{"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":78,"pdf_page":88,"physical_index":88,"physical_index_base":0,"printed_page_status":"recognized","page_sha256":"03c4c7d6e7ea712596bcc9e13db6c71411767df69bde1a9799dc4c2be1a35d2e"},"chapter":"IF...THEN","content":"Steinberg explained it as an algorithm “basically looking at the edges in the graph” to decide how to rank the stories in news feed.\n\nFor Facebook, every item (i.e., status update, image, video) in the graph is considered an object. Every interaction with the object—for instance, through a “like” or a “comment”—creates an “edge.” The formula suggests that the rank of an object in the graph is the sum of the ranks of the individual edges. To paraphrase Ari Steinberg, one may think of each edge as having three main components: a time when it happened, a type of edge (a comment or like), and a user who created that edge. For each user, Facebook computes a score on how much a particular viewer tends to interact with the user—for instance, how often they comment on the user’s updates, look at their profile, or send messages. The edge weight component of the formula assigns a weight to each type of edge; for example, a comment may weigh more heavily than a like and so on. Steinberg also suggests that they train models to figure out what the appropriate weights are for diferent types of edges, hinting at the use of machine learning techniques. The last component is time decay because, as Steinberg suggests, freshness tends to be prety important (Kincaid, 2010). EdgeRank is calculated by multiplying the afinity, weight, and time decay scores for each edge. Nowadays, however, the Facebook algorithm cannot simply be broken down into three distinct elements (if it ever could). Any time a user visits Facebook, there are on average 1,500 stories for them to see. Needless to say, only a few of these stories can be shown at any given time. The task for Facebook, then, is to decide which stories are the most deserving. By looking at friend relationships, frequency of interactions, number of likes and shares a post receives, how much a user has interacted with particular types of posts in the past etc. Facebook is “capable of assigning any given Facebook post a ‘relevancy score’ specific to any given Facebook user” (Oremus,  2016).13 Once the relevancy score for each post has been determined, a sorting algorithm can then put them in the right order for the user to see.\n\nBecoming visible on the news feed—appearing in that semi-public space— depends on a set of inscribed assumptions on what constitutes relevant or newsworthy stories. How many friends comment on a certain piece of content, who posted the content, and what type of content it is (e.g., photo, video, or status update) are just some of the factors at work in determining the rank of an edge. The higher the rank, the more likely it will be that an object appears in the user’s feed (Kincaid, 2010). As explained in a Facebook patent application, the system “uses diferent types of information about users in the process of selectively providing content to users, including user profile objects, an action log, and edge objects” (Luu, 2013). User profile objects refer to all the declarative information that users have explicitly shared with the system. The action log stores diferent kinds of interactions taking place on the platform—for example, becoming a fan of a musician or adding an event. Edge objects store information about users’ connections to other nodes, including the strength of the connections between users, which the system computes into an afinity score (Luu,  2013). In other words, the algorithm is based on the assumption that users are not equally connected to their friends. Some friends “count more” than others. The friends that count more are those with whom a user interacts on a frequent basis or a more “intimate” level—say, by communicating with a friend via “chat” rather than on the “wall.” The news feed algorithm is also geared toward highlighting certain types of edges while downgrading others in which the type of interaction becomes a decisive factor. Chating with someone on “Facebook Chat” presumably counts more than “liking” his or her post. Through tests, Facebook found that, “when people see more text status updates on Facebook they write more status updates themselves”—particularly, when they were writen by friends as opposed to pages (Turitzin, 2014). As product manager for news feed ranking Chris Turitzin (2014) says, “Because of this, we showed people more text status updates in their news feed.” Clearly, the type of content that is more likely to generate an interaction or prompt users to take action is made more visible and given priority. As Facebook engineers Andrew Bosworth—who is often cited as the inventor of news feed—and Chris Cox wrote in a patent document, the system determines an overall afinity for past, present, and future content based on one or more user activities and relationships:\n\n78\n\nIF . . . THEN","previous_page":77,"next_page":79}