{"resolved":true,"match_type":"uuid","source_type":"original_book","book_id":"1d3bd28e-c1ba-4af2-8580-60b32ef26053","title":"If...Then Algorithmic Power and Politics","author":null,"edition":null,"publisher":null,"year":null,"isbn":"2017054909","requested_by":"printed_page","book":{"source_type":"original_book","book_id":"1d3bd28e-c1ba-4af2-8580-60b32ef26053","id":"1d3bd28e-c1ba-4af2-8580-60b32ef26053","uuid":"1d3bd28e-c1ba-4af2-8580-60b32ef26053","title":"If...Then Algorithmic Power and Politics","normalized_title":"ifthenalgorithmicpowerandpolitics","author":null,"edition":null,"publisher":null,"year":null,"isbn":"2017054909","isbns":["2017054909","9780190493042","9780190493059"],"parsed":true,"ai_readable":true,"document_type":"book","page_count":211,"navigation_url":"https://workshop.closeclaw.top/ai/book/1d3bd28e-c1ba-4af2-8580-60b32ef26053","title_url":"https://workshop.closeclaw.top/ai/title/If...Then%20Algorithmic%20Power%20and%20Politics","fixed_page_url_template":"https://workshop.closeclaw.top/ai/book/1d3bd28e-c1ba-4af2-8580-60b32ef26053/page/{printed_page}","path_api_url_template":"https://workshop.closeclaw.top/api/ai/book/1d3bd28e-c1ba-4af2-8580-60b32ef26053/page/{printed_page}","read_url":"https://workshop.closeclaw.top/api/ai/read/1d3bd28e-c1ba-4af2-8580-60b32ef26053","page_url":"https://workshop.closeclaw.top/api/ai/books/1d3bd28e-c1ba-4af2-8580-60b32ef26053/page","search_url":"https://workshop.closeclaw.top/api/ai/books/1d3bd28e-c1ba-4af2-8580-60b32ef26053/search"},"page":{"printed_page":10,"pdf_page":20,"physical_index":20,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"4eafcd4086a24d5bc17ad68f7881d9b50867f29810a21a0b462d5924962093c8"},"chapter":"IF...THEN","content":"The systematic collection of data about people has afected not only the ways in which we conceive of a society, but also the ways in which we describe our neighbour. It has profoundly transformed what we choose to do, who we try to be, and what we think of ourselves. (1990: 3)\n\nIf we accept Hacking’s notion of “making up people,” it becomes necessary to interrogate the ways in which counting, quantification, and classification limits the condition of possibility for subjects on Facebook. The manner in which the categories and classifications are constituted is not arbitrary or neutral; nor are the implications incidental. Here, I want to suggest that Facebook friends are “made-up people” in the sense described by Hacking. Friends are not natural kinds but, rather, constructions that serve specific purposes in a specific historical and cultural context. As has already been pointed out, the category “friend” as used by the Facebook platform does not even require members of that category to be human beings. While all Facebook users may belong to the set of “friends,” subsets of “friends” or “users” are dynamically being made up to serve diferent purposes. As exemplified in a Facebook patent application detailing the idea of adaptive ranking of news feed: “The social networking system divides its users into diferent sets, for example, based on demographic characteristics of the users and generates one model for each set of users” (Gubin et al.,  2014). In other words, sets are powerful classification devices implying a politics in terms of demarcating who or what belongs and what does not, “what the set ‘counts’ in, what counts as members in the set” (Baki, 2015: 37). Subsets are “made up” based on demographic information, atributes of connections, frequency of interaction, and other factors afecting the features used for modeling what users can see or not see (we will return to the politics of visibility in chapter 4). If friends are not of a natural kind, what kinds of friends or, rather, subsets are there? Again, we might turn to Facebook patent applications for some tentative answers. According to a document describing a technique for optimizing user engagement, some friends seem more valuable than others (Chen et al., 2014). Particularly useful friends are called “top friends,” defined as persons having the highest measures of relatedness to a specific user (Chen et al., 2014). The determinants of relatedness are generated using a so-called coeficient module. These, in turn, depend on a variety of factors (as is the case with all of Facebook’s computations)—for example, “based on how many times or how frequently interactions occurred within the last 30 days, 60 days, 90 days, etc.” (Chen et al., 2014). Top friends are used for a number of purposes and in a variety of contexts such as:\n\n10\n\nIF . . . THEN","previous_page":9,"next_page":11}