Doctor SpinThe PR BlogManaging Social MediaWhy Social Media Algorithms Are Basic

Why Social Media Algorithms Are Basic

The secret: iterative distributions.

Cover photo: @jerrysilfwer

tl:dr;
With so many new technologies and social networks being R&D-driven tech giants, it's easy to assume that social media algorithms are complex works of wonder. They aren't.

Surprisingly, social media algorithms are basic.

With so many excitยญing new techยญnoยญloยญgies and social netยญworks being R&Dโ€‘driven tech giants, itโ€™s easy to assume that social media algorithms are comยญplex works of wonder.

Well, they arenโ€™t.

Here we go:

Why Social Media Algorithms Are Basic

Earning many subยญscribers, fans, and folยญlowยญers used to matยญter a lot, but since the silent switch, it has mattered less. The old trust-based sysยญtem has givยญen way to the single conยญtent algorithm, which favours sensationalism.

But, wait!

Why must it be this way? With such proยญfound advanceยญments in digitยญal techยญnoยญloยญgies (artiยญfiยญcial intelยญliยญgence, machine learnยญing, big data anaยญlysยญis, etc.), shouldยญnโ€™t social media algorithms be more sophยญistยญicยญated?

In an era of promยญinยญent social media issues (fake news, disยญinยญformยญaยญtion, electยญorยญal tamยญperยญing, etc.), shouldยญnโ€™t social netยญworks like Facebook, Instagram, TikTok, LinkedIn, and many othยญers be more interยญested in favourยญing trust?

Instead, social media algorithms are basic. There are a few reasยญons as to why:

  • Advanced servยญer-side comยญpuยญtaยญtion is expensยญive. The news media conยญstantly buzzes about machine learnยญing, neurยญal netยญworks, and artiยญfiยญcial intelยญliยญgence. The probยญlem for social netยญworks, howยญever, is that providยญing advanced servยญer-side calยญcuยญlaยญtions on user behaยญviour in real-time is expensยญive at scale.
  • Advanced servยญer-side comยญpuยญtaยญtion is still worse. Besides being cheapยญer, the single conยญtent algorithm is still superยญiยญor at presentยญing engaยญging conยญtent to users. This will undoubtedly change in the future, but weโ€™re not there yet.
  • There is a clash of desired outยญcomes. Trust-based algorithms make conยญtent creยญatยญors powerยญful, while single conยญtent-based algorithms bring back conยญtrol to the social netยญwork. This makes it easiยญer for the social netยญwork to optimยญise for ad revenue.

So, social media algorithms arenโ€™t as sophยญistยญicยญated as they could be. Theyโ€™re basic. For now, the single conยญtent algorithm reigns supreme.

The single conยญtent algorithm = when social netยญworks demote conยญtent creยญatยญor authorยญity to proยญmote single conยญtent perยญformยญance to maxยญimยญise user engageยญment for ad revenue.

Learn more: Why Social Media Algorithms Are Basic

The Silent Switch

All social media algorithms are built difยญferยญently and are conยญstantly being developed. At the same time, social media usersโ€™ behaยญviours are evolving.

Still, there was a way that social media algorithms used to behaveโ€”and there is a way that social media algorithms behave now.

This has been a funยญdaยญmentยญal but silent switch.

How Social Media Algorithms Used To Behave

For more than a decยญade, social media algorithms would delivยญer organยญic reach accordยญing to a disยญtriยญbuยญtion that looked someยญthing like this:

The Silent Switch - Doctor Spin - The PR Blog.001
Social media algorithms before the silent switch.

This disยญtriยญbuยญtion of organยญic reach enabled organยญisaยญtions to use social media desยญpite not being โ€œmedia companies.โ€

How Social Media Algorithms Behave Today

Today, after the silent shift, social media algorithms delivยญer organยญic reach more like this:

The Silent Switch - Doctor Spin - The PR Blog.002
Social media algorithms after the silent switch (click to enlarge).

The increased comยญpetยญiยญtion and sophยญistยญicยญaยญtion among conยญtent creยญatยญors parยญtially explain this new type of disยญtriยญbuยญtion. However, going virยญal is still just as posยญsible for anyone.

How does this work?

The Single Content Algorithm

How can a social netยญwork preยญdict what users will like? 

Content from a trusยญted creยญatยญor trusยญted by a large comยญmunity of folยญlowยญers used to be the leadยญing indicยญatยญor of future perยญformยญance. But today, social netยญworks have found a betยญter way to preยญdict conยญtent success.

The single conยญtent algorithm = when social netยญworks demote conยญtent creยญatยญor authorยญity to proยญmote single conยญtent perยญformยญance to maxยญimยญise user engageยญment for ad revenue.

The single conยญtent algorithm presents newly pubยญlished conยญtent to a limยญited test audiยญence. Itโ€™s not the biggest fans but rather a statยญistยญicยญal subset.

If the newly pubยญlished conยญtent tests sucยญcessยญfully, the social media algorithm pushes that conยญtent to a slightly larยญger statยญistยญicยญal subยญset. And so on.

Social Media Algorithms - Silent Shift - Doctor Spin - The PR Blog
How single conยญtent algorithms iterยญate to maxยญimยญise user engagement.

This iterยญatยญive proยญcess means that single pieces of conยญtent worthy of going virยญal will go virยญal, a) even if it takes a longer time, and b) regardยญless of the conยญtent creยญatยญorโ€™s numยญber of followers.

Learn more: The Silent Switch


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Jerry Silfwer
Jerry Silfwerhttps://doctorspin.net/
Jerry Silfwer, alias Doctor Spin, is an awarded senior adviser specialising in public relations and digital strategy. Currently CEO at Spin Factory and KIX Communication Index. Before that, he worked at Kaufmann, Whispr Group, Springtime PR, and Spotlight PR. Based in Stockholm, Sweden.

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