THE BIAS of ALGORITHMS

Facebook’s algorithm is designed to keep users on the platform for as long as possible. It does so by analyzing a user’s interactions—likes, shares, comments, time spent on posts—and using this data to predict and prioritize content the user is most likely to engage with. This personalized feed, known as the "News Feed," is dynamically updated using machine learning models that optimize for engagement.

However, this engagement-driven model introduces what is known as algorithmic bias—a tendency for the system to favor certain types of content over others based not on quality, accuracy, or balance, but on the likelihood that the user will interact with it. This bias is not necessarily ideological by intent, but rather a byproduct of prioritizing emotionally resonant, often sensational content. Unfortunately, in the realm of political information, this often results in the amplification of extreme viewpoints, the suppression of moderating voices, and the distortion of objective truth.

One of the most visible consequences of Facebook’s algorithmic bias is the creation of echo chambers and filter bubbles. When the algorithm repeatedly shows users content that aligns with their existing beliefs and preferences, it reinforces those beliefs while filtering out opposing viewpoints. Over time, this leads to a self-reinforcing cycle in which users become more entrenched in their political ideology, less exposed to alternative perspectives, and more susceptible to partisan rhetoric.

For example, a user who frequently interacts with conservative content will see more right-leaning posts, while a liberal user will see the opposite. This siloing of information environments increases political polarization and reduces the opportunity for cross-ideological dialogue. Studies have shown that users in these filter bubbles not only receive a skewed version of reality but are also more likely to believe misinformation if it aligns with their worldview.

Facebook’s algorithms are fine-tuned to promote content that garners strong reactions—especially outrage, anger, or fear. These emotional triggers drive higher levels of engagement and are therefore rewarded by the algorithm. Political content that is extreme, divisive, or sensational often outperforms more balanced or nuanced material because it is more emotionally charged.

This tendency contributes to the amplification of extremist views and fringe ideologies. Far-right and far-left content, conspiracy theories, and propaganda can achieve viral status more easily than moderate or fact-based posts. The algorithm does not distinguish between credible journalism and misleading content; it prioritizes virality over veracity. As a result, misinformation spreads rapidly, while sober political analysis struggles to gain visibility.

Facebook whistle blower Frances Haugen testified before the U.S. Congress in 2021, revealing internal research showing that the company was aware of its role in promoting harmful content. According to these documents, Facebook’s own systems were driving users toward more polarizing material because it kept them engaged longer—a business decision with clear political ramifications.

Another layer of concern lies in Facebook’s ability to micro target users with political advertising. Political campaigns and interest groups can use the platform’s vast troves of data to target specific demographics based on age, location, interests, behavior, and even emotional state. This allows for highly customized political messaging—sometimes truthful, but often misleading or manipulative.

Micro targeting enables actors to deliver tailored messages that exploit individual biases and fears without public scrutiny. During the 2016 U.S. presidential election, for example, the now-defunct firm Cambridge Analytica harvested data from millions of Facebook users to build psychological profiles and deliver targeted political advertisements. These ads were not just designed to persuade but also to suppress turnout among opposing voters or to sow confusion.

Because these ads are not subject to the same regulatory standards as traditional political advertising, and because they are invisible to anyone outside the target audience, they evade accountability and undermine transparency in the democratic process.

Facebook’s algorithm is constantly evolving through machine learning, a process that adapts based on the behavior of users. However, this self-optimization process can also encode and perpetuate bias. If certain types of content consistently receive more engagement (e.g., nationalist rhetoric or conspiracy theories), the algorithm will learn to prioritize that content. Over time, this creates a feedback loop in which biased content becomes more prominent, not because it is more accurate or important, but because it is more engaging.

This data-driven bias is difficult to audit or correct because Facebook does not disclose the inner workings of its algorithms. The opacity of the system makes it difficult for researchers, regulators, and users to understand how political content is selected, ranked, or suppressed.

Facebook’s use of algorithmic systems has transformed it from a social networking site into a dominant force in political communication. While its algorithms are designed to maximize engagement, they also introduce significant bias—bias that shapes what users see, what they believe, and ultimately how they vote. In doing so, Facebook influences the political landscape in ways that undermine democratic ideals of informed debate, transparency, and pluralism. As society becomes increasingly reliant on digital platforms for information, it is essential to scrutinize and reform the algorithms that govern our attention—and, by extension, our beliefs.