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# If...Then Algorithmic Power and Politics

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Moreover, weights allow the afinity function to be used by various processes and diferent purposes within the system. Besides “newsfeed algorithms,” a measure of afinity may be requested by “ad targeting algorithms and friend suggestion algorithms” to target their content and recommendations ( Juan and Hua,  2012). As such, afinity scores may constitute useful social endorsement metrics used to provide a social context for advertisements that are shown to a particular user ( Juan and Hua, 2012). For example, “a social networking system may display banner ads for a concert to members who include an afinity for the performing band in their social networking system profile and live near a concert venue where that band might be performing” (Adams and Pai, 2013: 1). Afinity scores are also used by the system to determine which friends automatically to invite for social games or recommend for pokes or other forms of phatic communication.

Facebook acknowledges that the strength of connections may vary as a function of time and context. Because user interests change, weights are continuously adjusted to cater beter to specific situations. As the time decay factor emphasized in the original EdgeRank formula indicates, time is of the essence. While there are many excellent accounts of social media as real-time streams (Berry,  2011; Kaun and Stiernstedt, 2014; Weltevrede et al. 2014), the algorithmic logic of Facebook shows how the news feed is not necessarily governed by the logic of real time. Algorithmic media such as Facebook are more about “right time” than they are about “real time.” After all, as Facebook repeatedly emphasizes in its News Feed FYI blog series: “Our goal is to show the right content to the right people at the right time so they don’t miss the stories that are important to them” (Kacholia, 2013; Ge, 2013; Owens and Vickrey, 2014, emphasis added). While the scale of content and activity on Facebook may restrict showing content in real time, the real challenge for Facebook is not showing stories in chronological time but finding the most interesting and relevant pieces of content in an ever-increasing flood of content (Luu, 2013). It is not chronos but kairos that constitutes the key temporal mode of algorithmic media.14 Kairos, the ancient Greek concept designating the right or opportune moment to say or do something (Smith,  1969), provides a useful way of understanding the kind of temporal work that algorithms perform. As John E. Smith argues, “kairos, or the ‘right time’, as the term is often translated, involves ordinality or the conception of a special temporal position” (1969: 1). Smith’s comparison between ordinal numbers and kairos suggests that the right moment can only be understood in relation to past and future points in time. Under conditions of permanent change, timing becomes critical.

How, then, does the news feed algorithm determine when the time is right? One way for Facebook to show timely content higher-up in the news feed is to factor in trending topics, showing people stories about things that are trending as soon as they occur (Owens and Vickrey, 2014). As is the case with Twiter’s trending topics, the notion of what a trend is may not be as straightforward as one might first think. As Gillespie (2011) has argued, “the algorithms that define what is ‘trending’ or what is ‘hot’ or what is ‘most popular’ are not simple measures, they are carefully designed to capture something the site providers want to capture.” A trend is not a simple measure but a most specific one, defined as a topic that spikes, as something that exceeds single clusters of interconnected users, new content over retweets, new terms over already trending ones (Gillespie, 2011). Although Facebook generally conceives of a trend as an object, topic, or event that is popular at a specific moment in time, the ways in which a trend is computationally defined is much more complex.15 Stories posted on Facebook about a topic that is currently trending (for example, about a national sports event or connected to a specific hashtag) are more likely to appear higher up in news feed. In addition, as Owens and Vickrey (2014) point out, deciding what to show on the news feed is not merely a measure of the total amount of previous engagement, as in the total number of likes, but also depends on when people choose to like, comment, and share. Moreover, timeliness is not simply a mater of current events. The time may be right to show a story even many days after it was originally posted. Facebook calls this practice story-bumping, meaning that stories people did not scroll down far enough to see are resurfaced, based on measures of engagement and other factors. The ordinality of kairos can be seen in the ways in which the news feed algorithm is clearly geared toward providing stories that are sensitive to a specific time and context. As Zuckerberg et al. explain in a news feed–related patent application:

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For example, a user planning a trip may be very interested in news of other users who have traveled recently, in news of trips identified as events by other users, and in travel information, and then be much less interested in these relationships, events, objects, or categories or subcategories thereof upon his return. Thus, items of media content associated with another user who has traveled recently may receive a large weighting relative to other items of media, and the weighting will decay steeply so that the weighting is low by the time of the user’s return. (Zuckerberg et al. 2012: 5)

Afinity, weight, and time are as dynamic as the news feed itself. Constantly changed and fine-tuned, there is no clearcut way in which a certain state of the feed can easily be discerned. Like the prospect of moving furniture at the Facebook headquarters, the news feed ofers users a slightly diferently designed space to hang out in every time they log in. While it is unlikely that the sofas at the headquarters are really moved that often or to radically diferent areas of the building, the prospect of continuous change is what is important. The culture of not knowing how the ofice may look when entering the building each day or the uncertainties Facebook users face when logging in are not that diferent. The main diference is that users may never really know what they are exposed to, even after entering the digital building that is Facebook. A moving sofa is easily detectable; an A/B test is not. Users will never know exactly what they are witness to or how the world appearing on their screens came into existence. Living in an algorithmically mediated environment means learning to live in the moment and not to expect to step into the same river twice.16 It also means acknowledging that talking about EdgeRank, the news feed algorithm, or whatever one now wants to call it, means talking about a general underlying logic, not its exact mathematic formula.17

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In general, the system is geared toward maximizing user engagement and will present what it deems to be the most interesting content in the hope that the user will be more likely to “take actions” ( Juan and Hua, 2012). The anticipatory logic inherent in the functioning of these algorithms is not primarily geared towards confirming some pre-existing cultural logic but, rather, toward a mode of government that has the capacity to take into account the probability of subjects’ actions. After all, “the predictions could then be used to encourage more user interaction” (Juan and Hua, 2012: 2). By looking at the ways in which the specificities of the Facebook platform, exemplified here through the Facebook algorithm, enables and constrains ways of becoming visible online, we can begin to rethink regimes of visibility that hinge on and operate through algorithmic architectures. In doing so, in this chapter I expand on Foucault’s idea of “panopticism” as it provides a useful and still highly relevant analytic for understanding the ways in which visibility is technologically structured.

Rethinking Regimes of Visibility

Algorithms establish new conditions through which visibility is constructed online. Social media sites are designed—computational spaces that determine how people socialize and communicate. As we have seen thus far in the book, the sociotechnical assemblage of which algorithms are part produce the conditions though which data are put into forms of meaningfulness, that is, determining what will appear on the screens of our digital devices. To understand how the distribution of the sensible happens and with what possible efects, the question is how visibility comes into being—through what mechanisms, logics, and practices of governance.

PANOPTICISM

Foucault operates with two basic notions of how things are made visible or shown, exemplified in his notion of the spectacle and surveillance. It is not just a mater of what is seen in a given historical context, but what can be seen and how the realm of the seeable and sayable is constructed in order to make a particular regime of visibility appear. As sociologist John Thompson explains, whereas the spectacle designates a regime of visibility in which a small number of subjects are made visible to many, surveillance mechanisms from the 16th century onward are connected to the emergence of disciplinary societies in which the visibility of the many is assured by a small number of subjects (2005: 39). Surveillance as a mode of visibility was famously exemplified in the architectural arrangement of the Panopticon.

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Adapting the figure of the Panopticon from Jeremy Bentham, Foucault sought to explain the regulatory force of power inherent in specific architectural compositions. The Panopticon designates an architectural vision of a prison, a circular building with an observation tower in the middle. The principle of this architectural vision is to render the subject, whether “a madman, a patient, a condemned man, a worker or a schoolboy” (Foucault, 1977: 200), to a state of permanent visibility. As Foucault explains: “All that is needed, then, is to place a supervisor in a central tower” in order to create the impression that someone might be watching (1977: 200). More than actually placing a prison guard in the tower, the purpose of the architectural design is to create a space in which one can never be certain whether one is being watched or not. As Foucault elaborates:

Hence the major efect of the Panopticon: to induce in the inmate a state of conscious and permanent visibility that assures the automatic functioning of power. So to arrange things that the surveillance is permanent in its efects, even if it is discontinuous in its action; that the perfection of power should tend to render its actual exercise unnecessary; that this architectural apparatus should be a machine for creating and sustaining a power relation independent of the person who exercises it; in short, that the inmates should be caught up in a power situation of which they are themselves the bearers. (1977: 201)

The uncertainty associated with the possibility of always being watched inevitably leads the subject to adjust his or her behavior accordingly, to behave as if they are indeed permanently observed. Surveillance, thus, signifies a state of permanent visibility. The novelty of Foucault’s notion of visibility, constructed by the specificities of the historically contingent architectural apparatus, lies precisely in highlighting the technical organization of power. As Foucault points out, the Panopticon is not a dream building: “it is the diagram of a mechanism of power reduced to its ideal form […] it is in fact a figure of political technology” (Foucault, 1977: 205). Power “has its principle not so much in a person as in a certain concerted distribution of bodies, surfaces, lights, gazes; in an arrangement whose internal mechanisms produce the relation in which individuals are caught up” (Foucault, 1977: 202). By highlighting the diagrammatic function of panoptic surveillance, Foucault provides a forceful analytical framework for understanding diferent modalities of visibility and the mechanisms by which it is being arranged.

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As Rajchman points out in his discussion of Foucault: “Architecture helps ‘visualize’ power in other ways than simply manifesting it. It is not simply a mater of what a building shows ‘symbolically’ or ‘semiotically’, but also of what it makes visible about us and within us” (1988: 103). Conceiving of visibility as an organization of power in both a negative and a positive sense, Foucault shows that “spaces are designed to make things seeable, and seeable in a specific way” (Rajchman, 1988). Prisons, hospitals, and social networking sites are essentially spaces of “constructed visibility.” The realm of visibility created by the panoptic architecture did not work primarily through a certain iconography or a visual semiotic regime but, first and foremost, through the technical structuring of a way of being, implementing an awareness or atentiveness to the constant possibility of inspection. To highlight visibility as a system, a diagram, is to highlight the “distribution of individuals in relation to one another, of hierarchical organization, of dispositions of centres and channels of power” (Foucault, 1977: 205). It is precisely this notion of a material or technical structuring of visibility that seems especially interesting and relevant in terms of new media. The spaces designed by the (im)material conditions of the software are similarly designed to make things visible and, thus, knowable in a specific way.

T H R E AT O F I N V IS I B I L I T Y

The mode of visibility at play in Facebook, as exemplified by the news feed algorithm, difers from that of disciplinary societies in one particularly interesting way. The technical architecture of the Panopticon makes sure that the uncertainty felt by the threat of permanent visibility is inscribed in the subject, who subsequently ad justs his/her behavior. While one of the premises of the panoptic diagram pertains to an even distribution of visibility in which each individual is subjected to the same level of possible inspection, the news feed does not treat individuals equally. There is no perceivable centralized inspector who monitors and casts everybody under the same permanent gaze. In Facebook, there is not so much a “threat of visibility” as there is a “threat of invisibility” that seems to govern the actions of its subjects. The problem is not the possibility of constantly being observed but the possibility of constantly disappearing, of not being considered important enough. In order to appear, to become visible, one needs to follow a certain platform logic embedded in the architecture of Facebook.

The threat of invisibility should be understood both literally and symbolically. Whereas the architectural form of the Panopticon installs a regime of visibility whereby “one is totally seen, without ever seeing” (Foucault, 1977: 202), the algorithmic arrangements in Facebook install visibility in a much more unstable fashion: One is never totally seen or particularly deprived of a seeing capacity. As is the  case in the Panopticon, the individual Facebook users can be said to occupy equally confined spaces. Like the carefully and equally designed prison cells, user profiles represent templates that “provide fixed positions and permit circulation”

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(Foucault, 1977: 148). Just as with the specific machines (i.e., military, prisons, hospitals) described by Foucault, it is not the actual individual that counts in Facebook. This is why spaces are designed in such a way as to make individuals interchangeable. The generic template structure of Facebook’s user profiles provide not so much a space for specific individuals but a space that makes the structured organization of individuals’ data easier and more manageable. The system, then, does not particularly care for the individual user as much as it thrives on the decomposition and recomposition of the data that users provide. However, whereas the architecture of  the Panopticon makes all inmates equally subject to permanent visibility, the Facebook algorithm does not treat subjects equally; it prioritizes some above others. Whereas visibility, as a consequence of the panoptic arrangement, is abundant and experienced more like a threat imposed from outside powers, visibility in the Facebook system arguably works the opposite way. The algorithmic architecture of the news feed algorithm does not automatically impose visibility on all subjects. Visibility is not something ubiquitous but rather something scarce. As content, users, and activities on the Facebook platform grow and expand, visibility becomes even scarcer. According to Brian Boland (2014), who leads the Ads Product Marketing team at Facebook, this means that “competition in news feed is increasing” and that it is “becoming harder for any story to gain exposure in news feed.”

In order to see how many posts actually made it to the news feed, I conducted an experiment of what could be considered a process of “reversed engineering.”18 Recall how it was claimed in the previous chapter that the first step in knowing algorithms is not to regard the impossibility of seeing inside the black box as an epistemological limit. Instead, what I termed a “technographic inquiry” starts by asking what algorithms are suggestive of by observing the outcomes of algorithmic procedures as indicative of its “point of view.” Over the course of several months (March-September, 2011), I used my own personal Facebook profile to compare the contents of the “top news” to that of the “most recent.” The most intensive investigation took place during April 2011 when I did the comparison a couple of times a week, took screen shots of the entire top news feeds, and manually counted the posts in the most recent feeds. I took the oldest story published in the top news and compared it to the number of stories published in the most recent feed up to the same time stamp. On a randomly selected day in April 2011, this amounted to 280 stories/updates published on the most recent feed as opposed to 45 posts appearing in the top news feed within the same timeframe. At first glance, only 16% of the possible stories seem to have made it to the top news. As time decay is one of the major known factors of the Facebook news feed algorithm, it is safe to assume that there is a higher probability of making it into the top news the closer to “real time” the story is published. In fact, my experiment showed that, if a story was published within the last three hours, the chance of geting onto the top news was between 40 and 50%. In addition to selecting from the total number of updates generated by friends from the real-time stream, top news also displays its own tailored news stories that are not displayed in the most-recent feed. I call these stories “communication stories,” as they make a story out of two friends’ recent communicative interaction.19 Communication stories in Facebook typically take the form of “X commented on Y’s photo” or “X likes Y’s link.” Taking these tailored stories into the equation, a beter estimate for the 45/280 ratio would be a mere 12% chance of geting in the top news. Of all the 45 stories published, 17 were communication stories. What most of these 17 communication stories had in common was a high degree of interaction. For example, a typical story would say: “Anna commented on Claire’s photo” along with “11 people like this” and “View all 14 comments.” Not only does Facebook tailor specific stories for the top news feed, these stories also receive a significant amount of visibility as opposed to other types of edges. Presumably, the emphasis on quantification is supposed to simulate the impression of high activity in order to lower the threshold for participation on the platform.

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A diferent top news sample from the experiment reveals the following: Of 42 posts displayed, only three of the stories published by my “friends” came without any form of interaction by others (that is, without any likes or comments). My top news was filled with stories that obviously signify engagement and interaction. Although distribution of the specific types of stories published varied over the course of the six months in which I systematically studied the news feed, stories without significant interaction seemed to be filtered out. The fact that there were almost no stories by friends that made it to the top news without any form of engagement by others strengthens the impression of an algorithmic bias toward making those stories that signify engagement more visible than those that do not. No mater how meticulous the counting and comparing between the two feeds may be, the exact percentage of stories making it to the top news remains largely obscure.

However, my initial experiments showed what Facebook only later confirmed— that, on average, Facebook users only see a small percentage of what they could have been seeing. Of the “1,500+ stories a person might see whenever they log onto Facebook, news feed displays approximately 300” (Boland, 2014). Making it onto the news feed has become a competitive advantage, especially for an increasing number of business pages. On Facebook, becoming visible is to be selected by the algorithm. Inscribed in the algorithmic logic of the news feed is the idea that visibility functions as a reward rather than as punishment, as is the case with Foucault’s notion of panopticism. This is even truer today than when the data reported on in this chapter were collected. In the first half of 2011, the top news feed primarily showed a mixture of stories from friends and pages, giving prominence to cluster stories or what I have called communication stories to provide the impression of constant activity and engagement. While stories of the type “X commented on Y’s photo” are still shown in the news feed, most of these stories are now shown as they happen and displayed as part of the much smaller Ticker feed. Talking about changes to the news feed since its conception in 2006, Chris Cox, vice president for products at Facebook, remarked that the most noticeable changes have come from what he sees as emergent types of activity—specifically, content derived from “groups” (King, 2013). Indeed, when I look at the kind of content shown on the news feed at the time of this writing in early 2015, stories from the diferent groups of which I am a member figure much more prominently than before. Now, updates from diferent housing search groups, local buy-and-sell groups, or community groups are competing with updates from friends and “pages” for atention in the news feed.

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Algorithms are fundamentally relational in the sense that they depend on some kind of external input (data) in order to function. Algorithms do not just represent a rigid, preprogrammed structure, understood as “recipes or sets of steps expressed in flowcharts, code or pseudocode” (Mackenzie,  2006: 43). They are also fluid, adaptable, and mutable. This means that the Facebook algorithm is not something that merely acts upon users from above. The power of the Facebook algorithm arises instead from its interrelationships with users. For example, as exemplified in the “News Feed FYI” blog series, “People who tend to watch more video in news feed  should expect to see more videos near the top of their Feed” (Welch and Zhang, 2014). Not only is there an obvious circularity to this logic of showing more or the same, the agency of algorithms—their capacity for action—is ultimately put into the hands of users. How Facebook’s news feed algorithm will process the data that I provide, therefore, also depends fundamentally on me as well as my relationships with my “friends.” This is also the principle of machine learning, the ethos of contemporary computational culture. For instance, top news dynamically updates, depending on how many times I visit Facebook, which makes it dificult to make a general claim about the percentage of stories making it to the top news. On a random day in September 2011, I compared the same top news feed before and after checking the most recent feed, which at that time counted over 300 new posts.20 What I found was a change of about 34% in stories displayed between the two instances of checking top news.21 After I checked the most-recent feed, 16 of the 47 posts in the top news feed had immediately changed. The quite noticeable change in stories displayed can be explained by referring back to the workings of the Facebook algorithm. In the first round of checking my top news, the algorithm seemed to put more emphasis on the “time decay” mechanism, since I knew that it had been a while since I had last logged onto Facebook. After checking the most recent feed, however, Facebook “knew” I was “up to date” again, replacing 16 of the previous posts with ones that the system had apparently calculated to be of greater “interest” to me. All 16 new stories displayed in the second round of checking had been posted between 12 and 23 hours after the time of checking and were either of the “communication story” type or stories that had generated a good number of likes and comments. In addition to the frequency with which users check their Facebook, time spent also plays a role. This is to say that the dynamically changing feed also takes into account how long users spend engaging with the stories, not merely how often they check them. While the algorithmic architecture works dynamically, thereby making analysis of its workings dificult, we can treat them as a particular way of framing the environments upon which they work (Mackenzie, 2007). We may say that Facebook’s news feed algorithm, acting as a gatekeeper of user-generated content, demarcates visibility as something that cannot be taken for granted. The uncertainty connected to the level of visibility and constant possibility of “disappearing” in relation to the “variable ontology” of software frames visibility as something exclusive. Becoming visible on the news feed is, thus, constructed as something to which to aspire rather than to feel threatened by.

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PA R T IC I PATO R Y S U B J E C T I V I T Y

The threat of invisibility on Facebook, then, is not merely a symbolical phenomenon but is also, quite literally, real. While the regime of visibility created by Facebook may difer from the one Foucault described in terms of surveillance, understood as imposing a state of permanent visibility, discipline is still part of the new diagrammatic mechanisms. While it has become commonplace to argue for the transition of a disciplinary society into a control society after the post-industrial fact described by Deleuze (1992), I do not see a necessary contradiction between the disciplinary diagram and software-mediated spaces. Discipline simply refers to a diagram that operates by making the subject the “principle of (its) own subjection” (Foucault, 1977: 203). Discipline denotes a type of power that economizes its functioning by making subjects responsible for their own behavior. This is still very much the case with the logic of feedback loops perpetuated by contemporary machine learning techniques. Contrary to the notion that we have moved away from a disciplinary society to a control society as the “Deleuzian turn” suggests, I agree with Mark Kelly (2015) in that many of the aspects ascribed to control societies were already covered by Foucault in his notion of discipline. For Foucault, “Discipline ‘makes’ individuals; it is the specific technique of a power that regards individuals both as objects and as instruments of its exercise” (Foucault, 1977: 170). It imposes a particular conduct on a particular human multiplicity (Deleuze,  2006: 29). It is important here to highlight that Foucault developed the notion of disciplinary power in order to account for the duality of power and subjectivation—efectuated by “training” subjects to think and behave in certain ways and, thus, to become the principle of their own regulation of conduct. Through the means of correct training, subjects are governed so as to reach their full potentiality as useful individuals (Foucault,  1977: 212). Foucault identified three techniques of correct training: hierarchical observation, normalizing judgment, and the examination. In this sense, we could say that the news feed algorithm exercises a form of disciplinary power since discipline, in Foucault’s words, “fixes; it arrests or regulates movements; it clears up confusion; it dissipates compact groupings of individuals wandering about the country in unpredictable ways; it establishes calculated distributions” (1977: 219).

For Facebook, a useful individual is the one who participates, communicates, and interacts. The participatory subject evidently produced by the algorithmic mechanisms in Facebook follows a similar logic to those techniques of correct training at work in sustaining disciplinary power. First, the very real possibility of becoming obsolete inscribed in the “threat of invisibility” arguably instigates a desire to participate. Here, we can see the dual logic inherent in Foucault’s understanding of power as both constraining and enabling. While visibility is constrained by the failure to conform to the inherent logic of participation, visibility is also produced and enabled by the same logic. As Foucault asserts: “What is specific to the disciplinary penalty is non-observance, that which does not measure up to the rule, that departs from it” (1977: 178). Not conforming to the rules set out by the architectural program is, thus, punishable. That is, not participating on Facebook will get you punished by making you invisible, and not seeing what may be “the most interesting” content to you.

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Second, making it appear as if everybody is participating and communicating by emphasizing those stories that generate a lot of comments and likes provides an incentive to like or comment as well. Simulation creates an impression, and it is precisely the power of impressions that Foucault thought was a driving force in the governing of the self. As Hofman, elaborating on Foucault’s notion of disciplinary power, explains: “Disciplinary power judges according to the norm. He depicts the norm as a standard of behaviour that allows for the measurement of forms of behaviour as ‘normal’ or ‘abnormal’ ” (2011: 32). By creating the impression that everybody participates, Facebook simultaneously suggests that participation is the norm. Normalization, according to Foucault, created a “whole range of degrees of normality indicating membership of a homogeneous social body but also playing a part in classification, hierarchization and the distribution of rank” (1977: 184). The news feed algorithm can be said to function as a disciplinary technique, producing a “desired user” by constructing subjects through sociotechnical arrangements that emphasize and favor participation, sharing, and socialization. Because interaction functions as a measure for interestingness, practices of liking, commenting, and participation become processes through which the subject may approximate this desired normality.

Third, the participatory subject constituted by the algorithm hinges on an underlying idea of popularity. Displaying edges with a high degree of interaction clearly remediates some well-known cultural assumptions and mass media logic—popularity fosters more popularity. There is, thus, a circular logic to the way in which visibility is organized on Facebook. Being popular enhances the probability of becoming visible, thus increasing the probability of generating even more interaction. Being confronted with communication stories that encourage the user to “view all  14 comments,” “view all 9 comments,” and acknowledge that “Christina and 7 others like this” enhances the impression that visibility is granted to the popular. By emphasizing the perceived popularity of certain connections, the algorithm also reinforces a regime of visibility that runs counter to much of the celebratory Web 2.0 discourse focusing on democratization and empowerment. While Facebook is certainly a space that allows for participation, the software suggests that some forms of participation are more desirable than others, whereby desirability can be mapped in the specific mechanisms of visibility that I have suggested throughout this chapter.

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