Each morning, many people begin the day by scrolling through familiar digital spaces: a Facebook news feed, a stream of YouTube recommendations, or an endless series of short videos on TikTok. The content often feels strikingly repetitive. Political views that align with one’s own appear frequently, music resembles what has already been played, and opinions that feel comfortable are reinforced again and again. This pattern is not accidental. It is the product of algorithmic systems designed to personalise what users see.
Social media platforms continuously record digital behaviour. They track which posts users pause on, which videos are watched to the end, what is liked or shared, and what is ignored. From these signals, platforms build detailed profiles that estimate individual preferences, interests and emotional triggers.
The objective is straightforward: to keep users engaged for as long as possible. The most reliable way to achieve that is by presenting content similar to what has already attracted attention.
Over time, this process can create what researchers describe as a “filter bubble”. Within this invisible boundary, users are repeatedly exposed to opinions and perspectives that mirror their own, while alternative viewpoints gradually fade from view. The effect can be subtle but powerful. When dissenting voices rarely appear, it becomes easy to assume that one’s beliefs are widely shared, even when broader public opinion may be far more divided.
The consequences are particularly visible in discussions of politics, religion and social issues. Different users can encounter sharply contrasting interpretations of the same event, each shaped by what algorithms predict will resonate most. Emotional and provocative content is often prioritised because it generates stronger reactions, while nuance and context are less likely to surface. As a result, disagreement can harden into hostility, and debate can give way to polarisation.
Algorithms have also reshaped how people consume news. Instead of actively seeking out information, many now rely on news stories that appear automatically in their feeds. These systems do not necessarily prioritise what is most significant, but what is most engaging. Stories that provoke anger, excitement or outrage are more likely to be amplified, while issues of long-term importance may receive less attention.
Escaping this dynamic entirely is difficult, but awareness can reduce its influence. Actively following a range of perspectives, seeking out established news organisations beyond social media platforms, and questioning the apparent consensus presented in personalised feeds can help counterbalance algorithmic filtering. Algorithms themselves are not inherently harmful, but uncritical reliance on them can shape not only what people see, but how they think.
As digital platforms play an ever larger role in shaping public discourse, a central question remains unresolved: to what extent are individuals forming their own views, and to what extent are those views being guided by systems designed to predict and reinforce what they already like.
Views expressed in this article are the author's own.
