With approximately two billion monthly active users, Facebook’s News Feed is an emerging, influential information platform in our social life. This paper briefly introduces the history of Facebook News Feed algorithm and then links the algorithm to the attention economy. Machine learning, as a powerful tool of data analysis, is utilized by Facebook to optimize its ability to attract people’s attention. By unpacking Facebook News Feed algorithm, this paper is intended to illustrate the ways in which Facebook News Feed manipulates users’ attention.
Facebook, mainly known as a social media and social networking service company, is also an important information platform for some users. This feature is embodied in Facebook News Feed, a place where you can see updates, news, and videos from the various publishers including families, friends, and media agencies. News Feed, according to Facebook, shows you the stories that are most relevant to you. Here comes a question: how does Facebook define “relevance”?
- A Brief History of Facebook News Feed Algorithm
The top secret behind “relevance” is the News Feed algorithm. It was developed by Facebook in 2006 and has gone through tens of updates over these years. In September 2006, Facebook officially launched News Feed. Since then, Facebook News Feed algorithm has undergone several changes. In 2017, the “Like” button feature was added, which made Facebook the first platform to practice an algorithm-based news feed. Prior to the addition of the “Like” button, the only way for users to interact with the other users was to comment on a status or post.
In October 2009, Facebook took a bold step. It introduced a new type of sorting order to the algorithm. It changed the whole picture of News Feed by replacing the original chronological default with a popularity-oriented algorithm. Six years later, Facebook announced other new changes to the News Feed ranking algorithm. For example, the algorithm emphasizes each user’s most 50 interactions on the network in determining what they see in their News Feed.
In 2015, Facebook expanded the sources of posts pushed to users’ News Feed, which means users would see posts from other users not on their “Friends” lists. In 2016, Facebook launched Audience Optimization tool, allowing publishers to reach a specific audience based on interests, demographics, and geographic location. In the same year, Facebook unveiled the elements that are given more weight in computation: user interest in the creator, post performance among other users, previous content performance of the creator, type of post the user prefers, and how recent the post is (Lua, 2019). In addition, when you click on a post, Facebook measures how much time you spend on the post, even if don’t respond to it. In March 2017, Facebook revamped the News Feed algorithm and decided to weigh “Reactions” more than “Likes”. In 2019, Facebook introduced a new metric called “Click-Gap” that analyzes sites and posts on Facebook compared to the internet as a whole. If a post is perceived as only popular on Facebook and nowhere else online, its reach will be limited in the News Feed.
In the beginning, the methodology behind the News Feed was the Edgerank Algorithm, an algorithm that is used to determine the order of posts. It is an adaptation of PageRank technique, a ranking algorithm used by Google’s search engine. What differentiates these two algorithms is the context they are in. Edgerank is based on social network, while PageRank is mainly used to order web pages.
Driven by machine learning, the Facebook algorithm evolves and changes as a result of an ever-increasing set of data (Introna, 2016). As of 2011, Facebook has stopped using the EdgeRank system and uses a machine learning algorithm. The machine learning algorithm takes more than 100,000 factors into account (McGee, 2013), making it more accurate in predicting what users want to see in their News Feed.
- Attention Economy
Attention economy, first theorized by Simon, focuses on how people’s limited attention is allocated among content (Simon, Deutsch & Shubik, 1971). It is a result of the rise of the attention industry that eventually came into reality in the past century. An overwhelming amount of information stimuli compete for people’s cognitive resources, giving rise to attention economy (Shapiro & Varian, 2007). Attention economy can be traced back to the nineteenth century when the first newspaper fully dependent on advertisers was created in New York. However, the business model that converts attention into revenue had not been fully realized until the twentieth century. As soon as the digital era has come, communication technologies provide everyone with a loudspeaker, allowing content to be distributed worldwide without any effort.
At present, the concept of attention economy has invaded into every facet of our lives. Consumers can be affected in many aspects, such as what to think and buy, the outcome of elections, and political discourse (Huberman, 2017). Tim Wu (2017), the author of The Attention Merchant,said in his book that the attention industry has asked and gained more and more of our waking moments in exchange for new conveniences and diversions, creating a grand bargain that has transformed our lives.
In the era of attention economy, big tech companies like Facebook take advantage of their high technology to hold people’s attention on their platform and then make money from advertisers. The engineers continuously adjust the weights of the algorithm to keep pace with Facebook’s business model and maximize revenue.
Machine Learning and Facebook News Feed Algorithm
Facebook has a large data set featuring 100 billion ratings, more than a billion users, and millions of items (Kabiljo & Ilic, 2017). With the rapid expansion of data set, machine learning is increasingly essential to Facebook algorithm because machine learning especially features the expertise of dealing with an incredible amount of data. Machine learning is a method of data analysis that aims to construct a program that fits the given data (Alpaydin, 2016). It is an important branch of artificial intelligence.
Driven by the business model, Facebook News Feed algorithm is designed and optimized to activate engagement. On Facebook, users’ engagement can be determined by many factors, such as view, click, like, comment, and share. By treating each factor as a metric, Facebook can treat the News Feed as a machine learning problem, where the inputs are various content on Facebook, and the output is the probability of an engagement event. According to an official document released by Facebook, general models are trained to determine various user and environmental factors that ultimately determine the rank order of content. When a user opens Facebook, the model generates a personalized set of the most relevant posts, images, and other content to display from thousands of publishers, as well as the best ordering of the chosen content (Hazelwood et al., 2018). The ML models implanted in prediction and ranking algorithm are illustrated in the following paragraphs.
- Ranking Algorithm
The Ranking algorithm is a process that ranks all available posts that can display on a user’s News Feed based on how likely the user will respond positively. To be specific, the algorithm calculates the ranking score of an event based on two factors: probability and value.
Probability presents the chance that users will react to the story as each event suggests; value represents the weight given to an event.
Here is an example of the ranking model. There is an 11% chance that users will click on the post and 2.2% probability that users will like it. Also, there is 0.099% chance that users will hide the story. Each event is given a weight based on its importance. A weighted ranking score will be generated as the final ranking score, which is 0.2277. The posts in the inventory will be ranked according to their final score.
All the probability column is calculated by machine learning models and the value column is based on the user study and product decision. Since interaction is more valued in the algorithm, the weights of “Like” and “Comment” are much higher than “Click”, while the weight of “Hide” is negative.
In 2016, Facebook stated the “core values” it uses when determining what shows up in a user’s feed. Facebook emphasized that the posts from friends and family will be on the top of one’s News Feed, followed by the posts that “inform” and posts that “entertain.” Other core values include posts that represent all ideas and posts with “authentic communication.” Facebook also claimed that it emphasized the user’s ability to hide posts, and the user’s ability to prioritize their own feed with the “See First” function.
- Decision Tree Models
Decision trees are commonly used as a predictive model, mapping possible outcomes of a series of related choices. The decision tree is one of the oldest methods in machine learning. Also, it is one of the most common predictive modeling approaches used in machine learning due to its non-linearity and fast evaluation (Ilic & Kuvshynov, 2017).
Decision trees are a type of supervised machine learning in which the input and the corresponding output are previously labelled. The decision tree is used to find the most similar training instances by a sequence of tests on different input attributes. It is a flowchart-like structure in which each decision node represents a class label, each branch represents the outcome that leads to those class labels (Alpaydin, 2016). Each decision node applies a splitting test on an attribute and one of the branches is taken based on the outcome. The paths from root to leaf represent classification rules. The leaves represent the decisions or final outcomes. When the flow reaches leaves, the search stops. Through a set of procedures, we can find the most similar training instances and get the probability of each instance.
Decision tree models are based on the idea that a user’s future behavior is generally consistent with his or her past actions. It is a powerful model in predicting and it is currently implanted in Facebook News Feed Algorithm. The figure above shows an example of a simple decision tree that generates the probability of clicking on a notification. This decision tree has the following attributes: 1) the number of clicks on notifications from a specific user today; 2) the number of likes that the story from the notification has; 3) the total number of notification clicks from this specific user. The input data goes through the decision nodes and the values of data input are checked according to the parameters. Eventually, we can get the probability of clicking on a notification. With the decision tree, we can predict the probability of the user clicking on the other notifications in the future. Decision trees can also be used in predicting the probability of clicking on ads in the News Feed.
- Collaborative Filtering
Collaborative filtering (CF) is a recommendation system that helps people discover items that are most relevant to them. It is based on the idea that the best recommendations come from people who share similar interests (Kabiljo& Ilic, 2017). Collaborative filtering is commonly implanted in e-commerce applications and online news aggregators. Facebook has a Collaborative Filtering recommender system that is used in many areas of the site.
There are three types of CF: User-based collaborative filtering, Item-based collaborative filtering, and Model-based collaborative filtering. The difference between these three CF recommendation systems is nuanced. User-based collaborative filtering firstly finds neighbors who share similar interests with the targeted user by comparing the posts they liked, then recommends posts to the targeted user based on the preferences of the neighbors. Item-based collaborative filtering calculates the similarity score of two posts based on all users’ reaction to them, and then recommend to the targeted user the posts that fit his preference. Model-based collaborative filtering trains a model with input data that is extracted from targeted user’s prior reactions and, later, predicts his or her future actions with the built model. Collaborative filtering-based recommendation is different from Content-based Recommendation because Collaborative Filtering recommendation systems connect a post with those who liked the post, instead of just focusing on the post itself.
In the example, Facebook uses Apache Giraph to analyze the social graph formed by users and their connections. Apache Giraph is an iterative graph processing system built for big data. It is able to break down the complicated structure and find the most relevant posts to the targeted user based on the results generated by Collaborative Filtering.
Attention manipulation is a strategic action to influence how a user allocates his or her attention. When a consumer’s attention is limited, her ultimate purchasing decisions may hinge on what she pays attention to, which in turn incentivizes firms to engage in attention manipulation (Persson, 2017). In order to achieve the goal of converting users’ attention to revenue, News Feed changes the way in which people receive information, which also influences what they see about the world around them. Algorithms are not just abstract computational processes, they also have the power to enact material realities by shaping social life to various degrees (Beer, 2013; Kitchin & Dodge, 2011).
As a result of attention economy, News Feed brings problems to users in three aspects. First of all, this manipulation is without consent. They didn’t ask users whether they were willing to participate in it. Users are automatically giving their consent to these kinds of attention manipulation when they sign up for Facebook and click the button “I agree on the user agreement”. Facebook does not provide users the option of not using News Feed algorithm, instead, it assumes that users are comfortable with it. The lack of consent deprives a rightful choice of users.
The second problem is the loss of agency. People are getting accustomed to being fed with the recommended messages, which is a big concern. The News Feed algorithm is trying to make decisions on users’ behalf. After interviewing 25 Facebook users, researchers found that several participants expressed their unease and discomfort about their perception of Facebook algorithm controlling what they see and do not get to say (Bucher, 2017). The feeling of being controlled always comes along with the loss of agency. The algorithm, instead of users themselves, decides what information they receive on Facebook, which might jeopardize people’s ability to think about the most relevant information they need.
Another problem resulting from the News Feed as an approach of attention manipulation is emotional contagion. Big tech companies like Facebook are trying to lead people to experience emotions without their awareness. According to a new study by social scientists at Cornell, the University of California, San Francisco (UCSF), and Facebook, emotions can spread among users of online social networks (Segelken & Shackford, 2014). People with lesser judgment are more vulnerable to these kinds of attention manipulation.
Since the inception of Facebook News Feed algorithm, it has undergone great changes to be consistent with Facebook’s business model. Although Facebook opened the curtains for the algorithm, it is still difficult for ordinary users to learn about hundred thousands of weights in the algorithm. Due to the incredible scale of Facebook’s data set, the algorithm nowadays is built with machine learning models, which is featured by the ability to handle big data.Unpacking the Ranking algorithm, Decision tree model, and Collaborative filtering helps to get deeper into how the algorithms work.
Facebook News Feed has become the world’s biggest information distribution platform. By now, there have been a variety of types of content on News Feed: text, photo, video, event, and groups. The diversity requires more complexity of the ranking algorithm; for outsiders, it brings a greater challenge to debalckbox the algorithm.
The real problem is that there is much less accessible information concerning the parameters. As the News Feed algorithm starts to supplant traditional editorial story selection, it is hard for users to get into its story curation system that is parallel to our knowledge of the principles that the editors used to refer to (DeVito, 2017). Education that teaches how to get rid of attention manipulation has not been in place for now.
But it is time to act. As Carl Newport (2019) suggests, you should transform the way you think about the different flavors of one-click approval indicators that populate the social media universe. The first rule is to learn about how it works and never fall into the trap.
Alpaydin, E. (2016). Machine learning. Cambridge, Massachusetts ; London, England: The MIT Press.
DeVito, M. A. (2017). From editors to algorithms.Digital Journalism, 5(6), 753-773. doi:10.1080/21670811.2016.1178592
Beer, D. (2013). Popular culture and new media. Basingstoke [u.a.]: Palgrave Macmillan.
Bucher, T. (2017). The algorithmic imaginary: Exploring the ordinary affects of facebook algorithms.Information, Communication & Society, 20(1), 30-44. doi:10.1080/1369118X.2016.1154086
Facebook newsfeed algorithm history. Retrieved from https://wallaroomedia.com/facebook-newsfeed-algorithm-history/
Hazelwood, K., Bird, S., Brooks, D., Chintala, S., Diril, U., Dzhulgakov, D., . . . Wang, X. (Feb 2018). Applied machine learning at facebook: A datacenter infrastructure perspective. Paper presented at the 620-629. doi:10.1109/HPCA.2018.00059 Retrieved from https://ieeexplore.ieee.org/document/8327042
Huberman, B. (2017). Big data and the attention economy.Ubiquity, 2017(December), 1-7. doi:10.1145/3158337
Ilic, A., & Kuvshynov, O. (2017). Evaluating boosted decision trees for billions of users. Retrieved from https://code.fb.com/ml-applications/evaluating-boosted-decision-trees-for-billions-of-users/
Introna, L. D. (2016). Algorithms, governance, and governmentality.Science, Technology, & Human Values, 41(1), 17-49. doi:10.1177/0162243915587360
Kabiljo, M., & Ilic, A. (2015).Recommending items to more than a billion people. Retrieved from https://code.fb.com/core-data/recommending-items-to-more-than-a-billion-people/
Kitchin, R., & Dodge, M. (2014). Code/space(1. MIT Press paperback edition ed.). Cambridge, Mass. [u.a.]: MIT Press.
Lua, A.Decoding the facebook algorithm: A fully up-to-date list of the algorithm factors and changes. Retrieved from https://buffer.com/library/facebook-news-feed-algorithm
McGee, M. (2013). EdgeRank is dead: Facebook’s news feed algorithm now has close to 100K weight factors. Retrieved from https://marketingland.com/edgerank-is-dead-facebooks-news-feed-algorithm-now-has-close-to-100k-weight-factors-55908
Newport, C. (2019). Digital minimalism. UK ; USA ; Canada ; Australia ; India ; New Zealand ; South Africa: Penguin Business.
Persson, P. (2017). Attention manipulation and information overload. Cambridge, MA: National Bureau of Economic Research.
Segelken, H. R., & Shackford, S. (2014). News feed:‘Emotional contagion sweeps Facebook. Cornell Chronical. Retrieved fromhttp://news.cornell.edu/stories/2014/06/news-feed-emotional-contagion-sweeps-facebook
Shapiro, C., & Varian, H. R. (2007). Information rules(Nachdr. ed.). Boston, Mass: Harvard Business School Press.
Simon, H. A., Deutsch, K. W., & Shubik, M. (1971). Designing organizations for an information-rich world., 37-72. Retrieved from http://www.econis.eu/PPNSET?PPN=487583434
Yves Citton, & Barnaby Norman. (2017). The ecology of attention(English edition. ed.). GB: Polity. Retrieved from https://ebookcentral.proquest.com/lib/[SITE_ID]/detail.action?docID=4788164
Wu, T. (2017). The attention merchant