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Algorithms, clickworkers, and the befuddled fury around Facebook Trends

May 18, 2016
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The controversy about the human curators behind Facebook Trends has grown, since the allegations made last week by Gizmodo. Besides being a major headache for Facebook, it has helped prod a growing discussion about the power of Facebook to shape the information we see and what we take to be most important. But we continue to fail to find the right words to describe what algorithmic systems are, who generates them, and what they should do for users and for the public. We have to get this clear.

Here’s the case so far: Gizmodo says that Facebook hired human curators to decide which topics, identified by algorithms, would be listed as trending, how they should be named and summarized; one former curator alleged that his fellow curators often overlooked or suppressed conservative topics. This came too close on the heels of a report a few weeks back that Facebook employees had asked internally if the company had a responsibility to slow Donald Trump’s momentum. Angry critics have noted that Zuckerberg, Search VP Tom Stocky, and other FB execs are liberals. Facebook has vigorously disputed the allegation, saying that they have guidelines in place to insure consistency and neutrality, asserting that there’s no evidence that it happened, distributing their guidelines for how Trending topics are selected and summarized, after they were leaked, inviting conservative leaders in for a discussion, and pointing out their conservative bona fides. The Senate’s Commerce Committee, chaired by Republican Senator John Thune, issued a letter demanding answers from Facebook about it. Some wonder if the charges may have been overstated. Other Facebook news curators have spoken up, some to downplay the allegations and defend the process that was in place, others to highlight the sexist and toxic work environment they endured.

Commentators have used the controversy to express a range of broader concerns about Facebook’s power and prominence. Some argue it is unprecedented: “When a digital media network has one billion people connected to entertainment companies, news publications, brands, and each other, the right historical analogy isn’t television, or telephones, or radio, or newspapers. The right historical analogy doesn’t exist.” Others have made the case that Facebook is now as powerful as the media corporations, which have been regulated for their influence; that their power over news organizations and how they publish is growing; that they could potentially and knowingly engage in political manipulation; that they are not transparent about their choices; that they have become an information monopoly.

This is an important public reckoning about Facebook, and about social media platforms more generally, and it should continue. We clearly don’t yet have the language to capture the kind of power we think Facebook now holds. But it would be great if, along the way, we could finally mothball some foundational and deeply misleading assumptions about Facebook and social media platforms, assumptions that have clouded our understanding of their role and responsibility. Starting with the big one:

Algorithms are not neutral. Algorithms do not function apart from people.

 

We prefer the idea that algorithms run on their own, free of the messy bias, subjectivity, and political aims of people. It’s a seductive and persistent myth, one Facebook has enjoyed and propagated. But its simply false.

I’ve already commented on this, and many of those who study the social implications of information technology have made this point abundantly clear (including Pasquale, Crawford, Ananny, Tufekci, boyd, Seaver, McKelvey, Sandvig, Bucher, and nearly every essay on this list). But it persists: in statements made by Facebook, in the explanations offered by journalists, even in the words of Facebook’s critics.

If you still think algorithms are neutral because they’re not people, here’s a list, not even an exhaustive one, of the human decisions that have to be made to produce something like Facebook’s Trending Topics (which, keep in mind, pales in scope and importance to Facebook’s larger algorothmic endeavor, the “news feed” listing your friends’ activity). Some are made by the engineers designing the algorithm, others are made by curators who turn the output of the algorithm into something presentable. If your eyes start to glaze over, that’s the point; read any three points and then move on, they’re enough to dispel the myth. Ready?

(determining what activity might potentially be seen as a trend)
– what data should be counted in this initial calculation of what’s being talked about (all Facebook users, or subset? English language only? private posts too, or just public ones?)
– what time frame should be used in this calculation — both for the amount of activity happening “now” (one minute, one hour, one day?) and to get a baseline measure of what’s typical (a week ago? a different day at the same time, or a different time on the same day? one point of comparison or several?)
– should Facebook emphasize novelty? longevity? recurrence? (e.g., if it has trended before, should it be easier or harder for it to trend again?)
– how much of a drop in activity is sufficient for a trending topic to die out?
– which posts actually represent a single topic (e.g., when do two hashtags referring to the same topic?)
– what other signals should be taken into account? what do they mean? (should Facebook measure posts only, or take into account likes? how heavily should they be weighed?)
– should certain contributors enjoy some privileged position in the count? (corporate partners, advertisers, high-value users? pay-for-play?)

(from all possible trends, choosing which should be displayed)
– should some topics be dropped, like obscenity or hate speech?
– if so, who decides what counts as obscene or hateful enough to leave off?
– what topics should be left off because they’re too generic? (Facebook noted that it didn’t include “junk topics” that do not correlate to a real world event. What counts as junk, case by case?)

(designing how trends are displayed to the users)
– who should do this work? what expertise should they have? who hires them?
– how should a trend be presented? (word? title? summary?)
– what should clicking on a trend headline lead to? (some form of activity on Facebook? some collection of relevant posts? an article off the platform, and if so, which one?)
– should trends be presented in single list, or broken into categories? if so, can the same topic appear in more than one category?
– what are the boundaries of those categories (i.e. what is or isn’t “politics”?)
– should trends be grouped regionally or not? if so, what are the boundaries of each region?
– should trends lists be personalized, or not? If so, what criteria about the user are used to make that decision?

(what to do if the list is deemed to be broken or problematic in particular ways)
– who looks at this project to assess how its doing? how often, and with what power to change it?
– what counts as the list being broken, or off the mark, or failing to meet the needs of users or of Facebook?
– what is the list being judged against, to know when its off (as tested against other measures of Facebook activity? as compared to Twitter? to major news sites?)
– should they re-balance a Trends list that appears unbalanced, or leave it? (e.g. what if all the items in the list at this moment are all sports, or all celebrity scandal, or all sound “liberal”?)
– should they inject topics that aren’t trending, but seem timely and important?
– if so, according to what criteria? (news organizations? which ones? how many? US v. international? partisan vs not? online vs off?)
– should topics about Facebook itself be included?

These are all human choices. Sometimes they’re made in the design of the algorithm, sometimes around it. The result we see, a changing list of topics, is not the output of “an algorithm” by itself, but rather of an effort that combined human activity and computational analysis, together, to produce it.

So algorithms are in fact full of people and the decisions they make. When we let ourselves believe that they’re not, we let everyone — Zuckerberg, his software engineers, regulators, and the rest of us — off the hook for actually thinking out how they should work, leaving us all unprepared when they end up in the tall grass of public contention. “Any algorithm that has to make choices has criteria that are specified by its designers. And those criteria are expressions of human values. Engineers may think they are “neutral”, but long experience has shown us they are babes in the woods of politics, economics and ideology.” Calls for more public accountability, like this one from my colleague danah boyd, can only proceed once we completely jettison the idea that algorithms are neutral — and replace it with a different language that can assess the work that people and systems do together.

The problem is not algorithms, it’s that Facebook is trying to clickwork the news.

 

It is certainly in Facebook’s interest to obscure all the people involved, so users can keep believing that a computer program is fairly and faithfully hard at work. Dismantling this myth raises the kind of hard questions Facebook is fielding. But, once we jettison this myth, what’s left? It’s easy to despair that with so many human decisions involved, how could we ever get a fair and impartial measure of what matters? And forget the handful of people that designed the algorithm and the handful of people that select and summarize from it: Trends are themselves a measure of the activity of Facebook users. These trending topics aren’t produced by dozens of people but millions. Their judgment of what’s worth talking about, in each case and in the aggregate, may be so distressingly incomplete, biased, skewed, and vulnerable to manipulation, that it’s absurd to pretend it can tell us anything at all.

But political bias doesn’t come from the mere presence of people. It comes from how those people organized to do what they’re asked to do. Along with our assumption that algorithms are neutral is a matching and equally misleading assumption that people are always and irretrievably biased. But human endeavors are organized affairs, and can organized to work against bias. Journalism is full of people too, making all sorts of just as opaque, limited, and self-interested decisions. What we hope keeps journalism from slipping into bias and error is the well-established professional norms and thoughtful oversight.

The real problem here is not the liberal leanings of Facebook’s news curators. If conservative news topics were overlooked, it’s only a symptom of the underlying problem. Facebook wanted to take surges of activity that its algorithms could identify and turn them into news-like headlines. But it treated this as an information processing problem, not an editorial one. They’re “clickworking” the news.

Clickwork begins with the recognition that computers are good at some kinds of tasks, and humans others. The answer, it suggests, is to break the task at hand down into components and parcel them out to each accordingly. For Facebook’s trending topics, the algorithm is good at scanning an immense amount of data and identifying surges of activity, but not at giving those surges a name and a coherent description. That is handled by people — in industry parlance, this is the “human computation” part. These identified surges of activities are delivered to a team of curators, each one tasked with following a set of procedures to identify and summarize them. The work is segmented into simple and repetitive tasks, and governed by a set of procedures such that, even though different people are doing it, their output will look the same. In effect, the humans are given tasks that only humans can do, but they are not invited to do them in a human way: they are “programmed” by the modularized work flow and the detailed procedures so that they do the work like computers would. As Lilly Irani put it, clickwork “reorganizes digital workers to fit them both materially and symbolically within existing cultures of new media work.”

This is apparent in the guidelines that Facebook gives to their Trends curators. The documents, leaked to The Guardian then released by Facebook, did not reveal some bombshell about political manipulation, nor did they do much to demonstrate careful guidance on the part of Facebook around the issue of political bias. What’s most striking is that they are mind-numbingly banal: “Write the description up style, capitalizing the first letter of all major words…” “Do not copy another outlet’s headline…” “Avoid all spoilers for descriptions of scripted shows…” “After identifying the correct angle for a topic, click into the dropdown menu underneath the Unique Keyword fielding select the Unique Keyword that best fits the topic…” “Mark a topic as ‘National Story’ importance if it is among the 1-3 top stories of the day. We measure this by checking if it is leading at least 5 of the following 10 news websites…”  “Sports games: rewrite the topic name to include both teams…” This is not the news room, it’s the secretarial pool.

Moreover, these workers were kept separate from the rest of the full-time employees, worked under quotas for how many trends to identify and summarize that were increased as the project went on. As one curator noted, “The team prioritizes scale over editorial quality and forces contractors to work under stressful conditions of meeting aggressive numbers coupled with poor scheduling and miscommunication. If a curator is underperforming, they’ll receive an email from a supervisor comparing their numbers to another curator.” All were hourly contractors, were kept under non-disclose agreements and asked not to mention that they worked for Facebook. “’It was degrading as a human being,’ said another. ‘We weren’t treated as individuals. We were treated in this robot way.’” A new piece in The Guardian from one such news curator insists that it was also a toxic work environment, especially for women. These “data janitors” are rendered so invisible in the images of Silicon Valley and how tech works that, when we suddenly hear from one, we’re surprised.

Their work was organized to quickly produce capsule descriptions of bits of information that are styled the same — as if they were produced by an algorithm. (this lines up with other concerns about the use of algorithms and clickworkers to produce cheap journalism at scale, and the increasing influence of audience metrics about what’s popular on news judgment.)  It was not, however, organized to thoughtfully assemble a vital information resource that some users treat as the headlines of the day. It was not organized to help these news curators develop experience together on how to do this work well, or handle contentious topics, or reflect on the possible political biases in their choices. It was not likely to foster a sense of community and shared ambitions with Facebook, which might lead frustrated and over-worked news curators to indulge in their own political preferences. And I suspect it was not likely to funnel any insights they had about trending topics back to the designers of the algorithms they depended on.

Trends are not the same as news, but Facebook kinda wants them to be.

 

Part of why charges of bias are so compelling is that we have a longstanding concern about the problem of bias in news. For more than a century we’ve fretted about the individual bias of reporters, the slant of news organizations, and the limits of objectivity [http://jou.sagepub.com/content/2/2/149.abstract]. But is a list of trending topics a form of news? Are the concerns we have about balance and bias in the news relevant for trends?

“Trends” is a great word, the best word to have emerged amidst the social media zeitgeist. In a cultural moment obsessed with quantification, defended as being the product of an algorithm, “trends” is a powerfully and deliberately vague term that does not reveal what it measures. Commentators poke at Facebook for clearer explanations of how they choose trends, but “trends” could mean such a wide array of things, from the most activity to the most rapidly rising to a completely subjective judgment about what’s popular.

But however they are measured and curated, Facebook’s Trends are, at their core, measures of activity on the site. So, at least in principle, they are not news, they are expressions of interest. Facebook users are talking about some things, a lot, for some reason. This has little to do with “news” which implies an attention to events in the world and some judgment of importance. Of course, many things Facebook users talk about, though not all, are public events. And it seems reasonable to assume that talking about a topic represents some judgment of its importance, however minimal. Facebook takes these identifiable surges of activity as proxies for importance. Facebook users “surface” the news… approximately. The extra step and “injecting” stories drawn from the news that were for whatever reason not surging among Facebook users goes a step further, to turn their proxy of the news into a simulation of it. Clearly this was an attempt to best Twitter, may also have played into their effort to persuade news organizations to partner with them and take advantage of their platform as a means of distribution. But it also encouraged us to hold Trends accountable for news-like concerns, like liberal bias.

We could think about Trends differently, not as approximating the news but as taking the public’s pulse. If Trends were designed to strictly represent “what are Facebook users talking about a lot,” presumably there is some scientific value, or at least cultural interest, in knowing what (that many) people are actually talking about. If that were its understood value, we might still worry about the intervention of human curators and their political preferences, but not because their choices would shape users’  political knowledge or attitudes, but because e’d want this scientific glimpse to be unvarnished by misrepresentation.

But that is not how Facebook has invited us to think about its Trending topics, and it couldn’t do so if it wanted: its interest in Trending topics is neither as a form of news production nor as a pulse of the public, but as a means to keep users on the site and involved. The proof of this, and the detail that so often gets forgotten in these debates, is that the Trending Topics are personalized. Here’s Facebook’s own explanation: “Trending shows you a list of topics and hashtags that have recently spiked in popularity on Facebook. This list is personalized based on a number of factors, including Pages you’ve liked, your location and what’s trending across Facebook.” Knowing what has “spiked in popularity” is not the same as news; a list “personalized based on… Pages you’ve liked” is no longer a site-wide measure of popular activity; an injected topic is no longer just what an algorithm identified.

As I’ve said elsewhere, “trends” are not a barometer of popular activity but a hieroglyph, making provocative but oblique and fleeting claims about “us” but invariably open to interpretation. Today’s frustration with Facebook, focused for the moment on the role their news curators might have played in producing these Trends, is really a discomfort with the power Facebook seems to exert — a kind of power that’s hard to put a finger on, a kind of power that our traditional vocabulary fails to capture. But across the controversies that seem to flare again and again, a connecting thread is Facebook’s insistence on colonizing more and more components of social life (friendship, community, sharing, memory, journalism), and turning the production of shared meaning so vital to sociality into the processing of information so essential to their own aims.

Facebook Trending: It’s made of people!! (but we should have already known that)

May 9, 2016

Gizmodo has released two important articles (1, 2) about the people who were hired to manage Facebook’s “Trending” list. The first reveals not only how Trending topics are selected and packaged on Facebook, but also the peculiar working conditions this team experienced, the lack of guidance or oversight they were provided, and the directives they received to avoid news that addressed Facebook itself. The second makes a more pointed allegation: that along the way, conservative topics were routinely ignored, meaning the trending algorithm had identified user activity around a particular topic, but the team of curators chose not to publish it as a trend.

This is either a boffo revelation, or an unsurprising look at how the sausage always gets made, depending on your perspective. The promise of “trends” is a powerful one. Even as the public gets more and more familiar with the way social media platforms work with data, and even with more pointed scrutiny of trends in particular, it is still easy to think that “trends” means an algorithm is systematically and impartially uncovering genuine patterns of user activity. So, to discover that a handful of j-school graduates were tasked with surveying all the topics the algorithm identified, choosing just a handful of them, and dressing them up with names and summaries, feels like a unwelcome intrusion of human judgment into what we wish were analytic certainty. Who are these people? What incredible power they have to dictate what is and is not displayed, what is and is not presented as important! Wasn’t this  supposed to be just a measure of what users were doing, what the people important! Downplaying conservative news is the most damning charge possible, since it has long been a commonplace accusation leveled at journalists. But the revelation is that there’s people in the algorithm at all.

But the plain fact of information algorithms like the ones used to identify “trends” is that they do not work alone, they cannot work alone — in so many ways that we must simply discard the fantasy that they do, or ever will. In fact, algorithms do surprisingly little, they just do it really quickly and with a whole lot of data. Here’s some of what they can’t do:

Trending algorithms identify patterns in data, but they can’t make sense of it. The raw data is Facebook posts, likes, and hashtags. Looking at this data, there will certainly be surges of activity that can be identified and quantified: words that show up more than other words, posts that get more likes than other posts. But there is so much more to figure out
(1) What is a topic? To decide how popular a topic is, Facebook must decide which posts are about that topic. When do two posts or two hashtags represent the same story, such that they should be counted together? An algorithm can only do so much to say whether a post about Beyonce and a post about Bey and a post about Lemonade and a post about QueenB and the hashtag BeyHive are all the same topic. And that’s an easy one, a superstar with a distinctive name, days after a major public event. Imagine trying to determine algorithmically if people are talking about European tax reform, enough to warrant calling it a trend.
(2) Topics are also composed of smaller topics, endlessly down to infinity. Is the Republican nomination process a trending topic, or the Indiana primary, or Trump’s win in Indiana, or Paul Ryan’s response to Trump’s win in Indiana? According to one algorithmic threshold these would be grouped together, by another would be separate. The problem is not that an algorithm can’t tell. It’s that it can tell both interpretations, all interpretations equally well. So, an algorithm could be programmed to decide,to impose a particular threshold for the granularity of topics. But would that choice make sense to readers, would it map onto their own sense of what’s important, and would it work for the next topic, and the next?
(3) How should a topic be named and described, in a way that Facebook users would appreciate or even understand? Computational attempts to summarize are notoriously clunky, and often produce the kind of phrasing and grammar that scream “a computer wrote this.”
What trending algorithms can identify isn’t always what a platform wants to identify. Facebook, unlike Twitter, chose to display trends that identify topics, rather than single hashtags. This was already a move weighted towards identifying “news” rather than topics. It already strikes an uneasy balance between the kind of information they have — billions and posts and likes surging through their system — and the kind they’d like to display — a list of the most relevant topics. And it already sets up an irreconcilable tension: what should they do when user activity is not a good measure of public importance? It is not surprising the, that they’d try to focus on articles being circulated and commented on, and from the most reputable sources, as a way to lean on their curation and authority to pre-identify topics. Which opens up, as Gizmodo identifies, the tendency to discount some sources as non-reputable, which can have unintentionally partisan implications.
“Trending” is also being asked to do a lot of things for Facebook: capture the most relevant issues being discussed on Facebook, and conveniently map onto the most relevant topics in the worlds of news and entertainment, and keep users on the site longer, and keep up with Twitter, and keep advertisers happy. In many ways, a trending algorithm can be an enormous liability, if allowed to be: it could generate a list of dreadful or depressing topics; it could become a playground for trolls who want to fill it with nonsense and profanity; it could reveal how little people use Facebook to talk about matters of public importance; it could reveal how depressingly little people care about matters of public importance; and it could help amplify a story critical of Facebook itself. It would take a whole lot of bravado to set that loose on a system like Facebook, and let it show what it shows unmanaged. Clearly, Facebook has a lot more at stake in producing a trending list that, while it should look like an unvarnished report of what users are discussing, must also massage it into something that represents Facebook well at the same time.

So: people are in the algorithm because how could they not be? People produce the Facebook activity being measured, people design the algorithms and set their evaluative criteria, people decide what counts as a trend, people name and summarize them, and people look to game the algorithm with their next posts.

The thing is, these human judgments are all part of traditional news gathering as well. Choosing what to report in the news, how to describe it and feature it, and how to honor both the interests of the audience and the sense of importance, has always been a messy, subjective process, full of gaps in which error, bias, self-interest, and myopia can enter. The real concern here is not that there are similar gaps in Facebook’s process as well, or that Facebook hasn’t yet invented an algorithm that can close those gaps. The real worry is that Facebook is being so unbelievably cavalier about it.

Traditional news organizations face analogous problems and must make analogous choices, and can make analogous missteps. And they do. But two countervailing forces work against this, keep them more honest than not, more on target than not: a palpable and institutionalized commitment to news itself, and competition. I have no desire to glorify the current news landscape, which in many ways produces news that is disheartening less than what journalism should be. But there is at least a public, shared, institutionally rehearsed, and historical sense of purpose and mission, or at least there’s one available. Journalism schools teach their students about not just how to determine and deliver the news, but why. They offer up professional guidelines and heroic narratives that position the journalist as a provider of political truths and public insight. They provide journalists with frames that help them identify the way news can suffer when it overlaps with public relations, spin, infotainment, and advertising. There are buffers in place to protect journalists from the pressures that can come from the upper management, advertisers, or newsmakers themselves, because of a belief that independence is an important foundation for newsgathering. Journalists recognize that their choices have consequences, and they discuss those choices. And there are stakeholders for regularly checking these efforts for possible bias and self-interest: public editors and ombudspeople, newswatch organizations and public critics,  all trying to keep the process honest. Most of all, there are competitors who would gleefully point out a news organization’s mistakes and failures, which gives editors and managers real incentive to work against the temptations to produce news that is self-serving, politically slanted, or commercially craven.

Facebook seemed to have thought of absolutely none of these. Based on the revelations in the two Gizmodo articles, it’s clear that they hired a shoestring team, lashed them to the algorithm, offered little guidance for what it meant to make curatorial choices, provided no ongoing oversight as the project progressed, imposed self-interested guidelines to protect the company, and kept the entire process inscrutable to the public, cloaked in the promise of an algorithm doing its algorithm thing.

The other worry here is that Facebook is engaged in a labor practice increasingly common among Silicon Valley: hiring information workers through third parties, under precarious conditions and without access to the institutional support or culture their full-time employees enjoy, and imposing time and output demands on them that can only fail a task that warrants more time, care, expertise, and support. This is the troubling truth about information workers in Silicon Valley and around the world, who find themselves “automated” by the gig economy — not just clickworkers on Mechanical Turk and drivers on Uber, but even “inside” the biggest and most established companies on the plant. It also is a dangerous tendency for the kind and scale of information projects that tech companies are willing to take on, without having the infrastructure and personnel to adequately support them. It is not uncommon now for a company to debut a new feature or service, only weeks in development and supported only by its design team, with the assumption that it can quickly hire and train a team of independent, hourly workers. Not only does this put a huge onus on those workers, but it means that, if the service finds users and begins to scales up quickly, little preparation was in place, and the overworked team must quickly make some ad hoc decisions about what are often tricky cases with real, public ramifications.

Trending algorithms are undeniably becoming part of the cultural landscape, and revelations like Gizmodo’s are helpful steps in helping us shed the easy notions of what they are and how they work, notions the platforms have fostered. Social media platforms must come to fully realize that they are newsmakers and gatekeepers, whether they intend to be or not, whether they want to be or not. And while algorithms can chew on a lot of data, it is still a substantial, significant, and human process to turn that data into claims about importance that get fed back to millions of users. This is not a realization that they will ever reach on their own — which suggests to me that they need the two countervailing forces that journalism has: a structural commitment to the public, imposed if not inherent, and competition to force them to take such obligations seriously.

Addendum: Techcrunch is reporting that Facebook has responded to Gizmodo’s allegations, suggesting that it has “rigorous guidelines in place for the review team to ensure consistency and neutrality.” This makes sense. But consistency and neutrality are fine as concepts, but they’re vague and insufficient in practice. There could have been Trending curators at Facebook who deliberately tanked conservative topics and knew that doing so violated policy. But (and this has long been known in the sociology of news) the greater challenge in producing the news, whether generating it or just curating it, is how to deal with the judgments that happen while being consistent and neutral. Making the news always requires judgments, and judgements always incorporate premises for assessing the relevance, legitimacy, and coherence of a topic. Recognizing bias in our own choices or across an institution is extremely difficult, but knowing whether you have produced a biased representation of reality is nearly impossible, as there’s nothing to compare it to — even setting aside that Facebook is actually trying to do something even harder, produce a representation of the collective representations of reality of their users, and ensure that somehow it also represents reality, as other reality-representers (be they CNN or Twitter users) have represented it. Were social media platforms willing to acknowledge that they constitute public life rather than hosting or reflecting it, they might look to those who produce news, educate journalists, and study news as a sociological phenomenon, for help thinking through these challenges.

Addendum 2 (May 9): The Senate Committee on Commerce, Science, and Transportation has just filed an inquiry with Facebook, raising concerns about their Trending Topics based on the allegations in the Gizmodo report. The letter of inquiry is available here, and has been reported by Gizmodo and elsewhere. In the letter they ask Mark Zuckerberg and Facebook to respond to a series of questions about how Trending Topics works, what kind of guidelines and oversight they provided, and whether specific topics were sidelined or injected. Gizmodo and other sites are highlighting the fact that this Committee is run by a conservative and has a majority of members who are conservative. But the questions posed are thoughtful ones. What they make so clear is that we simply do not have a vocabulary with which to hold these services accountable. For instance, they ask “Have Facebook news curators in fact manipulated the content of the Trending Topics section, either by targeting news stories related to conservative views for exclusion or by injecting non-trending content?” Look at the verbs. “Manipulated” is tricky, as it’s not exactly clear what the unmanipulated Trending Topics even are. “Targeting” sounds like they excluded stories, when what Gizmodo reports is that some stories were not selected as trending, or not recognized as stories. If trending algorithms can only highlight possible topics surging in popularity, but Facebook and its news curators constitute that data into a list of topics, then language that takes trending to be a natural phenomenon, that Facebook either accurately reveals or manipulates, can’t quite grip how this works and why it is so important. It is worth noting, though, that the inquiry pushes on how (whether) Facebook is keeping records of what is selected: “Does Facebook maintain a record ,of curators’ decisions to inject a story into the Trending Topics section or target a story for removal? If such a record. is not maintained, can such decisions be reconstructed or determined based on an analysis of the Trending Topics product? a. If so, how many stories have curators excluded that represented conservative viewpoints or topics of interest to conservatives? How many stories did curators inject that were not, in fact, trending? b. Please provide a list of all news stories removed from or injected into the Trending Topics section since January 2014.” This approach I think does emphasize to Facebook that these choices are significant, enough so that they should be treated as part of the public record and open to scrutiny by policymakers or the courts. This is a way of demanding Facebook take role in this regard more seriously.

Astro Noise: A survival guide for living under total surveillance

May 9, 2016

Documentary film maker Laura Poitra’s exhibit in the Whitney Museum presented an immersive installation covering issues of mass surveillance, the war on terror, Guantánamo Bay, occupation, the US drone program and torture. Some of these issues have been investigated in her films, including Citizenfour, which won the 2015 Academy Award for Best Documentary, and in her reporting, which was awarded a 2014 Pulitzer Prize.

With that came Astastronoisero Noise: A Survival Guide For Living Under Total Surveillance, where Poitras invited authors ranging from artists and novelists to technologists and academics to respond to the modern-day state of mass surveillance. Among them are author Dave Eggers, artist Ai Weiwei, the former Guantanamo Bay detainee Lakhdar Boumediene,  MSR SMC researcher Kate Crawford, and Edward Snowden. Some contributors worked directly with Poitras and the archive of documents leaked by Snowden; others contributed fictional reinterpretations of spycraft. The result is a “how-to” guide for living in a society that collects extraordinary amounts of information on individuals. A few excerpts by the different collaborators:

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Laura Poitras –> Her chapter is called “Berlin Journal,” which she wrote between 2012 and 2013, when she had relocated to Europe so she could work easily without fear of having her material taken when she went into the US.

Feb 11. 2013

I read the news for fear of an arrest. It still could be  a shakedown targeting Julian or Jake. Watching what i’ll do with the material. It really is a drama to understand the possible motivations/goals. I take it at face value, but why? He could have approached the NYT or the Washington Post for maximun exposure. Why reach out to a filmmaker? Because I’ve been targeted? Because he has already gone down other paths? Because he doesn’t have what he claims?  (p. 86)

Kate Crawford—> Asking the Oracle

Kate compares the ancient Greek Delphic Oracle, which had restrictions for acquiring knowledge, to the unrestricted vastness of information provided by total surveillance.

So the Oracle, as a technology, set up particular restrictions and limitations. The information flow was restricted by the number of people who could visit the Oracle, by how many questions they could ask, and by the cryptic nature of the responses they received. In this sense there is  strange similarity with the Snowden archive. The person seated before the search box must decide what to ask next and try to exercise restraint so as not to be drawn into thousands of documents and stories and systems. But in another sense, when analysts consult the database inside the fortresses of the NSA and the GCHQ, there seems to be little respect for limits beyond the stictures of policy. Everything that can be captured will be. The archive is an epic testament to information acquisition, overreach, and confidence. It’s as though the guiding principles of Delphi were reversed. Know Everyone. Everything in Excess. Just keep pledgdin that all the necessary protections ar ein place. (p 143)

Edward Snowden –> Astro Noise

With the right antenna, we can hear the universe’s radio noises. The stars themselves (or so it’s been theorized) can provide us an unpredictable source of information that will never be heard again in the same way. As the world turns, our antenna sweeps the vastness of the universe at a given point in time. The signals that we receive constitute an ever-changing key forged from the sky itself. Such a key could only be imitated by an agent listening from that exact same place, in that same direction, at the same time, to those exact same stars. (p. 121)

Cory Doctorow –> The Adventure of the Extraordinary Rendition

In his chapter, Cory Doctorow explores a story of Sherlock Holmes in the times of the NSA.

It’s life in prison if I go public, Mr. Holmes. These kids, their parents are in the long-term XKeyscore retention, all their communications, and they’re frantic. I read their emails to their relatives and each other, and I can only think of how I’d feel if my son had gone missing without a trace. These parents, they’re thinking that their kids have been snatched by pedos and are getting the Daily Mail front-page treatment. The truth, if they knew it, might terrify them even more. Far as I can work out, the NSA sent them to a cIA black site, the kind of place you wouldn’t wish on your worst enemy. The kind of place you build for revenge, not for intelligence.

 

 

We’re all selfish superficial and too fat? Tedx talk by Kat Tiidenberg

May 2, 2016
by

This is a video and the transcript of my Ted talk at Ted x TTU in April 2016. It’s about body image, consumer economy and selfies.

*

I have some sayings here; let’s do a show of hands if you’ve heard these: “don’t judge a book by its cover” or “beauty is only skin deep.” The point seems to be that we shouldn’t be judged based on how we look, is that true? That we are more than our appearances, more than our bodies, do you agree?

Let’s do anther show of hands. During the past week, how many of you looked in the mirror and wished for something to be different? To be a little taller, or a little thinner – just, you know, the belly; or the thighs. Maybe you looked and wished to be more muscular or younger? To have smoother skin?

It seems, we are at an impasse. We don’t think we should be judged by our looks, but we quite harshly judge ourselves based on them. We think beauty is only skin deep, but we spend a lot of time, effort and money on trying to make ourselves look better, thus constantly engaging in something that is supposedly trivial. And it’s not just me and you either – according to the American Society of Plastic Surgeons, butt implants were the fastest growing type of cosmetic surgery in 2015. On average, there was a butt implant procedure every 30 minutes of every day. When I search for “love your body” in just Amazon Books, I find 14 399 results. 14 000 titles just to help us get comfortable in our own skin. Clearly we need a lot of help.

So the relationship we have with our bodies seems best described as tense. Why is that?Some say it is because we’re self-centered, narcissistic and superficial. I don’t think so. I also have some ideas on how to soothe this tension. To explain those ideas, I will use an example of something many people think is self-centered, narcissistic and superficial – selfies.

Read more…

We are hiring a Research Assistant

April 27, 2016

The Social Media Collective is looking for a Research Assistant to work with us at Microsoft Research New England in Cambridge, Massachusetts.

Starting in July 2016 the MSR Social Media Collective will consist of Nancy Baym, Tarleton Gillespie, Mary L. Gray, Dan Greene, and Dylan Mulvin in Cambridge, Kate Crawford and danah boyd in New York City, as well as faculty visitors and Ph.D. interns affiliated with the MSR New England. The RA will work directly with Nancy Baym, Kate Crawford, Tarleton Gillespie, and Mary L. Gray.

An appropriate candidate will be a self-starter who is passionate and knowledgeable about the social and cultural implications of technology. Strong skills in writing, organization and academic research are essential, as are time-management and multi-tasking. Minimal qualifications are a BA or equivalent degree in a humanities or social science discipline and some qualitative research training. A Masters degree is preferred.

Job responsibilities will include:

– Sourcing and curating relevant literature and research materials
– Developing literature reviews and/or annotated bibliographies
– Coding ethnographic and interview data
– Copyediting manuscripts
– Working with academic journals on themed sections
– Assisting with research project data management and event organization

The RA will also have opportunities to collaborate on ongoing projects. While publication is not a guarantee, the RA will be encouraged to co-author papers while at MSR. The RAship will require 40 hours per week on site in Cambridge, MA, and remote coordination with New York. It is a 12 month contractor position, with the opportunity to extend the contract an additional 6 months. The position pays hourly with flexible daytime hours. The start date will ideally be July 25, although flexibility is possible for the right candidate.

This position is perfect for emerging scholars planning to apply to PhD programs in Communication, Media Studies, Sociology, Anthropology, Information Studies, History, Philosophy, STS and Critical Data Studies, and related fields who want to develop their research skills and area expertise before entering a graduate program. Current New England-based MA/PhD students are welcome to apply provided they can commit to 40 hours of on-site work per week.

To apply, please send an email to Nancy Baym (baym@microsoft.com) with the subject “RA Application” and include the following attachments:

– One-page (single-spaced) personal statement, including a description of research experience and training, interests, and professional goals
– CV or resume
– Writing sample (preferably a literature review or a scholarly-styled article)
– Links to online presence (e.g., blog, homepage, Twitter, journalistic endeavors, etc.)
– The names and email addresses of two recommenders

We will begin reviewing applications on May 15 and will continue to do so until we find an appropriate candidate. We will post to the blog when the position is filled.

We regret that because this is a time-limited contract position, we can only consider candidates who are already legally authorized to work in the United States.

Please feel free to ask questions about the position in the blog comments!

 

“Metaphors of Data” reading list

April 26, 2016

With generous contributions from the Social Media Collective extended family, I have put together a list that brings together academic and popular writing on metaphors of data, along with pieces that approach questions of data and commercial/political power. The goal in assembling this list was to catalog resources that are helpful in unpacking and critiquing different metaphors, ranging from the hype around big data as the new oil to less common (and perhaps more curious) formulations, such as data as sweat or toxic waste.

 

Metaphors of Data: a Reading List

 

These resources were originally compiled to support a workshop on data and power (organized at the Mobile Life Centre in Stockholm, Sweden). Sara Watson’s insightful DIS piece on the Industrial Metaphors of Big Data and Maciej Cegłowski’s brilliant talk Haunted By Data turned out to be particularly helpful for provoking conversation among scholars and practitioners. The hope is that the list could be useful also for others in having critical conversations about data.

The list is best seen as an unfinished, non-exhaustive document. We welcome comments and, in particular, recommendations of further work to include. Please use the comment space at the bottom of the page to offer suggestions, and we will try to update the list in light of them.

CFP: Studying Social Media and Digital Infrastructures: a workshop-within-a-conference

April 19, 2016
multimediacarosel

 

part of the 50th Hawaii International Conference on System Sciences (HICSS-50)

paper submission deadline: June 15, 2016, 11:59pm HST.

  

For fifty years, the Hawaii International Conference on System Sciences (HICSS) has been a home for researchers in the information, computer, and system sciences (http://www.hicss.org/). The 50th anniversary event will be held January 4-7, 2017, at the Hilton Waikoloa Village. With an eye to the exponential growth of digitalization and information networks in all aspects of human activity, HICSS has continued to expand its track on Digital and Social Media (http://www.hicss.org/#!track3/c1xcj).

This year, among the Digital and Social Media track’s numerous offerings, we offer two minitracks meant to work in concert. Designed to sequence together into a single day-long workshop-within-a-conference, they will host the best emerging scholarship from sociology, anthropology, communication, information studies, and science & technology studies that addresses the most pressing concerns around digital and social media. In addition, we have developed a pre-conference workshop on digital research methods that will inform and complement the work presented in these minitracks.

 

Minitrack 1: Critical and Ethical Studies of Digital and Social Media

http://www.hicss.org/#!critical-ethical-studies-of-dsm/c24u6

Organizers: Tarleton Gillespie, Mary Gray, and Robert Mason

The minitrack will critically interrogate the role of DSM in supporting existing power structures or realigning power for underrepresented or social marginalized groups, and raise awareness or illustrate the ethical issues associated with doing research on DSM. Conceptual papers would address foundational theories of critical studies of media or ethical conduct in periods of rapid sociotechnical change—e.g., new ways of thinking about information exchange in communities and societies. Empirical papers would draw on studies of social media data that illustrate the critical or ethical dimensions of the use of such data. We welcome papers considering topics such as (but not limited to):

*   the power and responsibility of digital platforms

*   bias and discrimination in the collection and use of social data

*   political economies and labor conditions of paid and unpaid information work

*   values embedded in search engines and social media algorithms

*   changes in societal institutions driven by social media and data-intensive techniques

*   alternative forms of digital and social media

*   the ethical dynamics of studying human subjects through their online data

*   challenges in studying the flow of information and misinformation

*   barriers to and professional obligations around accessing and studying proprietary DSM data

 

Minitrack 2: Values, Power, and Politics in Digital Infrastructures

http://www.hicss.org/#!values-power-and-politics-in-digital-i/c19uj

Organizers: Katie Shilton, Jaime Snyder, and Matthew Bietz

This minitrack will explore the themes of values, power, and politics in relation to the infrastructures that support digital data, documents, and interactions. By considering how infrastructures – the underlying material properties, policy decisions, and mechanisms of interoperability that support digital platforms – are designed, maintained, and dismantled, the work presented in this mini-track will contribute to debates about sociotechnical aspects of digital and social media, with a focus on data, knowledge production, and information access. This session will focus on research that employs techniques such as infrastructural inversion, trace ethnography or design research (among other methods) to explore factors that influence the development of infrastructures and their use in practice. We welcome papers considering topics such as (but not limited to):

*  politics and ethics in digital platforms and infrastructures

*  values of stakeholders in digital infrastructures

*  materiality of values, power, or politics in digital infrastructures

*  tensions between commercial infrastructures and the needs of communities of practice

*  maintenance, repair, deletion, decay of digital and social media infrastructures

*  resistance, adoption and adaptation of digital infrastructures

*  alternative perspectives on what comprises infrastructures

 

Pre-conference workshop: Digital Methods “Best Practices”

http://shawnw.io/workshops/HICSS-digitalmethods

Organizers: Shawn Walker, Mary Gray, and Robert Mason

While the study of digital and social media and its impact on society has exploded, discussion of the best methods for doing so remains thin. Academic researchers and practitioners have deployed traditional techniques, from ethnography to social network analysis; but digital and social media challenge and even defy these techniques in a number of ways that must be examined. At the same time, digital and social media may benefit from more organic and unorthodox methods that get at aspects that cannot be examined otherwise. This intensive half day workshop will focus on approaches and best practices for studying digital and social media. We aim to go beyond the application of existing methods into online environments and collect innovative methods that break new ground while producing rigorous insights. This workshop will draw on invited and other participants’ research, teaching, classroom, and business experiences to think through “mixed methods” for qualitative and quantitative studies of digital and social media systems.

Through a series of roundtables and guided discussions, the workshop will focus on best practices for studying digital and social media. As part of these discussions, we also will highlight technical and ethical challenges that arise from our studying cross-platform, digital and social media phenomenon. The output of this workshop will be an open, “co-authored” syllabus for a seminar offering what we might call a mixed-method, “from causal to complicated” approach to digital and social media research, applicable to both researchers and practitioners alike.

 

How to apply

April 1, 2016: Paper submission opens.

June 15, 2016: Paper submission ends, 11:59pm HST.

Submission to one of the the mini-tracks requires a complete paper. Instructions for submission requirements are available here: http://www.hicss.org/#!author-instructions/c1dsb

Though the two minitracks are designed to work together, for submitting a paper you must choose one to apply to. Feel free to contact the mini-track organizers if you have questions about which is a better fit for your work. For the pre-conference workshop, application instructions, updates, materials, and a group syllabus will be posted on the workshop website.

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