LONDON: A video recorded on a mobile phone can reach millions of people within hours, while a carefully produced news report may struggle to attract a few hundred views. Neither outcome necessarily reflects the importance, accuracy or quality of the content.
So who decides what goes viral?
The answer lies largely in recommendation algorithms — automated systems that determine which posts appear on social media feeds, who sees them and how widely they are distributed.
Yet algorithms do not operate independently. They are designed by technology companies, respond to audience behaviour and operate within rules established by the platforms themselves.
How Do Algorithms Decide What You See?
Platforms including TikTok, Instagram and YouTube use recommendation systems to predict which content users are most likely to engage with.
TikTok says its For You feed considers interactions such as likes, shares, comments and viewing behaviour, alongside information about videos, including captions and hashtags.
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Watching a video until the end can signal stronger interest than simply scrolling past it, according to TikTok’s explanation of its recommendation system.
Instagram’s parent company, Meta, similarly explains that its ranking systems predict how likely users are to interact with posts, including liking, commenting or saving them.
YouTube considers viewing and search histories, subscriptions, likes and user satisfaction surveys when recommending videos.
These systems do not follow one universal formula. Each platform applies different signals and continually adjusts how recommendations work.
Why Do Some Posts Suddenly Go Viral?
A post can spread rapidly when recommendation systems identify signals suggesting that additional audiences may find it engaging.
Videos attracting sustained attention, repeated viewing or widespread sharing can gain greater visibility.
However, high engagement does not guarantee viral success, and platforms do not publicly disclose every factor determining distribution.
Follower numbers are not always decisive either.
TikTok states that an account’s follower count is not a direct ranking factor in its For You recommendation system, although established audiences can help creators attract initial attention.
This creates opportunities for relatively unknown individuals to reach large audiences.
It also makes online popularity difficult to predict, even for experienced creators and major media organisations.
Are Social Media Companies Controlling Public Attention?
Technology companies exercise considerable influence because they design the systems that rank, recommend and restrict content.
They determine which signals matter, establish moderation policies and decide which material qualifies for wider distribution.
Meta, for example, says its Instagram ranking system can reduce the prominence of certain content identified as misinformation.
TikTok also says some material may be excluded from recommendations even when it has not been removed from the platform.
Such decisions can affect public visibility, although they do not mean companies manually select every viral post.
The distinction is important: algorithms automate individual recommendations, but the companies operating them establish the broader rules.
What Happens When News Competes for Attention?
The consequences extend beyond entertainment.
The Reuters Institute’s Digital News Report 2026 found that 54 per cent of respondents across 48 markets accessed news through social media and video networks, compared with 51 per cent through news organisations’ websites and applications.
The findings underline the growing influence of platforms over how audiences encounter public affairs.
Another Reuters Institute finding showed that 27 per cent of respondents obtained information from news-focused creators each week.
As audiences increasingly discover journalism through social feeds, news organisations compete for visibility alongside influencers, entertainment accounts and political commentators.
A report’s public importance does not necessarily determine its reach.
This raises concerns that emotionally engaging material may attract attention more easily than complex reporting requiring context and explanation.
Can Algorithms Spread Misinformation?
Recommendation systems can help misleading claims reach large audiences, particularly when users engage with sensational or emotionally charged material.
However, their effects vary between platforms, and engagement alone does not establish that a system promotes misinformation.
The Reuters Institute’s 2025 Digital News Report found that 47 per cent of respondents across surveyed markets identified online influencers and personalities as a major potential source of false or misleading information.
Platforms have introduced moderation systems, recommendation restrictions and user controls, although questions remain about their effectiveness and transparency.
Can Users Influence What Goes Viral?
Users are not powerless.
Their viewing choices, shares, comments and feedback help shape recommendations. YouTube, for example, allows users to indicate that they are not interested in particular videos or channels.
But individual choices operate within systems whose design remains largely controlled by technology companies.
Ultimately, virality is not decided by a single person, algorithm or audience. It emerges from their interaction.
The deeper question is whether systems designed to capture attention can consistently serve the public’s need for accurate, diverse and meaningful information.
As social media increasingly shapes public debate, understanding who controls visibility may become as important as understanding the stories themselves.
