{"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":80,"pdf_page":90,"physical_index":90,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"5c300e196a74896f6adcd0eacf32c7dbd36343d73c3c1709b8bac71c4ec727f0"},"chapter":"IF...THEN","content":"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.\n\nFacebook 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.\n\nHow, 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:\n\n80\n\nI F . . . T H E N","previous_page":79,"next_page":81}