nascent field was the talk of the Modern Language Association (MLA) convention: “among all the contending subfields,” a reporter wrote about that year’s gathering, “the digital humanities seem like the first ‘next big thing’ in a long time.” Even earlier, the National Endowment for the Humanities created its Office of Digital Humanities to help fund projects. And digital humanities continues to go from strength to strength, thanks in part to the Mellon Foundation, which has seeded programs at a number of universities with large grants—most recently, $1 million to the University of Rochester to create a graduate fellowship.

Despite all this enthusiasm, the question of what the digital humanities is has yet to be given a satisfactory answer. Indeed, no one asks it more often than the digital humanists themselves. The recent proliferation of books on the subject—from sourcebooks and anthologies to critical manifestos—is a sign of a field suffering an identity crisis, trying to determine what, if anything, unites the disparate activities carried on under its banner. “Nowadays,” writes Stephen Ramsay in Defining Digital Humanities, “the term can mean anything from media studies to electronic art, from data mining to edutech, from scholarly editing to anarchic blogging, while inviting code junkies, digital artists, standards wonks, transhumanists, game theorists, free culture advocates, archivists, librarians, and edupunks under its capacious canvas.”

Within this range of approaches, we can distinguish a minimalist and a maximalist understanding of digital humanities. On the one hand, it can be simply the application of computer technology to traditional scholarly functions, such as the editing of texts. An exemplary project of this kind is the Rossetti Archive created by Jerome McGann, an online repository of texts and images related to the career of Dante Gabriel Rossetti: this is essentially an open-ended, universally accessible scholarly edition. To others, however, digital humanities represents a paradigm shift in the way we think about culture itself, spurring a change not just in the medium of humanistic work but also in its very substance. At their most starry-eyed, some digital humanists—such as the authors of the jargon-laden manifesto and handbook Digital_Humanities—want to suggest that the addition of the high-powered adjective to the long-suffering noun signals nothing less than an epoch in human history: “We live in one of those rare moments of opportunity for the humanities, not unlike other great eras of cultural-historical transformation such as the shift from the scroll to the codex, the invention of movable type, the encounter with the New World, and the Industrial Revolution.”

The language here is the language of scholarship, but the spirit is the spirit of salesmanship—the very same kind of hyperbolic, hard-sell approach we are so accustomed to hearing about the Internet, or about Apple’s latest utterly revolutionary product. Fundamental to this kind of persuasion is the undertone of menace, the threat of historical illegitimacy and obsolescence. Here is the future, we are made to understand: we can either get on board or stand athwart it and get run over. The same kind of revolutionary rhetoric appears again and again in the new books on the digital humanities, from writers with very different degrees of scholarly commitment and intellectual sophistication.

In Uncharted, Erez Aiden and Jean-Baptiste Michel, the creators of the Google Ngram Viewer—an online tool that allows you to map the frequency of words in all the printed matter digitized by Google—talk up the “big data revolution”: “Its consequences will transform how we look at ourselves…. Big data is going to change the humanities, transform the social sciences, and renegotiate the relationship between the world of commerce and the ivory tower.” These breathless prophecies are just hype. But at the other end of the spectrum, even McGann, one of the pioneers of what used to be called “humanities computing,” uses the high language of inevitability: “Here is surely a truth now universally acknowledged: that the whole of our cultural inheritance has to be recurated and reedited in digital forms and institutional structures.”

If ever there were a chance to see the ideological construction of reality at work, digital humanities is it. Right before our eyes, options are foreclosed and demands enforced; a future is constructed as though it were being discovered. By now we are used to this process, since over the last twenty years the proliferation of new technologies has totally discredited the idea of opting out of “the future.”…

Saving Big Data from Big Mouths

Curated on May 1, 2014July 18, 2019 by Stefaan Verhulst

Cesar A. Hidalgo in Scientific American: “It has become fashionable to bad-mouth big data. In recent weeks the New York Times, Financial Times, Wired and other outlets have all run pieces bashing this new technological movement. To be fair, many of the critiques have a point: There has been a lot of hype about big data and it is important not to inflate our expectations about what it can do.
But little of this hype has come from the actual people working with large data sets. Instead, it has come from people who see “big data” as a buzzword and a marketing opportunity—consultants, event organizers and opportunistic academics looking for their 15 minutes of fame.
Most of the recent criticism, however, has been weak and misguided. Naysayers have been attacking straw men, focusing on worst practices, post hoc failures and secondary sources. The common theme has been to a great extent obvious: “Correlation does not imply causation,” and “data has biases.”
Critics of big data have been making three important mistakes:
First, they have misunderstood big data, framing it narrowly as a failed revolution in social science hypothesis testing. In doing so they ignore areas where big data has made substantial progress, such as data-rich Web sites, information visualization and machine learning. If there is one group of big-data practitioners that the critics should worship, they are the big-data engineers building the social media sites where their platitudes spread. Engineering a site rich in data, like Facebook, YouTube, Vimeo or Twitter, is extremely challenging. These sites are possible because of advances made quietly over the past five years, including improvements in database technologies and Web development frameworks.
Big data has also contributed to machine learning and computer vision. Thanks to big data, Facebook algorithms can now match faces almost as accurately as humans do.
And detractors have overlooked big data’s role in the proliferation of computational design, data journalism and new forms of artistic expression. Computational artists, journalists and designers—the kinds of people who congregate at meetings like Eyeo—are using huge sets of data to give us online experiences that are unlike anything we experienced in paper. If we step away from hypothesis testing, we find that big data has made big contributions.
The second mistake critics often make is to confuse the limitations of prototypes with fatal flaws. This is something I have experienced often. For example, in Place Pulse—a project I created with my team the M.I.T. Media Lab—we used Google Street View images and crowdsourced visual surveys to map people’s perception of a city’s safety and wealth. The original method was rife with limitations that we dutifully acknowledged in our paper. Google Street View images are taken at arbitrary times of the day and showed cities from the perspective of a car. City boundaries were also arbitrary. To overcome these limitations, however, we needed a first data set. Producing that first limited version of Place Pulse was a necessary part of the process of making a working prototype.
A year has passed since we published Place Pulse’s first data set. Now, thanks to our focus on “making,” we have computer vision and machine-learning algorithms that we can use to correct for some of these easy-to-spot distortions. Making is allowing us to correct for time of the day and dynamically define urban boundaries. Also, we are collecting new data to extend the method to new geographical boundaries.
Those who fail to understand that the process of making is iterative are in danger of being too quick to condemn promising technologies. In 1920 the New York Times published a prediction that a rocket would never be able to leave atmosphere. Similarly erroneous predictions were made about the car or, more recently, about iPhone’s market share. In 1969 the Times had to publish a retraction of their 1920 claim. What similar retractions will need to be published in the year 2069?
Finally, the doubters have relied too heavily on secondary sources. For instance, they made a piñata out of the 2008 Wired piece by Chris Anderson framing big data as “the end of theory.” Others have criticized projects for claims that their creators never made. A couple of weeks ago, for example, Gary Marcus and Ernest Davis published a piece on big data in the Times. There they wrote about another of one of my group’s projects, Pantheon, which is an effort to collect, visualize and analyze data on historical cultural production. Marcus and Davis wrote that Pantheon “suggests a misleading degree of scientific precision.” As an author of the project, I have been unable to find where I made such a claim. Pantheon’s method section clearly states that: “Pantheon will always be—by construction—an incomplete resource.” That same section contains a long list of limitations and caveats as well as the statement that “we interpret this data set narrowly, as the view of global cultural production that emerges from the multilingual expression of historical figures in Wikipedia as of May 2013.”
Bickering is easy, but it is not of much help. So I invite the critics of big data to lead by example. Stop writing op–eds and start developing tools that improve on the state of the art. They are much appreciated. What we need are projects that are worth imitating and that we can build on, not obvious advice such as “correlation does not imply causation.” After all, true progress is not something that is written, but made.”

Mapping the Intersection Between Social Media and Open Spaces in California

Curated on May 1, 2014August 3, 2018 by Stefaan Verhulst

Stamen Design: “Last month, Stamen launched parks.stamen.com, a project we created in partnership with the Electric Roadrunner Lab, with the goal of revealing the diversity of social media activity that happens inside parks and other open spaces in California. If you haven’t already looked at the site, please go visit it now! Find your favorite park, or the parks that are nearest to you, or just stroll between random parks using the wander button. For more background about the goals of the project, read Eric’s blog post: A Conversation About California Parks.
In this post I’d like to describe some of the algorithms we use to collect the social media data that feeds the park pages. Currently we collect data from four social media platforms: Twitter, Foursquare, Flickr, and Instagram. We chose these because they all have public APIs (Application Programming Interfaces) that are easy to work with, and we expect they will provide a view into the different facets of each park, and the diverse communities who enjoy these parks. Each social media service creates its own unique geographies, and its own way of representing these parks. For example, the kinds of photos you upload to Instagram might be different from the photos you upload to Flickr. The way you describe experiences using Twitter might be different from the moments you document by checking into Foursquare. In the future we may add more feeds, but for now there’s a lot we can learn from these four.
Through the course of collecting data from these social network services, I also found that each service’s public API imposes certain constraints on our queries, producing their own intricate patterns. Thus, the quirks of how each API was written results in distinct and fascinating geometries. Also, since we are only interested in parks for this project, the process of culling non-park-related content further produces unusual and interesting patterns. Rural areas have large parks that cover huge areas, while cities have lots of (relatively) tiny parks, which creates its own challenges for how we query the APIs.
Broadly, we followed a similar approach for all the social media services. First, we grab the geocoded data from the APIs. This ignores any media that don’t have a latitude and longitude associated with them. In Foursquare, almost all checkins have a latitude and longitude, and for Flickr and Instagram most photos have a location associated with them. However, for Twitter, only around 1% of all tweets have geographic coordinates. But as we will see, even 1% still results in a whole lot of tweets!
After grabbing the social media data, we intersect it with the outlines of parks and open spaces in California, using polygons from the California Protected Areas Database maintained by GreenInfo Network. Everything that doesn’t intersect one of these parks, we throw away. The following maps represent the data as it looks before the filtering process.
But enough talking, let’s look at some maps!”

Cyberlibertarians’ Digital Deletion of the Left

Curated on April 29, 2014August 3, 2018 by Stefaan Verhulst

David Golumbia in Jacobin: “The digital revolution, we are told everywhere today, produces democracy. It gives “power to the people” and dethrones authoritarians; it levels the playing field for distribution of information critical to political engagement; it destabilizes hierarchies, decentralizes what had been centralized, democratizes what was the domain of elites.
Most on the Left would endorse these ends. The widespread availability of tools whose uses are harmonious with leftist goals would, one might think, accompany broad advancement of those goals in some form. Yet the Left today is scattered, nearly toothless in most advanced democracies. If digital communication technology promotes leftist values, why has its spread coincided with such a stark decline in the Left’s political fortunes?
Part of this disconnect between advancing technology and a retreating left can be explained by the advent of cyberlibertarianism, a view that widespread computerization naturally produces democracy and freedom.
In the 1990s, UK media theorists Richard Barbrook and Andy Cameron, US journalist Paulina Borsook, and US philosopher of technology Langdon Winner introduced the term to describe a prominent worldview in Silicon Valley and digital culture generally; a related analysis can be found more recently in Stanford communication scholar Fred Turner’s work. While cyberlibertarianism can be defined as a general digital utopianism, summed up by a simple slogan like “computerization will set us free” or “computers provide the solution to any and all problems,” these writers note a specific political formation — one Winner describes as “ecstatic enthusiasm for electronically mediated forms of living with radical, right-wing libertarian ideas about the proper definition of freedom, social life, economics, and politics.”
There are overt libertarians who are also digital utopians — figures like Jimmy Wales, Eric Raymond, John Perry Barlow, Kevin Kelly, Peter Thiel, Elon Musk, Julian Assange, Dread Pirate Roberts, and Sergey Brin, and the members of the Technology Liberation Front who explicitly describe themselves as cyberlibertarians. But the term also describes a wider ideological formation in which people embrace digital utopianism as compatible or even identical with leftist politics opposed to neoliberalism.
In perhaps the most pointed form of cyberlibertarianism, computer expertise is seen as directly applicable to social questions. In The Cultural Logic of Computation, I argue that computational practices are intrinsically hierarchical and shaped by identification with power. To the extent that algorithmic forms of reason and social organization can be said to have an inherent politics, these have long been understood as compatible with political formations on the Right rather than the Left.
Yet today, “hacktivists” and other promoters of the liberatory nature of mass computerization are prominent political voices, despite their overall political commitments remaining quite unclear. They are championed by partisans of both the Right and the Left as if they obviously serve the political ends of each. One need only reflect on the leftist support for a project like Open Source software to notice the strange and under-examined convergence of the Right and Left around specifically digital practices whose underlying motivations are often explicitly libertarian. Open Source is a deliberate commercialization of Richard Stallman’s largely noncommercial notion ofFree Software (see Stallman himself on the distinction). Open Source is widely celebrated by libertarians and corporations, and was started by libertarian Eric Raymond and programmer Bruce Perens, with support from businessman and corporate sympathizer Tim O’Reilly. Today the term Open Source has wide currency as a political imperative outside the software development community, despite its place on the Right-Left spectrum being at best ambiguous, and at worst explicitly libertarian and pro-corporate.
When computers are involved, otherwise brilliant leftists who carefully examine the political commitments of most everyone they side with suddenly throw their lot in with libertarians — even when those libertarians explicitly disavow Left principles in their work…”

Minecraft: All of Denmark virtually recreated

Curated on April 26, 2014October 31, 2018 by Stefaan Verhulst

BBC: “The whole of Denmark has been recreated, to scale, within the virtual world of Minecraft. The whole country has been faithfully reproduced in the hugely popular title’s building-block style by the Danish government. Danish residents are urged to “freely move around in Denmark” and “find your own residential area, to build and tear down”.
Around 50 million copies of Minecraft have been sold worldwide.Known as a “sandbox” game, the title allows players to exist in a virtual world, using building blocks to create everything from basic structures to entire worlds. Minecraft was launched in 2011 by independent Swedish developer Markus “Notch” Persson.
The Danish government said the maps were created to be used as an educational tool – suggesting “virtual field trips” to hard-to-reach parts of the country.
Flat roofs
There are no specific goals to achieve other than continued survival. Recreating real-world locations is of particular interest for many players. Last year an intern working with the UK’s Ordnance Survey team built geographically accurate landscapes covering 86,000 sq miles (224,000 sq km) of Britain.The Danish project is more ambitious however, with buildings and towns reproduced in more detail. The only difference, the team behind it said, was that all roofs were flat.
It has also banned the use of one of the game’s typical tools – dynamite. The full map download of Denmark will be available until 23 October.”

Wikipedia Use Could Give Insights To The Flu Season

Curated on April 21, 2014May 29, 2019 by Stefaan Verhulst

Agata Blaszczak-Boxe in Huffington Post: “By monitoring the number of times people look for flu information on Wikipedia, researchers may be better able to estimate the severity of a flu season, according to a new study.
Researchers created a new data-analysis system that looks at visits to Wikipedia articles, and found the system was able to estimate flu levels in the United States up to two weeks sooner than the flu data from the Centers for Disease Control and Prevention were released.
Looking at data spanning six flu seasons between December 2007 and August 2013, the new system estimated the peak flu week better than Google Flu Trends, another data-based system. The Wikipedia-based system accurately estimated the peak flu week in three out of six seasons, while the Google-based system got only two right, the researchers found.
“We were able to get really nice estimates of what the [flu] level is in the population,” said study author David McIver, a postdoctoral fellow at Boston Children’s Hospital.
The new system examined visits to Wikipedia articles that included terms related to flulike illnesses, whereas Google Flu Trends looks at searches typed into Google. The researchers analyzed the data from Wikipedia on how many times in an hour a certain article was viewed, and combined their data with flu data from the CDC, using a model they created.
The research team wanted to use a database that is accessible to everyone and create a system that could be more accurate than Google Flu Trends, which has flaws. For instance, during the swine flu pandemic in 2009, and during the 2012-2013 influenza season, Google Flu Trends got a bit “confused,” and overestimated flu numbers because of increased media coverage focused on the two illnesses, the researchers said.
When a pandemic strikes, people search for news stories related to the pandemic itself, but this doesn’t mean that they have the flu. In general, the problem with Internet-based estimation systems is that it is practically impossible to tell whether people are looking for information about an illness because they are sick, the researchers said.
In the new system, the researchers tried to overcome this issue by including a number of Wikipedia articles “to act as markers for general background-level activity of normal usage of Wikipedia,” the researchers wrote in the study. However, just like any other data-based system, the Wikipedia system is not immune to the issues related to figuring out the actual motivation of someone checking information related to the flu…
The study is published … in the journal PLOS Computational Biology.”

'Hackathons' Aim to Solve Health Care's Ills

Curated on April 8, 2014May 29, 2019 by Stefaan Verhulst

Amy Dockser Marcus in the Wall Street Journal: “Hackathons, the high-octane, all-night problem-solving sessions popularized by the software-coding community, are making their way into the more traditional world of health care. At Massachusetts Institute of Technology, a recent event called Hacking Medicine’s Grand Hackfest attracted more than 450 people to work for one weekend on possible solutions to problems involving diabetes, rare diseases, global health and information technology used at hospitals.
Health institutions such as New York-Presbyterian Hospital and Brigham and Women’s Hospital in Boston have held hackathons. MIT, meantime, has co-sponsored health hackathons in India, Spain and Uganda.
Hackathons of all kinds are increasingly popular. Intel Corp. recently bought a group that organizes them. Companies hoping to spark creative thinking sponsor them. And student-run hackathons have turned into intercollegiate competitions.
But in health care, where change typically comes much more slowly than in Silicon Valley, they represent a cultural shift. To solve a problem, scientists and doctors can spend years painstakingly running experiments, gathering data, applying for grants and publishing results. So the idea of an event where people give two-minute pitches describing a problem, then join a team of strangers to come up with a solution in the course of one weekend is radical.
“We are not trying to replace the medical culture with Facebook culture,” said Elliot Cohen, who wore a hoodie over a button-down dress shirt at the MIT event in March and helped start MIT Hacking Medicine while at business school. “But we want to try to blend them more.”
Mr. Cohen co-founded and is chief technology officer at PillPack, a pharmacy that sends customers personalized packages of their medications, a company that started at a hackathon.
At MIT’s health-hack, physicians, researchers, students and a smattering of people wearing Google Glass sprawled on the floor of MIT’s Media Lab and at tables with a view of the Boston skyline. At one table, a group of college students, laptops plastered with stickers, pulled juice boxes and snacks out of backpacks, trash piling up next to them as they feverishly wrote code.
Nupur Garg, an emergency-room physician and one of the eventual winners, finished her hospital shift at 2 a.m. Saturday in New York, drove to Boston and arrived at MIT in time to pitch the need for a way to capture images of patients’ ears and throats that can be shared with specialists to help make diagnoses. She and her team immediately started working on a prototype for the device, testing early versions on anyone who stopped by their table.
Dr. Garg and teammate Nancy Liang, who runs a company that makes Web apps for 3-D printers, caught a few hours of sleep in a dorm room Saturday night. They came up with the idea for their product’s name—MedSnap—later that night while watching students use cellphone cameras to send SnapChats to one another. “There was no time to conduct surveys on what was the best name,” said Ms. Liang. “Many ideas happen after midnight.”
Winning teams in each category won $1,000, as well as access to the hackathons sponsors for advice and pilot projects.
Yet even supporters say hackathons can’t solve medicine’s challenges overnight. Harlan Krumholz, a professor at Yale School of Medicine who ran a many-months trial that found telemonitoring didn’t reduce hospitalizations or deaths of cardiology patients, said he supports the problem-solving ethos of hackathons. But he added that “improvements require a long-term commitment, not just a weekend.”
Ned McCague, a data scientist at Blue Cross Blue Shield of Massachusetts, served as a mentor at the hackathon. He said he wasn’t representing his employer, but he used his professional experiences to push groups to think about the potential customer. “They have a good idea and are excited about it, but they haven’t thought about who is paying for it,” he said.
Zen Chu, a senior lecturer in health-care innovation and entrepreneur-in-residence at MIT, and one of the founders of Hacking Medicine, said more than a dozen startups conceived since the first hackathon, in 2011, are still in operation. Some received venture-capital funding.
The upsides of hackathons were made clear to Sharon Moalem, a physician who studies rare diseases. He had spent years developing a mobile app that can take pictures of faces to help diagnose rare genetic conditions, but was stumped on how to give the images a standard size scale to make comparisons. At the hackathon, Dr. Moalem said he was approached by an MIT student who suggested sticking a coin on the subjects’ forehead. Since quarters have a standard measurement, it “creates a scale,” said Dr. Moalem.
Dr. Moalem said he had never considered such a simple, elegant solution. The team went on to write code to help standardize facial measurements based on the dimensions of a coin and a credit card.
“Sometimes when you are too close to something, you stop seeing solutions, you only see problems,” Dr. Moalem said. “I needed to step outside my own silo.”