multicast

(or, I’m Moving My Blogging to Other Platforms.)

After a great run of six full years, I’ve decided to retire this blog. It worked well, but increasingly I find that most of the readership from my writing comes from my blogging at The Social Media Collective and occasionally at other venues like The Huffington Post and Wired.

Thanks so much for reading this. I’ll still be blogging and I hope that you’ll keep reading after I move things over there.

In the unlikely event that I launch any new standalone blogs I’ll be sure to alert you via my homepage.

(or, Caillou Sucks)

What should people who are interested in accountability and algorithms be thinking about? Here is one answer: My eleven-minute remarks are now online from a recent event at NYU. I’ve edited them to intersperse my slides.

This talk was partly motivated by the ethics work being done in the machine learning community. That is very exciting and interesting work and I love, love, love it. My remarks are an attempt to think through the other things we might also need to do. Let me know how to replace the “??” in my slides with something more meaningful!

Preview: My remarks contain a minor attempt at a Michael Jackson joke.

Here is the video: https://www.youtube.com/embed/rJfDKx2fjdE

A number of fantastic Social Media Collective people were at this conference — you can hear Kate Crawford in the opening remarks.  For more videos from the conference, see:

Algorithms and Accountabilityhttp://www.law.nyu.edu/centers/ili/algorithmsconference

Thanks to Joris van Hoboken, Helen Nissenbaum and Elana Zeide for organizing such a fab event.

If you bought this 11-minute presentation you might also buy: Auditing Algorithms, a forthcoming workshop at Oxford.

http://auditingalgorithms.wordpress.com

 

(This post was cross-posted to The Social Media Collective.)

 

 

Today in Science, members of the Facebook data science team released a provocative study about adult Facebook users in the US “who volunteer their ideological affiliation in their profile.” The study “quantified the extent to which individuals encounter comparatively more or less diverse” hard news “while interacting via Facebook’s algorithmically ranked News Feed.”*

• The research found that the user’s click rate on hard news is affected by the positioning of the content on the page by the filtering algorithm. The same link placed at the top of the feed is about 10-15% more likely to get a click than a link at position #40 (figure S5).
• The Facebook news feed curation algorithm, “based on many factors,” removes hard news from diverse sources that you are less likely to agree with but it does not remove the hard news that you are likely to agree with (S7). They call news from a source you are less likely to agree with “cross-cutting.”*
• The study then found that the algorithm filters out 1 in 20 cross-cutting hard news stories that a self-identified conservative sees (or 5%) and 1 in 13 cross-cutting hard news stories that a self-identified liberal sees (8%).
• Finally, the research then showed that “individuals’ choices about what to consume” further limits their “exposure to cross-cutting content.” Conservatives will click on only 17% a little less than 30% of cross-cutting hard news, while liberals will click 7% a little more than 20% (figure 3).

My interpretation in three sentences:

1. We would expect that people who are given the choice of what news they want to read will select sources they tend to agree with–more choice leads to more selectivity and polarization in news sources.
2. Increasing political polarization is normatively a bad thing.
3. Selectivity and polarization are happening on Facebook, and the news feed curation algorithm acts to modestly accelerate selectivity and polarization.

I think this should not be hugely surprising. For example, what else would a good filter algorithm be doing other than filtering for what it thinks you will like?

But what’s really provocative about this research is the unusual framing. This may go down in history as the “it’s not our fault” study.

 

Facebook: It’s not our fault.

I carefully wrote the above based on my interpretation of the results. Now that I’ve got that off my chest, let me tell you about how the Facebook data science team interprets these results. To start, my assumption was that news polarization is bad.  But the end of the Facebook study says:

“we do not pass judgment on the normative value of cross-cutting exposure”

This is strange, because there is a wide consensus that exposure to diverse news sources is foundational to democracy. Scholarly research about social media has–almost universally–expressed concern about the dangers of increasing selectivity and polarization. But it may be that you do not want to say that polarization is bad when you have just found that your own product increases it. (Modestly.)

And the sources cited just after this quote sure do say that exposure to diverse news sources is important. But the Facebook authors write:

“though normative scholars often argue that exposure to a diverse ‘marketplace of ideas’ is key to a healthy democracy (25), a number of studies find that exposure to cross-cutting viewpoints is associated with lower levels of political participation (22, 26, 27).”

So the authors present reduced exposure to diverse news as a “could be good, could be bad” but that’s just not fair. It’s just “bad.” There is no gang of political scientists arguing against exposure to diverse news sources.**

The Facebook study says it is important because:

“our work suggests that individuals are exposed to more cross-cutting discourse in social media they would be under the digital reality envisioned by some“

Why so defensive? If you look at what is cited here, this quote is saying that this study showed that Facebook is better than a speculative dystopian future.*** Yet the people referred to by this word “some” didn’t provide any sort of point estimates that were meant to allow specific comparisons. On the subject of comparisons, the study goes on to say that:

“we conclusively establish that…individual choices more than algorithms limit exposure to attitude-challenging content.”

“compared to algorithmic ranking, individuals’ choices about what to consume had a stronger effect”

Alarm bells are ringing for me. The tobacco industry might once have funded a study that says that smoking is less dangerous than coal mining, but here we have a study about coal miners smoking. Probably while they are in the coal mine. What I mean to say is that there is no scenario in which “user choices” vs. “the algorithm” can be traded off, because they happen together (Fig. 3 [top]). Users select from what the algorithm already filtered for them. It is a sequence.**** I think the proper statement about these two things is that they’re both bad — they both increase polarization and selectivity. As I said above, the algorithm appears to modestly increase the selectivity of users.

The only reason I can think of that the study is framed this way is as a kind of alibi. Facebook is saying: It’s not our fault! You do it too!

 

Are we the 4%?

In my summary at the top of this post, I wrote that the study was about people “who volunteer their ideological affiliation in their profile.” But the study also describes itself by saying:

“we utilize a large, comprehensive dataset from Facebook.”

“we examined how 10.1 million U.S. Facebook users interact”

These statements may be factually correct but I found them to be misleading. At first, I read this quickly and I took this to mean that out of the at least 200 million Americans who have used Facebook, the researchers selected a “large” sample that was representative of Facebook users, although this would not be representative of the US population. The “limitations” section discusses the demographics of “Facebook’s users,” as would be the normal thing to do if they were sampled. There is no information about the selection procedure in the article itself.

Instead, after reading down in the appendices, I realized that “comprehensive” refers to the survey research concept: “complete,” meaning that this was a non-probability, non-representative sample that included everyone on the Facebook platform. But out of hundreds of millions, we ended up with a study of 10.1m because users were excluded unless they met these four criteria:

1. “18 or older”
2. “log in at least 4/7 days per week”
3. “have interacted with at least one link shared on Facebook that we classified as hard news”
4. “self-report their ideological affiliation” in a way that was “interpretable”

That #4 is very significant. Who reports their ideological affiliation on their profile?

 

 

It turns out that only 9% of Facebook users do that. Of those that report an affiliation, only 46% reported an affiliation in a way that was “interpretable.” That means this is a study about the 4% of Facebook users unusual enough to want to tell people their political affiliation on the profile page. That is a rare behavior.

More important than the frequency, though, is the fact that this selection procedure confounds the findings. We would expect that a small minority who publicly identifies an interpretable political orientation to be very likely to behave quite differently than the average person with respect to consuming ideological political news.  The research claims just don’t stand up against the selection procedure.

But the study is at pains to argue that (italics mine):

“we conclusively establish that on average in the context of Facebook, individual choices more than algorithms limit exposure to attitude-challenging content.”

The italicized portion is incorrect because the appendices explain that this is actually a study of a specific, unusual group of Facebook users. The study is designed in such a way that the selection for inclusion in the study is related to the results. (“Conclusively” therefore also feels out of place.)

Algorithmium: A Natural Element?

Last year there was a tremendous controversy about Facebook’s manipulation of the news feed for research. In the fracas it was revealed by one of the controversial study’s co-authors that based on the feedback received after the event, many people didn’t realize that the Facebook news feed was filtered at all. We also recently presented research with similar findings.

I mention this because when the study states it is about selection of content, who does the selection is important. There is no sense in this study that a user who chooses something is fundamentally different from the algorithm hiding something from them. While in fact the the filtering algorithm is driven by user choices (among other things), users don’t understand the relationship that their choices have to the outcome.

 

 

In other words, the article’s strange comparison between “individual’s choices” and “the algorithm,” should be read as “things I choose to do” vs. the effect of “a process Facebook has designed without my knowledge or understanding.” Again, they can’t be compared in the way the article proposes because they aren’t equivalent.

I struggled with the framing of the article because the research talks about “the algorithm” as though it were an element of nature, or a naturally occurring process like convection or mitosis. There is also no sense that it changes over time or that it could be changed intentionally to support a different scenario.*****

Facebook is a private corporation with a terrible public relations problem. It is periodically rated one of the least popular companies in existence. It is currently facing serious government investigations into illegal practices in many countries, some of which stem from the manipulation of its news feed algorithm. In this context, I have to say that it doesn’t seem wise for these Facebook researchers to have spun these data so hard in this direction, which I would summarize as: the algorithm is less selective and less polarizing. Particularly when the research finding in their own study is actually that the Facebook algorithm is modestly more selective and more polarizing than living your life without it.

 

 

Update: (6pm Eastern)

Wow, if you think I was critical have a look at these. It turns out I am the moderate one.

Eszter Hargittai from Northwestern posted on Crooked Timber that we should “stop being mesmerized by large numbers and go back to taking the fundamentals of social science seriously.” And (my favorite): “I thought Science was a serious peer-reviewed publication.”

Nathan Jurgenson from Maryland and Snapchat wrote on Cyborgology (“in a fury“) that Facebook is intentionally “evading” its own role in the production of the news feed. “Facebook cannot take its own role in news seriously.” He accuses the authors of using the “Big-N trick” to intentionally distract from methodological shortcomings. He tweeted that “we need to discuss how very poor corporate big data research gets fast tracked into being published.”

Zeynep Tufekci from UNC wrote on Medium that “I cannot remember a worse apples to oranges comparison” and that the key take-away from the study is actually the ordering effects of the algorithm (which I did not address in this post). “Newsfeed placement is a profoundly powerful gatekeeper for click-through rates.”

 

Update: (5/10)

A comment helpfully pointed out that I used the wrong percentages in my fourth point when summarizing the piece. Fixed it, with changes marked.

 

Update: (5/15)

It’s now one week since the Science study. This post has now been cited/linked in The New York Times, Fortune, Time, Wired, Ars Technica, Fast Company, Engaget, and maybe even a few more. I am still getting emails. The conversation has fixated on the <4% sample, often saying something like: “So, Facebook said this was a study about cars, but it was actually only about blue cars.” That’s fine, but the other point in my post is about what is being claimed at all, no matter the sample.

I thought my “coal mine” metaphor about the algorithm would work but it has not always worked. So I’ve clamped my Webcam to my desk lamp and recorded a four-minute video to explain it again, this time with a drawing.******

Here’s the video:
https://www.youtube.com/watch?v=eBPntMSDGSs

If the coal mine metaphor failed me, what would be a better metaphor? I’m not sure. Suggestions?

 

 

 

Notes:

* Diversity in hard news, in their study, would be a self-identified liberal who receives a story from FoxNews.com, or a self-identified conservative who receives one from the HuffingtonPost.com, where the stories are about “national news, politics, [or] world affairs.” In more precise terms, for each user “cross-cutting content” was defined as stories that are more likely to be shared by partisans who do not have the same self-identified ideological affiliation that you do.

** I don’t want to make this even more nitpicky, so I’ll put this in a footnote. The paper’s citations to Mutz and Huckfeldt et al. to mean that “exposure to cross-cutting viewpoints is associated with lower levels of political participation” is just bizarre. I hope it is a typo. These authors don’t advocate against exposure to cross-cutting viewpoints.

*** Perhaps this could be a new Facebook motto used in advertising: “Facebook: Better than one speculative dystopian future!”

**** In fact, algorithm and user form a coupled system of at least two feedback loops. But that’s not helpful to measure “amount” in the way the study wants to, so I’ll just tuck it away down here.

***** Facebook is behind the algorithm but they are trying to peer-review research about it without disclosing how it works — which is a key part of the study. There is also no way to reproduce the research (or do a second study on a primary phenomenon under study, the algorithm) without access to the Facebook platform.

****** In this video, I intentionally conflate (1) the number of posts filtered and (2) the magnitude of the bias of the filtering. I did so because the difficulty with the comparison works the same way for both, and I was trying to make the example simpler. Thanks to Cedric Langbort for pointing out that “baseline error” is the clearest way of explaining this.
—

 

(This was cross-posted to The Social Media Collective and Wired.)

(Or, Should I Stay or Should I Go?)

Is it time to boycott “traditional” scholarly publishing? Perhaps you are an academic researcher, just like me. Perhaps, just like me, you think that there are a lot of exciting developments in scholarly publishing thanks to the Internet. And you want to support them. And you also want people to read your research. But you also still need to be sure that your publication venues are held in high regard.

Or maybe you just receive research funding that is subject to new open access requirements.

Academia is a funny place. We are supposedly self-governing. So if we don’t like how our scholarly communications are organized we should be able to fix this ourselves. If we are dissatisfied with the journal system, we’re going to have to do something about it. The question of whether or not it is now time to eschew closed access journals is something that comes up a fair amount among my peers.

It comes up often enough that a group of us at Michigan decided to write an article on the topic. Here’s the article.  It just came out yesterday (open access, of course):

Carl Lagoze, Paul Edwards, Christian Sandvig, & Jean-Christophe Plantin. (2015). Should I stay or Should I Go? Alternative Infrastructures in Scholarly Publishing. International Journal of Communication 9: 1072-1081.

The article is intended for those who want some help figuring out the answer to the question the article title poses: Should I stay or should I go? It’s meant help you decipher the unstable landscape of scholarly publishing these days. (Note that we restrict our topic to journal publishing.)

Researching it was a lot of fun, and I learned quite a bit about how scholarly communication works.

• It contains a mention of the first journal. Yes, the first one that we would recognize as a journal in today’s terms. It’s Philosophical Transactions published by the Royal Society of London. It’s on Volume 373.
• It should teach you about some of the recent goings-on in this area. Do you know what a green repository is? What about an overlay journal? Or the “serials crisis“?
• It addresses a question I’ve had for a while: What the heck are those arXiv people up to? If it’s so great, why hasn’t it spread to all disciplines?
• There’s some fun discussion of influential experiments in scholarly publishing. Remember the daring foundation of the Electronic Journal of Communication? Vectors? Were you around way-back-in-the-day when the pioneering, Web-based JCMC looked like this hot mess below? Little did we know that we were actually looking at the future.(*)

(JCMC circa 1995)

(*): Unless we were looking at the Gopher version, then in that case we were not looking at the future.

Ultimately, we adapt a framework from Hirschman that we found to be an aid to our thinking about what is going on today in scholarly communication. Feel free to play this song on a loop as you read it.

 

(This post has been cross-posted on The Social Media Collective.)

(“And also Bud Light.”)

In my last two posts I’ve been writing about my attempt to convince a group of sophomores with no background in my field that there has been a shift to the algorithmic allocation of attention — and that this is important. In this post I’ll respond to a student question. My favorite: “Sandvig says that algorithms are dangerous, but what are the the most serious repercussions that he envisions?” What is the coming social media apocalypse we should be worried about?

This is an important question because people who study this stuff are NOT as interested in this student question as they should be. Frankly, we are specialists who study media and computers and things — therefore we care about how algorithms allocate attention among cultural products almost for its own sake. Because this is the central thing that we study, we don’t spend a lot of time justifying it.

And our field’s most common response to the query “what are the dangers?” often lacks the required sense of danger. The most frequent response is: “extensive personalization is bad for democracy.” (a.k.a. Pariser’s “filter bubble,” Sunstein’s “egocentric” Internet, and so on). This framing lacks a certain house-on-fire urgency, doesn’t it?

(sarcastic tone:) “Oh, no! I’m getting to watch, hear, and read exactly what I want. Help me! Somebody do something!”

Sometimes (as Hindman points out) the contention is the opposite, that Internet-based concentration is bad for democracy.  But remember that I’m not speaking to political science majors here. The average person may not be as moved by an abstract, long-term peril to democracy as the average political science professor. As David Weinberger once said after I warned about the increasing reliance on recommendation algorithms, “So what?” Personalization sounds like a good thing.

As a side note, the second most frequent response I see is that algorithms are now everywhere. And they work differently than what came before. This also lacks a required sense of danger! Yes, they’re everywhere, but if they are a good thing…

So I really like this question “what are the the most serious repercussions?” because I think there are some elements of the shift to attention-sorting algorithms that are genuinely “dangerous.” I can think of at least two, probably more, and they don’t get enough attention. In the rest of this post I’ll spell out the first one which I’ll call “corrupt personalization.”

Here we go.

Common-sense reasoning about algorithms and culture tells us that the purveyors of personalized content have the same interests we do. That is, if Netflix started recommending only movies we hate or Google started returning only useless search results we would stop using them. However: Common sense is wrong in this case. Our interests are often not the same as the providers of these selection algorithms.  As in my last post, let’s work through a few concrete examples to make the case.

In this post I’ll use Facebook examples, but the general problem of corrupt personalization is present on all of our media platforms in wide use that employ the algorithmic selection of content.

(1) Facebook “Like” Recycling

(Image from ReadWriteWeb.)

On Facebook, in addition to advertisements along the side of the interface, perhaps you’ve noticed “featured,” “sponsored,” or “suggested” stories that appear inside your news feed, intermingled with status updates from your friends. It could be argued that this is not in your interest as a user (did you ever say, “gee, I’d like ads to look just like messages from my friends”?), but I have bigger fish to fry.

Many ads on Facebook resemble status updates in that there can be messages endorsing the ads with “likes.” For instance, here is an older screenshot from ReadWriteWeb:

Another example: a “suggested” post was mixed into my news feed just this morning. recommending World Cup coverage on Facebook itself. It’s a Facebook ad for Facebook, in other words.  It had this intriguing addendum:

So, wait… I have hundreds of friends and eleven of them “like” Facebook?  Did they go to http://www.facebook.com and click on a button like this:

But facebook.com doesn’t even have a “Like” button!  Did they go to Facebook’s own Facebook page (yes, there is one) and click “Like”? I know these people and that seems unlikely. And does Nicolala really like Walmart? Hmmm…

What does this “like” statement mean? Welcome to the strange world of “like” recycling. Facebook has defined “like” in ways that depart from English usage.  For instance, in the past Facebook has determined that:

1. Anyone who clicks on a “like” button is considered to have “liked” all future content from that source. So if you clicked a “like” button because someone shared a “Fashion Don’t” from Vice magazine, you may be surprised when your dad logs into Facebook three years later and is shown a current sponsored story from Vice.com like “Happy Masturbation Month!” or “How to Make it in Porn” with the endorsement that you like it. (Vice.com example is from Craig Condon [NSFW].)
2. Anyone who “likes” a comment on a shared link is considered to “like” wherever that link points to.  a.k.a. “‘liking a share.” So if you see a (real) FB status update from a (real) friend and it says: “Yuck! The McLobster is a disgusting product idea!” and your (real) friend include a (real) link like this one — that means if you clicked “like” your friends may see McDonald’s ads in the future that include the phrase “(Your Name) likes McDonalds.” (This example is from ReadWriteWeb.)

This has led to some interesting results, like dead people “liking” current news stories on Facebook.

There is already controversy about advertiser “like” inflation, “like” spam, and fake “likes,” — and these things may be a problem too, but that’s not what we are talking about here.  In the examples above the system is working as Facebook designed it to. A further caveat: note that the definition of “like” in Facebook’s software changes periodically and when they are sued. Facebook now has an opt-out setting for the above two “features.”

But these incendiary examples are exceptional fiascoes — on the whole the system probably works well. You likely didn’t know that your “like” clicks are merrily producing ads on your friends pages and in your name because you cannot see them.  These “stories” do not appear on your news feed and cannot be individually deleted.

Unlike the examples from my last post you can’t quickly reproduce these results with certainty on your own account. Still, if you want to try, make a new Facebook account under a fake name (warning! dangerous!) and friend your real account. Then use the new account to watch your status updates.

Why would Facebook do this? Obviously it is a controversial practice that is not going to be popular with users. Yet Facebook’s business model is to produce attention for advertisers, not to help you — silly rabbit. So they must have felt that using your reputation to produce more ad traffic from your friends was worth the risk of irritating you. Or perhaps they thought that the practice could be successfully hidden from users — that strategy has mostly worked!

In sum this is a personalization scheme that does not serve your goals, it serves Facebook’s goals at your expense.

(2) “Organic” Content

This second group of examples concerns content that we consider to be “not advertising,” a.k.a. “organic” content. Funnily enough, algorithmic culture has produced this new use of the word “organic” — but has also made the boundary between “advertising” and “not advertising” very blurry.

 

The general problem is that there are many ways in which algorithms act as mixing valves between things that can be easily valued with money (like ads) and things that can’t. And this kind of mixing is a normative problem (what should we do) and not a technical problem (how do we do it).

For instance, for years Facebook has encouraged nonprofits, community-based organizations, student clubs, other groups, and really anyone to host content on facebook.com.  If an organization creates a Facebook page for itself, the managers can update the page as though it were a profile.

Most page managers expect that people who “like” that page get to see the updates… which was true until January of this year. At that time Facebook modified its algorithm so that text updates from organizations were not widely shared. This is interesting for our purposes because Facebook clearly states that it wants page operators to run Facebook ad campaigns, and not to count on getting traffic from “organic” status updates, as it will no longer distribute as many of them.

This change likely has a very differential effect on, say, Nike‘s Facebook page, a small local business‘s Facebook page, Greenpeace International‘s Facebook page, and a small local church congregation‘s Facebook page. If you start a Facebook page for a school club, you might be surprised that you are spending your labor writing status updates that are never shown to anyone. Maybe you should buy an ad. Here’s an analytic for a page I manage:

 

The impact isn’t just about size — at some level businesses might expect to have to insert themselves into conversations via persuasive advertising that they pay for, but it is not as clear that people expect Facebook to work this way for their local church or other domains of their lives. It’s as if on Facebook, people were using the yellow pages but they thought they were using the white pages.  And also there are no white pages.

(Oh, wait. No one knows what yellow pages and white pages are anymore. Scratch that reference, then.)

No need to stop here, in the future perhaps Facebook can monetize my family relationships. It could suggest that if I really want anyone to know about the birth of my child, or I really want my “insightful” status updates to reach anyone, I should turn to Facebook advertising.

Let me also emphasize that this mixing problem extends to the content of our personal social media conversations as well. A few months back, I posted a Facebook status update that I thought was humorous. I shared a link highlighting the hilarious product reviews for the Bic “Cristal For Her” ballpoint pen on Amazon. It’s a pen designed just for women.

The funny thing is that I happened to look at a friend of mine’s Facebook feed over their shoulder, and my status update didn’t go away. It remained, pegged at the top of my friend’s news feed, for as long as 14 days in one instance. What great exposure for my humor, right? But it did seem a little odd… I queried my other friends on Facebook and some confirmed that the post was also pegged at the top of their news feed.

I was unknowingly participating in another Facebook program that converts organic status updates into ads. It does this by changing their order in the news feed and adding the text “Sponsored” in light gray, which is very hard to see. Otherwise at least some updates are not changed. I suspect Facebook’s algorithm thought I was advertising Amazon (since that’s where the link pointed), but I am not sure.

This is similar to Twitter’s “Promoted Tweets” but there is one big difference.  In the Facebook case the advertiser promotes content — my content — that they did not write. In effect Facebook is re-ordering your conversations with your friends and family on the basis of whether or not someone mentioned Coke, Levi’s, and Anheuser Busch (confirmed advertisers in the program).

Sounds like a great personal social media strategy there–if you really want people to know about your forthcoming wedding, maybe just drop a few names? Luckily the algorithms aren’t too clever about this yet so you can mix up the word order for humorous effect.

(Facebook status update:) “I am so delighted to be engaged to this wonderful woman that I am sitting here in my Michelob drinking a Docker’s Khaki Collection. And also Coke.”

Be sure to use links. I find the interesting thing about this mixing of the commercial and non-commercial to be that it sounds to my ears like some sort of corny, unrealistic science fiction scenario and yet with the current Facebook platform I believe the above example would work. We are living in the future.

So to recap, if Nike makes a Facebook page and posts status updates to it, that’s “organic” content because they did not pay Facebook to distribute it. Although any rational human being would see it as an ad. If my school group does the same thing, that’s also organic content, but they are encouraged to buy distribution — which would make it inorganic. If I post a status update or click “like” in reaction to something that happens in my life and that happens to involve a commercial product, my action starts out as organic, but then it becomes inorganic (paid for) because a company can buy my words and likes and show them to other people without telling me. Got it? This paragraph feels like we are rethinking CHEM 402.

The upshot is that control of the content selection algorithm is used by Facebook to get people to pay for things they wouldn’t expect to pay for, and to show people personalized things that they don’t think are paid for. But these things were in fact paid for.  In sum this is again a scheme that does not serve your goals, it serves Facebook’s goals at your expense.

The Danger: Corrupt Personalization

With these concrete examples behind us, I can now more clearly answer this student question. What are the most serious repercussions of the algorithmic allocation of attention?

I’ll call this first repercussion “corrupt personalization” after C. Edwin Baker. (Baker, a distinguished legal philosopher, coined the phrase “corrupt segmentation” in 1998 as an extension of the theories of philosopher Jürgen Habermas.)

Here’s how it works: You have legitimate interests that we’ll call “authentic.” These interests arise from your values, your community, your work, your family, how you spend your time, and so on. A good example might be that as a person who is enrolled in college you might identify with the category “student,” among your many other affiliations. As a student, you might be authentically interested in an upcoming tuition increase or, more broadly, about the contention that “there are powerful forces at work in our society that are actively hostile to the college ideal.”

However, you might also be authentically interested in the fact that your cousin is getting married. Or in pictures of kittens.

Corrupt personalization is the process by which your attention is drawn to interests that are not your own. This is a little tricky because it is impossible to clearly define an “authentic” interest. However, let’s put that off for the moment.

In the prior examples we saw some (I hope) obvious places where my interests diverged from that of algorithmic social media systems. Highlights for me were:

• When I express my opinion about something to my friends and family, I do not want that opinion re-sold without my knowledge or consent.
• When I explicitly endorse something, I don’t want that endorsement applied to other things that I did not endorse.
• If I want to read a list of personalized status updates about my friends and family, I do not want my friends and family sorted by how often they mention advertisers.
• If a list of things is chosen for me, I want the results organized by some measure of goodness for me, not by how much money someone has paid.
• I want paid content to be clearly identified.
• I do not want my information technology to sort my life into commercial and non-commercial content and systematically de-emphasize the noncommercial things that I do, or turn these things toward commercial purposes.

More generally, I think the danger of corrupt personalization is manifest in three ways.

1. Things that are not necessarily commercial become commercial because of the organization of the system. (Merton called this “pseudo-gemeinschaft,” Habermas called it “colonization of the lifeworld.”)
2. Money is used as a proxy for “best” and it does not work. That is, those with the most money to spend can prevail over those with the most useful information. The creation of a salable audience takes priority over your authentic interests. (Smythe called this the “audience commodity,” it is Baker’s “market filter.”)
3. Over time, if people are offered things that are not aligned with their interests often enough, they can be taught what to want. That is, they may come to wrongly believe that these are their authentic interests, and it may be difficult to see the world any other way. (Similar to Chomsky and Herman’s [not Lippman’s] arguments about “manufacturing consent.”)

There is nothing inherent in the technologies of algorithmic allocation that is doing this to us, instead the economic organization of the system is producing these pressures. In fact, we could design a system to support our authentic interests, but we would then need to fund it. (Thanks, late capitalism!)

To conclude, let’s get some historical perspective. What are the other options, anyway? If cultural selection is governed by computer algorithms now, you might answer, “who cares?” It’s always going to be governed somehow. If I said in a talk about “algorithmic culture” that I don’t like the Netflix recommender algorithm, what is supposed to replace it?

This all sounds pretty bad, so you might think I am asking for a return to “pre-algorithmic” culture: Let’s reanimate the corpse of Louis B. Mayer and he can decide what I watch. That doesn’t seem good either and I’m not recommending it. We’ve always had selection systems and we could even call some of the earlier ones “algorithms” if we want to.  However, we are constructing something new and largely unprecedented here and it isn’t ideal. It isn’t that I think algorithms are inherently dangerous, or bad — quite the contrary. To me this seems like a case of squandered potential.

With algorithmic culture, computers and algorithms are allowing a new level of real-time personalization and content selection on an individual basis that just wasn’t possible before. But rather than use these tools to serve our authentic interests, we have built a system that often serves a commercial interest that is often at odds with our interests — that’s corrupt personalization.

If I use the dominant forms of communication online today (Facebook, Google, Twitter, YouTube, etc.) I can expect content customized for others to use my name and my words without my consent, in ways I wouldn’t approve of. Content “personalized” for me includes material I don’t want, and obscures material that I do want. And it does so in a way that I may not be aware of.

This isn’t an abstract problem like a long-term threat to democracy, it’s more like a mugging — or at least a confidence game or a fraud. It’s violence being done to you right now, under your nose. Just click “like.”

In answer to your question, dear student, that’s my first danger.

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ADDENDUM:

This blog post is already too long, but here is a TL;DR addendum for people who already know about all this stuff.

I’m calling this corrupt personalization because I cant just apply Baker’s excellent ideas about corrupt segments — the world has changed since he wrote them. Although this post’s reasoning is an extension of Baker, it is not a straightforward extension.

Algorithmic attention is a big deal because we used to think about media and identity using categories, but the algorithms in wide use are not natively organized that way. Baker’s ideas were premised on the difference between authentic and inauthentic categories (“segments”), yet segments are just not that important anymore. Bermejo calls this the era of post-demographics.

Advertisers used to group demographics together to make audiences comprehensible, but it may no longer be necessary to buy and sell demographics or categories as they are a crude proxy for purchasing behavior. If I want to sell a Subaru, why buy access to “Brite Lights, Li’l City” (My PRIZM marketing demographic from the 1990s) when I can directly detect “intent to purchase a station wagon” or “shopping for a Subaru right now”? This complicates Baker’s idea of authentic segments quite a bit. See also Gillespie’s concept of calculated publics.

Also Baker was writing in an era where content was inextricably linked to advertising because it was not feasible to decouple them. But today algorithmic attention sorting has often completely decoupled advertising from content. Online we see ads from networks that are based on user behavior over time, rather than what content the user is looking at right now. The relationship between advertising support and content is therefore more subtle than in the previous era, and this bears more investigation.

Okay, okay I’ll stop now.

(This post was cross-posted to The Social Media Collective.)