Social media platforms have become a major source of news and information, especially for young people. Content on platforms such as Instagram, TikTok, YouTube and X is not shown randomly. Instead, algorithms decide what users see based on their past behavior. While these systems are designed to increase engagement, research shows that they can also influence opinions, reinforce existing beliefs and contribute to the spread of misinformation.
Social media algorithms analyze user data such as likes, shares, watch time and interactions with specific accounts. Based on this information, they predict what content a user is most likely to engage with and prioritize similar posts. This process creates a highly personalized feed, meaning that we can see only posts that does not oppose our opinion.
Algorithmic personalization can lead to echo chambers and filter bubbles, where users are mainly exposed to information that confirms their existing beliefs. When opposing viewpoints are shown less frequently, users may develop a distorted sense of reality and believe that their views are more widely shared than they actually are. This can reduce critical thinking and increase polarization.
Additionally, emotionally charged content, such as posts that provoke anger or fear, spreads faster online than neutral or factual information. Because algorithms prioritize engagement rather than accuracy, sensational or misleading content is often amplified. This creates an environment where misinformation can spread rapidly, especially when it captures strong emotional reactions.
Social media algorithms do not distinguish between true and false information. As a result, false content can spread widely if it attracts attention. This problem has intensified with the AI-generated images, videos and deepfakes, which can make misinformation appear highly realistic. Repeated exposure to such content can make false claims seem familiar and trustworthy. Positive side of this thing is that we can see it changing, as for example X introduced a way to correct misinformation using additional comments to posts, which are being approved by the community vote. On the other hand, as the community votes, closed communities can upvote for additional comment which does not have to be truthful.
Repeated exposure to similar information strengthens beliefs and makes them harder to challenge. Algorithm-driven feeds can therefore reinforce existing opinions, increase distrust in reliable sources and contribute to social division. These effects are particularly significant for users who rely on social media as their primary source of news.
Social media algorithms play a powerful role in shaping what people see and believe online. While they improve personalization and engagement, they also create risks by limiting exposure to diverse viewpoints and amplifying misinformation. Understanding how algorithms work and developing strong media literacy skills are essential for navigating the digital world responsibly.
Sources
- Pariser, E. (2011). The Filter Bubble: What the Internet Is Hiding from You. Penguin Press.
- Vosoughi, S., Roy, D., & Aral, S. (2018). "The spread of true and false news online." Science, 359(6380), 1146–1151.
- Pew Research Center. (2020). News Use Across Social Media Platforms.
- UNESCO. (2021). Media and Information Literacy: Curriculum for Educators and Learners.