Introduction: Gaining “Meaningful Access” to Privacy Policies
There is an increasing need to attend to the role social media plays in our society as more of the work of maintaining relationships moves to online platforms. While platforms like Facebook and YouTube have experienced increased public scrutiny, a 2019 Pew Research Center study found that social media usage remained relatively unchanged from 2016 to 2018, with seven out of ten adults reporting they rely on social media platforms to get information (Perrin and Anderson 2019). International data-collection scandals like Cambridge Analytica and numerous congressional hearings on Big Tech’s’ power in the United States have not deterred the general public from using social media. Everyday users are increasingly aware that their privacy is compromised by using social media platforms, and many agree that Silicon Valley needs more regulation (Perrin and Anderson 2019; Pew Research Center 2019). Yet, many of these same users continue to rely on social media platforms like Facebook, Twitter, and TikTok to inform themselves on important issues in our society.
Usually long and full of legalese, privacy policies are often ignored by students (and most users) who simply click “agree” instead of reading the terms. This means users are less knowledgeable about the privacy policies they agree to in order to continue using social media platforms. As Obar and Oeldorf-Hirsch find in their study “The Biggest Lie on the Internet: Ignoring the Privacy Policies and Terms of Service Policies of Social Networking Services,” undergraduate students in the U.S. find privacy policies to be “nothing more than an unwanted impediment to the real purpose users go online—the desire to enjoy the ends of digital production” (Obar and Oeldorf-Hirsch 2020, 142). To this point, the 2019 Pew Research Center survey “Americans and Digital Knowledge” found that only 48% of Americans understood how privacy policies function as contracts between themselves and a website concerning the use of their data. Through their alluring affordances and obscure privacy policies, social media platforms hinder users’ ability to meaningfully engage with the data exploitation these platforms rely on.
Americans have long turned to policy for contending with sociocultural issues. While breaches of user privacy energize the public, the scale of social media platforms makes it difficult to fully comprehend these violations of trust; as long as social media works as we expect it to, users rarely question what social media platforms are doing behind the scenes. As mentioned earlier, privacy policies are also oftentimes long, jargon-filled, and unapproachable to the average user. How many of us can say we have read, let alone comprehended, all of the fine print of the privacy policies of the platforms we choose to engage on every day? Doing so requires what digital rhetorics scholar Adam J. Banks refers to in Race, Rhetoric, and Technology as “meaningful access,” or access to not only the technology itself but also to the knowledge, experience, and opportunities necessary to grasp its long-term impacts and the policies guiding its development and use (Banks 2006, 135). Meaningful access as a concept can work against restrictive processes such as digital redlining or restricting access (thus eliminating meaningful access) from certain users based on the filtering preferences of their internet access provider. Privacy policies are obtainable, but they are not truly accessible: users may be able to obtain these documents, but they don’t have a meaningful, useful sense of them.
Teachers and students need to rhetorically engage with social media privacy policies in order to learn about data and privacy: we need to understand not only what these policies say, but also what impacts they have and for whom. We also need to determine who has meaningful access and why that might be. As Angela M. Haas (2018) explains, rhetoric concerns the cultural, social, economic, and political implications of when we “negotiate” information; she specifies digital rhetoric as concerned with the “negotiation of information” when we interface with technology. Safiya Umoja Noble develops a related argument in Algorithms of Oppression: How Search Engines Reinforce Racism, suggesting internet search engine algorithms are a reflection of the values and biases of those who create them, and since algorithmic processes extend into hiring practices and mortgage lending evaluations, big-data practices nonetheless reproduce pre-existing social inequities. We need to learn about data generation and its wide-reaching, real-world impact on how we connect and interact with other people to really grasp these platforms and the policies that govern them.
By learning to critically engage with the policies that shape their digital experiences, students develop an important skill set they can use to identify the ways social media platform algorithms use data collected from users to direct their attention in ways that may be more important to the platforms than to the users themselves—working to generate clicks, repetitive usage, and thus revenue from ad impressions, rather than providing the content the user actually seeks. Students might also think about the ways these privacy policies structure the information-filtering and data-collection functions on which these platforms depend, while such policies likewise fail to protect users from the potential socio-economic and racial disparities their algorithmic infrastructures re-entrench (Gilliard and Culik 2016). To this end, it can be useful to introduce concepts like data aggregation and digital redlining, which can equip users with a better understanding for how data collection works and its far-reaching rhetorical effects. In this way, it is important to understand privacy policies as a writing genre, a typified form of writing that accomplishes a desired rhetorical action (e.g. providing social media platforms with the legal framework to maximize data usage).
As writing studies scholars Irene L. Clark and Andrea Hernandez (2011) explain, “When students acquire genre awareness, they are not only learning how to write in a particular genre. They gain insight into how a genre fulfills a rhetorical purpose” (66–67). By investigating the genre of privacy policies, students gain both transferable skills and crucial data literacy that will serve them as writers, media consumers, and, more basically, as citizens. Working within this niche genre provides insights both into the rhetoric of privacy policies per se, as well as into the use of rhetoric and data aggregation for social manipulation.
Ultimately, a multi-pronged approach is required to gain meaningful access to privacy policies. In the following section, we provide a framework with terms and questions that consider how data is collected, processed, and used. We direct attention to digital studies scholar John Cheney-Lippold’s theory of “measurable types,” the algorithmic categories created from aggregated user data, as a framework in our development of an assignment sequence that tasks students with performing two remediations—one that focuses on making information more digestible and another that centers long-term effects. The primary audience for this article is instructors who are new to digital surveillance and big-data concepts and are looking to orient themselves with theory as they create assignments about this emerging issue for their classroom.
How Is Data Collected, Processed, and Used?
Our internet activity on social media platforms creates metadata, which is another form of data web companies collect and use to track our online activity. Metadata is not the content of our posts and messages, but the information about who and/or what we interact with and how often those interactions occur. While quantitative forms of information may appear more trustworthy and objective, in actuality this seemingly neutral data has been stripped of important rhetorical context. Digital humanities scholar Johanna Drucker suggests that we refer to data as “capta,” since data is not information that perfectly represents whatever was observed as much as it is information that is “captured” with specific purposes in mind. Capta cannot fully stand in for us, but it can be used to compare us to other users who “like” and “share” similar things. Therefore, the collection of metadata is valuable because it more efficiently reveals what we do online than the meaning of our content alone. Rather than try to understand what we are communicating, computers instead process this quantified information and use it to calculate the probability that we will engage with certain media and buy certain products (van Dijck and Poell 2013, 10). So, even though data collection requires us to give up our privacy, the stakes may seem relatively low considering that we are presumably getting “free” access to the platform in exchange. Coming to terms with how data impacts our society requires understanding the ostensibly predictive capacities of data aggregation because data we consciously share is never separate from other data, including data from other users and the data we don’t realize we are sharing (e.g. location, time, etc).
Data is what powers social media platforms, but their rhetorical power comes from how data is processed into predictions about our behavior online. Our individual data does not provide accuracy when it comes to recommending new things, so data aggregation makes recommendations possible by establishing patterns “made from a population, not one person” (Cheney-Lippold 2017, 116). These “dividual” identities, as digital studies scholar Cheney-Lippold explains via digital theorist Tiziana Terranova (2004), are the algorithmic classifications of individual users based on the data generated and processed about them. Indeed, we each have our own personal preferences, but we are also interested in what captures the attention of the larger public: we care about the most recent YouTube sensation or the latest viral video. When platforms like YouTube make video recommendations they are comparing data collected from your viewing behavior to a massive cache of data aggregated from the viewing behavior of many other users.
A primary use of data is in the personalization of online experiences. Social media platforms function under the assumption that we want our online experience to be customized and that we are willing to give up our data to make that happen. Personalization may appear to be increasing our access to information because it helps us filter through the infinite content available to us, but in actuality it has to restrict what we pay attention to in order to work. This filtering can result in digital redlining, which limits the information users have access to based on the filtering preferences of internet access providers (Gilliard and Culik 2016). Internet service providers shape users’ online experiences through both privacy policies and acceptable use policies. Not unlike how banks used racist strategies to limit minority access to physical spaces, internet service providers (including universities) employ “acceptable use policies” to limit engagement with information pre-categorized as “inappropriate” and explain why various users might have very different perceptions of the same event. Practices like digital redlining reveal how personalization, albeit potentially desirable, comes at the cost of weakening the consistent, shared information we rely on to reach consensus with other people. Ultimately, we embrace data aggregation and content personalization without considering its full implications for how we connect and communicate with one another and how businesses and governments see and treat us.
Using Measurable Types to Investigate Privacy Policies
One helpful tool for analyzing how algorithms construct online experiences for different users is Cheney-Lippold’s concept of “measurable types.” Measurable types are algorithmically generated norms or “interpretations of data that stand in as digital containers of categorical meaning” (Cheney-Lippold 2017, 19). Like dividual identities, measurable types are ever-changing categories created from aggregate user data without any actual input from the user. Essentially, measurable types assign users to categories that have very real impacts on them, but from data that has been collected with very specific definitions in mind that users don’t know about. The insidiousness of measurable types is how they automatically draw associations from user behaviors without providing any opportunity for users to critique or correct the “truths” scraped from their dividual data. For instance, most users might not see any adverse effects of being labeled a “gamer”; however being classified as a “gamer” measurable type could also algorithmically align users with members of the #gamergate movement resulting in misogynist content spilling into their digital experiences. In this way, measurable types remove humans from the processes that operationalize their data into consequential algorithmic decisions made on their behalf.
Expanding the scope to amplify measurable types
The exchange of our personal information for accessing services online is among the most complex issues we must address when considering how data use is outlined in social media privacy policies. Therefore, students should build upon their initial remediation, paying attention to the far-reaching implications of practices like data aggregation which lead to data commodification. Cheney-Lippold’s measurable types help us understand how our online experiences are cultivated by the processes of big data—the information you have access to, the content you are recommended, the advertisements you are shown, and the classification of your digital footprint (Beck 2016, 70). The following classroom activities expand the scope of these conversations beyond social media privacy policies towards larger conversations concerning big data by making measurable types visible.
According to Pew Research Center, 90% of adults in the United States have access to the internet; however, this does not mean that users get the same information. What we access online is curated by algorithmic processes, thus creating variable, often inequitable experiences. Digital redlining is about the information you have access to online. As with personalization earlier, digital redlining is “not only about who has access but also about what kind of access they have, how it’s regulated, and how good it is” (Gilliard and Culik 2016). Therefore, analysis should center on the access issues that privacy policies could address to help users better understand the myriad of ways social media platforms limit access just as much as they distribute it. Since digital redlining creates different, inequitable experiences arranged according to measurable types, it is easy to observe, as Gilliard and Culik do, how this frequent practice extends beyond social media privacy policies and into our everyday lives. Even simple, familiar online actions like engaging with mainstream search engines (e.g. Google) can demonstrate how different measurable types yield different results.
The techniques used to investigate social media privacy policies are transferable to any policy about data collection. For example, Google is often criticized for mismanaging user privacy, just as social media platforms like Facebook suffer scrutiny for not protecting users’ information. To examine the cultural, economic, social, and political impacts of user privacy on Google, students can perform some basic searches while logged out of Google services and note the results that appear on the first few pages. Then, students can log into their Google accounts and compare how personalized results differ not only from previous search results, but also from the results provided to friends, family, and their peers. What information is more widely shared? What information feels more restricted and personalized? These questions help us to process how measurable types contribute to the differences in search results even among those in our own communities.
Internet advertisements are another way to see measurable types at work online. As in the previous case with Google searches, we can easily observe the differences in the advertisements shown to one user compared to others since search engine results have a considerable amount of bias built into them (Noble 2018). Moreover, visiting websites from different interest groups across the internet allows you to see how the advertisements shown on those web pages are derived from the measurable types you belong to and how you (knowingly or unknowingly) interact with the various plugins and trackers active on the sites you visit. In comparing how the advertisements from the same webpage differ among students, we can develop an awareness of how algorithmic identities differ among users and what these advertisements infer about them as a person or consumer—the composite of their measurable types. Facebook also has a publicly accessible ad database that allows anyone to view various advertisements circulating on the platform in addition to information pertaining to their cost, potential reach, and the basic demographic information of users who actually viewed them. Advertisements present various sites for analysis and are a useful place to start when determining what data must have been collected about us because they provide a window into the measurable types we are assigned.
Internet advertisers are not the only stakeholders interested in data related to our measurable types. Governments are as well, as they are invested in assessing and managing risks to national security as they define it. For instance, certain search engine queries and other otherwise mundane internet activity (keyword searches, sharing content, etc.) could be a factor in a user being placed on a no-fly list. Artist and technologist James Bridle refers to these assigned algorithmic identities as an “algorithmic citizenship,” a new form of citizenship where your allegiance and your rights are continuously “questioned, calculated, and rewritten” by algorithmic processes using the data they capture from your internet activity writ large (Bridle 2016). Algorithmic citizenship relies on users’ actions across the internet, whereas most users might reasonably assume that data collected on a social media platform would be contained and used for that platform. However, algorithmic citizenship, like citizenship to any country, comes with its own set of consequences when a citizen deviates from an established norm. Not unlike the increased social ostracism a civilian faces from their community when they break laws, or appear to break laws, a user’s privacy and access is scrutinized when they don’t conform to the behavioral expectations overseen by government surveillance agencies like the National Security Agency (NSA).
Performing advanced remediations to account for algorithm-driven processes
Discussion and Further Implications
Learning outcomes vary across classrooms, programs, and institutions, but instructors who choose to teach about data aggregation and social media privacy policies should focus on critical objectives related to genre analysis and performance, cultural and ethical (rhetorical) context, and demonstrating transferable knowledge. Focusing on each of these objectives when assessing remediations of privacy policies in the writing classroom helps students learn and master these concepts. Importantly, the magnitude of the grade matters; genre remediations of privacy policies should be among the highest, if not the highest, weighted assignments during a writing course because of the knowledge of the complex concepts and rigor of writing required to perform the work. Instructors should create and scaffold various lower-stakes assignments and activities for students to complete throughout a sequence, unit, or course which augment the aforementioned learning outcomes.
Data: Beyond the Confines of the Classroom
We recommend analyzing social media privacy policies as a way to provoke meaningful interactions between students and the digital communities to which they belong. With so many documents to analyze, students should not feel restricted to the privacy policies for mainstream social media platforms like Facebook and Twitter but should interrogate fringe platforms like Parler and emerging platforms like TikTok. We have focused on extending conversations about digital privacy, data aggregation, digital redlining, and algorithmic citizenship but there are other concepts and issues worthy of thorough investigation. For example, some students might strive to highlight the intersection of digital policing techniques and mass incarceration in the United States by analyzing the operational policies for police departments that implement digital technologies like body cams and the privacy policies for the companies they partner with (like the body cam company Axon). Others might focus on how data manipulation impacts democracy domestically and abroad by analyzing how social media platforms were used to plan the insurrection in the U.S. Capitol on January 6, 2021, and the meteoric rise of fringe “free speech” platforms like MeWe and Gab in the days following the insurrection.
 Scholars Chris Gilliard and Hugh Culik (2016) propose the concept of “digital redlining” as a social phenomenon whereby effective access to digital resources is restricted for certain populations by institutional and business policies, in a process that echoes the economic inequality enforced by mortgage banks and government authorities who denied crucial loans to Black neighborhoods throughout much of the 20th century.
 Stephanie Vie (2008), for instance, described over a decade ago a “digital divide 2.0,” whereby people’s lack of critical digital literacy denies them equitable access to digital technologies, particularly Web 2.0 tools and technologies, despite having physical access to the technologies and services themselves.
 Facebook creator Mark Zuckerberg is not lying when he says that Facebook users own their content, but he also does not clarify that what Facebook is actually interested in is your metadata.
 Aggregate data does not mean more accurate data, because data is never static: it is dynamically repurposed. This process can have disastrous results when haphazardly applied to contexts beyond the data’s original purpose. We must recognize and challenge the ways aggregate data can wrongly categorize the most vulnerable users, thereby imposing inequitable experiences online and offline.
 #gamergate was a 2014 misogynistic digital aggression campaign meant to harass women working within and researching gaming, framed by participants as a response to unethical practices in videogame journalism.
 Facebook launched its ad library (https://www.facebook.com/ads/library/) in 2019 in an effort to increase transparency around political advertisement on the platform.
 Perhaps the most recognizable example of this is the Patriot Act (passed October 26, 2001) which prescribes broad and asymmetrical surveillance power to the U.S. government. For example, Title V specifically removes obstacles for investigating terrorism which extend to digital spaces.
 This is what Estee Beck (2015) refers to as the “invisible digital identity.”
Alexander, Jonathan, and Jacqueline Rhodes. 2014. On Multimodality: New Media in Composition Studies. Urbana: Conference on College Composition and Communication/National Council of Teachers of English NCTE.
Banks, Adam Joel. 2006. Race, Rhetoric, and Technology: Searching for Higher Ground. Mahwah, New Jersey: Lawrence Erlbaum.
Beck, Estee. 2015. “The Invisible Digital Identity: Assemblages of Digital Networks.” Computers and Composition 35: 125–140.
Beck, Estee. 2016. “Who is Tracking You? A Rhetorical Framework for Evaluating Surveillance and Privacy Practices.” In Establishing and evaluating digital ethos and online credibility, edited by Moe Folk and Shawn Apostel, 66–84. Hershey, Pennsylvania: IGI Global.
Bridle, James. 2016. “Algorithmic Citizenship, Digital Statelessness.” GeoHumanities 2, no. 2: 377–81. https://doi.org/10.1080/2373566X.2016.1237858.
CBC/Radio-Canada. 2018. “Bad Algorithms Are Making Racist Decisions.” Accessed June 18, 2020. https://www.cbc.ca/radio/spark/412-1.4887497/bad-algorithms-are-making-racist-decisions-1.4887504.
Cheney-Lippold, John. 2017. We Are Data: Algorithms and the Making of Our Digital Selves. New York: New York University Press.
Clark, Irene L., and Andrea Hernandez. 2011. “Genre Awareness, Academic Argument, and Transferability.” The WAC Journal 22, no. 1, 65–78. https://doi.org/10.37514/WAC-J.2011.22.1.05.
Dijck, José van, and Thomas Poell. 2013. “Understanding Social Media Logic.” Media and Communication 1, no. 1: 2–14. https://doi.org/10.12924/mac2013.01010002.
Drucker, Johanna. 2014. Graphesis: Visual Forms of Knowledge Production. MetaLABprojects. Cambridge, Massachusetts: Harvard University Press.
Facebook. n.d. “Data policy.” Accessed March 28, 2021. https://www.facebook.com/about/privacy.
Gilliard, Christopher, and Hugh Culik. 2016. “Digital Redlining, Access, and Privacy.” Common Sense Education. Accessed June 16, 2020. https://www.commonsense.org/education/articles/digital-redlining-access-and-privacy.
Haas, Angela M. 2018. “Toward a Digital Cultural Rhetoric.” In The Routledge Handbook of Digital Writing and Rhetoric, edited by Jonathan Alexander & Jaqueline Rhodes, 412–22. New York, New York: Routledge.
Miller, Carolyn R. 2015. “Genre as Social Action (1984), Revisited 30 Years Later (2014).” Letras & Letras 31, no. 3: 56–72.
Noble, Safiya Umoja. 2018. Algorithms of Oppression: How Search Engines Reinforce Racism. New York: New York University Press.
Obar, Jonathan A., and Anne Oeldorf-Hirsch. 2020. “The Biggest Lie on the Internet: Ignoring the Privacy Policies and Terms of Service Policies of Social Networking Services.” Information, Communication & Society 23, no. 1: 128–47. https://doi.org/10.1080/1369118X.2018.1486870.
Perrin, Andrew, and Monica Anderson. 2019. “Share of US adults using social media, including Facebook, is mostly unchanged since 2018.” Pew Research Center.
Pew Research Center. 2019, June 12. “Internet/Broadband Fact Sheet.” Accessed March 20, 2021. https://www.pewresearch.org/internet/fact-sheet/internet-broadband/.
Terranova, Tiziana. 2004. Network Culture: Politics for the Information Age. London, UK; Ann Arbor, Michigan: Pluto Press.
Vie, Stephanie. 2008. “Digital Divide 2.0: ‘Generation M’ and Online Social Networking Sites in the Composition Classroom. Computers and Composition 25, no. 1: 9–23. https://doi.org/10.1016/j.compcom.2007.09.004.
We would like to thank our Journal of Interactive Technology and Pedagogy reviewers for their insightful feedback. We are particularly indebted to Estee Beck and Dominique Zino. This article would not have been possible without Estee’s mentorship and willingness to work with us throughout the revision process.