by Grace Sargent, April 9, 2024

The intertwined relationship between social media platforms and the technologies they rely on is often overlooked by its users, despite their relevance. Within the past decade, social media usage has increased immensely, and it has become second nature for many people. We have grown accustomed to logging onto our favorite websites and immediately being greeted with content that is tailored to our hobbies and interests, without truly questioning the work that led up to that satisfying experience. Thus, I would like to delve into the evolution behind Facebook and its machine learning—namely, its algorithms. I will then expand on the implications of this relation regarding both Facebook’s users and the development of the platform itself, exploring the question of its greater impact.

Though the two were not always intimately connected, the current, continued success of Facebook is largely due to the advancement of its machine learning; in other words, the two have become inextricably linked. At their inception, many social media platforms were created with pleasurability and collaboration in mind. Indeed, their significance has simply been an extension of the main purpose of where they are typically housed (our phones): to bring us together. When we think of our phones, we think of how we are afforded the opportunity “to stay connected with loved ones and friends no matter where they are” (Liberty). Facebook’s creation was then seen as an innovative way to do exactly that—to stay in touch. After launching in February 2004, the platform allowed users to view their profile page and make posts as they pleased. To check in on your friends, you were required to manually search their profiles. It wasn’t until 2006 when “News Feed” was introduced that users could enjoy a homepage that included the updates and pictures posted by their friends (Wallaroo). Facebook’s true incorporation of machine learning, however, was in November of 2007 when users were able to “like” posts, resulting in a News Feed that was more likely to display content the user would interact with (Wallaroo). Machine learning is defined as “a subset of artificial intelligence (AI) that focuses on the development of computer algorithms that improve automatically through experience and by the use of data” (Crabtree). From there, the power of Facebook’s algorithms took off, and so did how the platform facilitated engagement from its users. 

Machine learning and the algorithms it employs have advanced alongside the social media platforms that use them, allowing their accuracy to improve tenfold. As aforementioned, the preliminary stages of Facebook’s algorithms were solely dependent on the content a user would “like” or “dislike.” Currently, however, its algorithms consider multiple factors to formulate content that is tailored to the interests of each specific user. Not only do the algorithms take note of what users “like,” but also how frequently they will interact with certain types of accounts, and even at what times they do so (Zote). Furthermore, it takes into account large amounts of data that span more than individual incidents; it will take note of your overall behavior, and predict whether you would enjoy seeing content that is similar to what you have already expressed an interest in (Adisa). It is nearly impossible for users to regularly use Facebook without inadvertently creating a space for themselves where they are exposed to a specific genre of content. Thus, as its algorithms (machine learning) widened its capabilities to take in multiple variables (such as what type of content they engage with, which accounts they check frequently, etc.), Facebook’s curation has also become more accurate. This is beneficial to Facebook itself as it increases the likelihood of satisfied users who will return to their platform and further its notoriety, though it is not entirely positive for users in the long run. 

Facebook has evolved past being a website for friendly connections and has established itself as a space for the widespread sharing of important information that can educate the masses. This was seen prominently during the beginning stages of the COVID-19 pandemic when there was an urgent need for medical knowledge. When the world was experiencing unprecedented times, knowing how to take necessary precautions was incredibly important, and many people turned to Facebook as their source. In 2021, Facebook even partnered with the World Health Organization (WHO) to provide vulnerable communities with the means of accessing important health information. Facebook implemented their Discover mobile web, as well as Free Basics, which gave people “health information, job sites, communication tools, education resources, and local government information without data charges” (World Health Organization). This demonstrates how Facebook has managed to merge pleasure with practicality for its users who are navigating challenges while depending on their online platforms. 

Another way users have utilized Facebook is its ability to provide people with the opportunity for advocacy. Many people consider Facebook to be “pivotal for the innovation of online social networking platforms as the limitless functions of these platforms contribute to the construction of social change” (Kennedy). One of the best ways Facebook allows social activism to thrive is through its “Groups” function. Groups are where people gather with similar interests or ideas, and it allows them to unite, communicate, and collaborate promptly. Those same ideas can then be brought to larger audiences on the site, where they can gain further traction and attention to fulfill their ultimate goals of bringing about change. In other words, this social media site is often viewed—and used—as a legitimate means of mass communication.

Taking these purposes into account, it is interesting to then consider how some of Facebook’s demographics have changed over the years. Years ago, in 2012, Facebook was said to be especially appealing to women who were aged 18-29 (Duggan and Brenner). A reported 57% of its users were female, making 43% of them male (Alexander). Statistics for 2023, however, illustrate a change that has occurred over time. Across all age groups except for 65 and older, there are more male users than female users (Dixon). Additionally, the largest audience group was found to be men ranging from 25-34 years old (Dixon). Therefore, the kinds of users that Facebook attracts have changed along with its general purposes. Facebook is typically not regarded as a go-to social media site for younger audiences looking for pure entertainment—rather, platforms like Instagram and TikTok are most appealing. 

When considering the information that has been gathered and analyzed, I think it’s important to recognize the ongoing trends Facebook has experienced. As I previously explained, Facebook was first and most popular among college-aged adults, and it was used very casually. People enjoyed sharing small parts of their lives such as what they were up to or who they were dating. Since then, however, it has shifted away from serving purely entertainment purposes and instead become a popular place to share news and pressing information. While I have laid out how this has been beneficial to educating large audiences, I think it is also worth noting the potential downsides; namely, the phenomenon of filter bubbles. I established that Facebook’s algorithms are greatly developed to put content on your feed that you are likely to enjoy, however, their ability to prevent you from seeing a diverse range of posts can create these filter bubbles. A filter bubble effectively isolates users from information and perspectives they haven’t yet expressed an interest in, cutting them off from information that could be important (GCF Global). The most dangerous aspect of a filter bubble is that the user will often not realize they are in one, and then not take any action to broaden their perspective since they feel they are being adequately informed in the first place. 

In other words, I am cautious about saying Facebook should be prioritized as a place for information. Rather, I would argue that the site will continue to foster digital spaces that isolate groups of people which could potentially further divide our society. This is largely due to the complex machine learning (algorithms) employed by Facebook—as they become more advanced and consequently adept at curating feeds that are guaranteed to satisfy audiences, users will have a harder time breaking free from the restraints they set. If individuals of the general public each remain in their filter bubble, how will we become properly exposed to a variety of topics and information? Given this, I think a good practice users should keep in mind is to actively seek out opinions that differ from their own to ensure they understand all sides of an issue. It is also important for users to be aware of the limitations of a singular social media site like Facebook; it is always best to get information from multiple sources. 

Works Cited

Adisa, Dorcas. “How to Rise above Social Media Algorithms.” Sprout Social, 30 Oct. 2023, sproutsocial.com/insights/social-media-algorithms/. 

Alexander, Anson. “Facebook User Statistics 2012 [Infographic].” AnsonAlex.Com, 31 Dec. 2020, ansonalex.com/infographics/facebook-user-statistics-2012-infographic/#:~:text=57%25%20of%20Facebook%20user%20are,on%20the%20site%20per%20visit. 

Crabtree, Matt. “What Is Machine Learning? Definition, Types, Tools & More.” DataCamp, DataCamp, 19 July 2023, www.datacamp.com/blog/what-is-machine-learning. 

“Digital Media Literacy: How Filter Bubbles Isolate You.” GCFGlobal.Org, GCFGlobal Learning, edu.gcfglobal.org/en/digital-media-literacy/how-filter-bubbles-isolate-you/1/. Accessed 18 Feb. 2024. 

Dixon, Stacy Jo. “Global Facebook User Age & Gender Distribution 2023.” Statista, 29 Aug. 2023, www.statista.com/statistics/376128/facebook-global-user-age-distribution/. 

Duggan, Maeve. “The Demographics of Social Media Users – 2012.” Pew Research Center: Internet, Science & Tech, Pew Research Center, 14 Feb. 2013, www.pewresearch.org/internet/2013/02/14/the-demographics-of-social-media-users-2012/. 

“Facebook News Feed Algorithm History: 2023 Update.” Wallaroo Media, 9 Mar. 2023, wallaroomedia.com/facebook-newsfeed-algorithm-history/. 

“How Mobile Phones Are Making Our Lives Convenient.” Liberty Title, 6 Mar. 2023, libtitle.com/how-mobile-phones-are-making-our-lives-convenient/#:~:text=One%20of%20the%20biggest%20benefits,to%20anyone%20in%20the%20world. 

Kennedy, Che-Anne. “Facebook as a Construct of Social Change and Collaboration for Activists., Debating Communities and Networks XII.” Debating Communities and Networks XII, 27 Apr. 2021, networkconference.netstudies.org/2021/2021/04/27/facebook-as-a-construct-of-social-change-and-collaboration-for-activists/. 

“Who, Facebook and Praekelt.Org Provide Critical Mobile Access to COVID-19 Information for Vulnerable Communities.” World Health Organization, World Health Organization, 11 Aug. 2021, www.who.int/news/item/11-08-2021-who-facebook-and-praekelt.org-provide-critical-mobile-access-to-covid-19-information-for-vulnerable-communities. 

Zote, Jacqueline. “How the Facebook Algorithm Works and Ways Your Brand Can Outsmart It.” Sprout Social, 10 Jan. 2024, sproutsocial.com/insights/facebook-algorithm/.