{"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":168,"pdf_page":178,"physical_index":178,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"d4f16bcb1ced585171829582045de9cb9451547cff1236dea7337de959df171e"},"chapter":"IF...THEN","content":"13. As exemplified in a Slate article about the Facebook news feed: “It doesn’t just predict whether you’ll actually hit the like buton on a post based on your past behavior. It also predicts whether you’ll click, comment, share, or hide it, or even mark it as spam. It will predict each of these outcomes, and others, with a certain degree of confidence, then combine them all to produce a single relevancy score that’s specific to both you and that post” (Oremus, 2016).\n\n14. Kairos refers to the ancient Greek notion of the opportune or the right time to do or say something. For more details on the concept of kairos, see, for example, Boer (2013), or Smith (1969).\n\n15. For a detailed description on how trends are defined and algorithmically processed in Facebook, see Li et al. (2013).\n\n16. Daniel Ek, who is the founder of the music streaming site Spotify, which entered into a close partnership with Facebook in 2011, tellingly suggests: “We’re not in the music space—we’re in the moment space” (Seabrook, 2014).\n\n17. These are just some of the changes made to the news feed as documented by the Facebook News Feed FYI blog (htps://newsroom.fb.com/news/category/news-feed-fyi): May 2017, reducing the amount of posts and ads that link to “low-quality web page experiences” by using artificial intelligence to “identify those [web pages] that contain litle substantive content and have a large number of disruptive, shocking or malicious ads.” January 2017, emphasizing “authentic” content by looking at “signals personal to you, such as how close you are to the person or Page posting.” August 2016, reducing the ranking of updates that classify as having clickbait-like headlines. June 2016, posts from friends and family to get top priority followed by posts that “inform” and posts that “entertain.” March 2016, prioritizing live videos. June 2015, tweaking the algorithm to take more account of user actions on videos such as turning on sound or making the video full screen as indications of heightened interest. June 2015, taking into account time spent on stories as indicated by users scrolling behavior. April 2015, making posts from friends rank even higher and relaxing previous rules about not showing multiple posts in a row from the same source. September 2014, prioritizing more timely stories, for example as indicated by two people talking about the same issues (i.e., a TV series or a football game). Timeliness, moreover, is indicated by when people choose to like or comment on a post. If more people like a post right after it was published but not a few hours later, the algorithm takes it as an indication that it was more relevant at the time it was posted. What’s interesting to note about the blog posts documenting changes is that the posts from 2017 more readily mention artificial intelligence as part of the procedures and techniques for making the news feed more relevant.\n\n18. See chapter 3 for a discussion of the possibilitities and challenges of applying a reverse engineering ethos to algorithms.\n\n19. Facebook alternately calls these types of stories aggregate or cluster stories. See Luu (2013) for a description of cluster stories.\n\n20. The most recent feed used to have a counter next to it indicating the number of new stories that had been posted and published by a user’s Facebook connections since the last time they had checked the feed. The counter only went as far as 300 new posts, so any number above the limit would just be indicated as “+300.” Usually, my most recent feed would reach this limit only after one or two days of not checking.\n\n21. In September 2011, I did this kind of comparison between my “top news” feed before and after checking the most recent feed a couple of times; and, every time, there was a considerable change in the stories displayed between the first and second time I checked.\n\nChapter 5\n\n1. @Desareon, February 8, 2016.\n\n2. @doughj, February 7, 2016.\n\n168\n\nNOTES TO PAGES 78–93","previous_page":167,"next_page":169}