When Algorithms Decide What a Movement Looks Like

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Mona Leafah
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2 Facts:

Every movement has a message. Every platform has an algorithm.
Those two things don’t always want the same outcome.

For most of history, ideas spread because people shared them with one another.

Today, many of those ideas first have to pass through recommendation systems built to maximize attention, retention, and engagement.

That changes more than reach. It changes perception.

The Algorithm Isn’t Creating Movements.

Simply put: it’s curating them.

When a social movement begins online, millions of posts compete for visibility.

The content people actually see is often the content
that performs best according to platform signals: 

  • Watch time
  • Shares
  • Comments
  • Saves
  • Rewatches
  • Predicted engagement

 

The result is a version of the movement that may emphasize its most emotional, controversial, visually striking, or simplified moments.

That version can become what people believe the movement actually is.

Visibility Becomes Influence

If one type of content consistently receives more distribution,
creators naturally make more of it.

Over time, incentives begin shaping behavior.

Creators learn what gets pushed.

Audiences learn what to expect.

Platforms learn what keeps people scrolling.

Nobody necessarily planned this.
Yet the outcome still changes public conversation.

Researchers have described this phenomenon as algorithmic amplification, where recommendation systems influence what information spreads farther and faster than other information.

While the systems are designed to optimize engagement rather than ideology, they inevitably affect which voices become prominent.

The Loudest Voices 

Social media has a strange optical illusion.

A small number of highly visible accounts can create the impression that everyone believes the same thing.

Psychologists call this the availability heuristic.

People estimate how common something is based on how easily examples come to mind.If your feed repeatedly shows the same perspective, your brain naturally assumes it’s widespread. Your feed isn’t society…

It’s a recommendation engine making predictions.

Engagement Rewards Certainty

Nuance rarely goes viral.

Simple statements travel faster than complicated ones.

Confident opinions usually outperform thoughtful uncertainty.

That doesn’t mean people suddenly become less thoughtful.

It means the platform rewards different communication styles.

Eventually… creators adapt. Some consciously. Some without realizing it.

This Affects Every Side

It’s tempting to believe algorithms only distort conversations you disagree with.

Reality is much less satisfying.

Recommendation systems influence nearly every topic:

  • Politics
  • Fitness
  • Mental health
  • Business
  • Parenting
  • Finance
  • Technology
  • Science

The mechanism stays remarkably consistent
even when the subjects change.

For Creators:

As creators, we’re constantly balancing two questions: 

Will people watch this?

Will people understand this?

Those aren’t always the same question.

Sometimes the version that performs best
isn’t the version that’s most accurate.

Sometimes slowing down actually serves your audience better
than chasing another viral moment.

That’s becoming a competitive advantage.

Mona’s Thoughts
(hi, im mona)

I’ve spent years studying social media professionally.

One thing keeps becoming clearer.

Algorithms DO NOT tell us what to think.

They heavily influence what we have the opportunity to think about.

That distinction matters.

The next time a movement suddenly feels like it’s everywhere,
it’s worth asking one extra question:

How much of what I’m seeing is public opinion… and how much is platform distribution?

Sources


Gillespie, T.
Custodians of the Internet
(Yale University Press, 2018).

Meta.
"How Feed Ranking Works."
https://transparency.meta.com/

TikTok.
"How the For You Feed Works."
https://newsroom.tiktok.com/

Pew Research Center.
Research on social media, news consumption, and public opinion.

ACM Conference on Fairness, Accountability, and Transparency (FAccT)
publications on algorithmic amplification and recommendation systems.


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