How is the silent switch impacting your content?
As a PR adviser since 2005 and a digital strategist since 2007, I’ve seen the silent switch take over — one seamless iteration after another.
In this blog article, I explain how social media algorithms silently have been centralised into a series of global Pavlovian experiments.
As PR- and communications professionals, we should all be aware of what has happened — and what’s still happening.
Let’s dive right in:
Having Subscribers is Becoming Less and Less Important
5,55 million is a respectable audience; millions of YouTube users have actively chosen to follow McKinnon’s future updates.
Still, it’s not uncommon for McKinnon’s regular videos to acquire only a few hundred thousand views.
Don’t get me wrong: I’m not saying that a few hundred thousand views are something to sneeze at. And I’m not saying we should feel sorry for online mega-influencers, either.
I’m asking why acquiring social media subscribers, fans, and followers seems less significant today. After all, followers drive most content creators to social media in the first place.
A few hundred thousand views is a lot — but it’s not 5,55 million. And it’s reasonable to assume that 5,55 million subscribers haven’t actively decided against watching McKinnon’s video. The video just hasn’t been suggested to them.
Earning many subscribers, fans, and followers used to matter a lot, but nowadays, it matters less.
Another way of putting it: Although McKinnon has 5,55 million YouTube subscribers, it doesn’t mean his videos have that kind of reach. Something else is at play here.
The Silent Switch: The Death of Building Trust Over Time
So, why have Facebook, Instagram, LinkedIn, and many other social networks, gone through so much trouble to render subscribers, fans, and followers less valuable to content creators?
Here’s a historical explanation:
In the early days of social networks, feeds relied on network-based distribution — the more central the network node, the greater the reach.
Clean and classy. If you wanted a higher reach, you had to earn an audience by establishing trust over a more extended period.
Today, other types of selection criteria are dominating our social media feeds. Reach is rewarded not to content creator authority but to individual content pieces based on their single performance.
Wait, What? Network- vs Algorithmic Distribution
We have slowly moved from network distribution based on content creator authority to algorithmic distribution based on single content performance.
Instead of showing users content based on who they’ve decided to follow, algorithms expose audiences to single content pieces based on standardised content tests.
The YouTube algorithm won’t show Peter McKinnon’s content to all his 5,55 million subscribers.
Instead, YouTube shows a new McKinnon video to a small statistical subset of users. If the video passes the small first test, the video is then shown to a slightly larger statistical subset.
And so on.
The video could stop at 200,000 views — or climb to 2 billion views. It depends on that single piece of content, not how many followers the creator has amassed. McKinnon’s 5,55 million subscribers are not as significant in this new context.
Okay, so what?
Doesn’t a successful vlogger like McKinnon already enjoy a massive reach at his fingertips? Aren’t powerful online influencers already making enough money?
Here, we’re entering into controversial territory:
Why Tech Giants Are Switching Away
There are three main reasons social media tech giants move from network distribution to algorithmic distribution:
1. There’s a clash of desired outcomes. Network distribution doesn’t maximise the type of engagement (i.e. ad clicks and exposure) social media tech giants seek to promote.
Online audiences seek to distribute their trust sparingly and follow creators who will psychologically emphasise how they see themselves — they don’t follow content creators to increase their spending or ad exposure.
2. Advanced server-side computation is expensive. The complexity of social media algorithms is typically overstated; the media is constantly buzzing about machine learning, neural networks, and artificial intelligence.
The problem, however, is that providing advanced server-side calculations on user behaviour in real-time is … expensive.
And apart from being expensive, this type of advanced analytics is likely to be GDPR-invasive and almost impossible to get right due to the complexity of human psychology.
3. Control is power; power is money. The algorithmic distribution gives the social network more control over engagement. As a consequence, this removes control from both audiences and content creators.
This Is the Money Web
I love the idea of the social web. I love the idea of a democratic and neutral space where everyone has a voice, communities can form unbound by geographical constraints, and everyone is connected.
“Here comes everybody,” Clay Shirky wrote.
Two decades ago, the network-based Hippie Web (2005 – 2015) had a burgeoning blogosphere and lively forums where anyone discussed ideas and joined together in communities around causes and interests.
Social media optimists cited The Cluetrain Manifesto and fought for net neutrality. Sharing was caring.
The widespread notion was that information wanted to be free, an ideology spearheaded by open-source enthusiasts. We saw the advent of Napster and The Pirate Bay.
The Hippie Web was fun, but it had to end at some point.
We weren’t making enough money, frankly.
Enter the Money Web (2016 – present).
Today, successful influencers can earn a full-time living as online content creators. Not only that — they can get rich. We have a new and growing creative class of entertainers and educators.
The digital economy is growing, and we reward those who can attract people’s attention. Monetisation allows content creators to go full-time, which is impossible otherwise. Having algorithms designed to get people to click on ads is essential.
But the algorithm owners are setting the stage, not the creatives. Content must drive clicks, and creators must produce content that drives those clicks.
“So what’s the Original Sin of the Internet? Nearly all business models it supports require spying on consumers and monetising them.”
— Bob Sullivan, author and journalist
Read also: Enter The Money Web (2016 – Present)
Sensationalism and Winner-Takes-All
The Silent Switch from network-based to algorithm-based distribution changes the game for everyone. Especially for us PR professionals.
The Silent Switch
Not that long ago, social media algorithms would deliver organic reach according to a distribution that looked like this:
Today, social media algorithms deliver organic reach more like this:
It’s the Silent Switch where social networks have demoted the publisher’s authority and reputation and promoted single content performance instead.
This algorithmic change has likely had profound and severe media logic amplifications:
Classic Media Logic Effects
Media logic is hypothesised to influence the news media in the following ways: 1Nord, L., & Strömbäck, J. (2002, January). Tio dagar som skakade världen. En studie av mediernas beskrivningar av terrorattackerna mot USA och kriget i Afghanistan hösten 2001. ResearchGate; … Continue reading
Social Media Logic Effects
Our job as PR professionals is to help organisations navigate the media landscape and communicate more efficiently — especially in times of change.
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For those of us who create online content on behalf of an organisation, the effects of the silent switch can be devastating.
On the “classic” pre-money social web, where earning subscribers, fans, and followers, even not-so-spectacular corporate content found its way to reasonable audience sizes.
But on today’s Money Web, where algorithmic distribution disregards earning trust over earning clicks, sensationalism prevails in a winner-takes-all scenario.
In a way, the playing field today is levelled. As long as you’re prepared to invest heavily in the risky business of creating the type of sensational content that ultimately drives ads, that is.
Theoretically, algorithmic distribution should produce better content through competition.
But what constitutes “better?”
The problem under this new paradigm is fundamental — loyalty and trust are non-rewarded behaviours.
A Global Pavlovian Experiment on Humans
I hear many people discussing online influencers. There’s a fascinating — and often provoking! — allure to them.
The provocation is apparent:
Many successful content creators produce notable earnings by churning out sensational content.
In a sense, the phenomenon is not novel: Neil Postman warned us that we might amuse ourselves to death. And that was back in 1985!
Also, you don’t need to be an Ayn Rand ideologist to suggest that private companies strive for profits. No one forces Peter McKinnon and other video creators to play by YouTube’s rules.
But as PR professionals, we must adapt and play by these new rules. We have no other choice. Digital is way too prominent to be ignored:
Tech giants have asked their audiences to permit them to run large-scale Pavlovian experiments on us. And they have given it to them.
It’s no coincidence that terms of services are lengthy and hard-to-read disclaimer documents designed for scrolling past while their main content is dopamine-inducing click baits.
Love it, hate it. If you’re a PR professional, it doesn’t matter.
If the playbook is changing, so must we.
Those Who Crush the Algorithm
Content that attracts instantaneous momentum and makes a big splash throughout each iterative testing series will outperform other content types.
As for YouTube, this is perhaps most clearly demonstrated by the record-breaking MrBeast:
The fact that MrBeast has millions of followers on YouTube is not fundamental for his continuous success — his extravagant, over-the-top videos would go viral regardless of which account posted them. 2I’ve studied several in-depth interviews with MrBeast, and it’s clear that he’s a brilliant content creator that has been publishing videos on YouTube for a long time before his public … Continue reading
Here’s the point: Algorithmic distribution doesn’t mean content creators can’t reach a vast audience. Innovative creators, like MrBeast, will find ways to negotiate the algorithms and go viral repeatedly.
While we can appreciate MrBeast’s videos as welcome distractions, as pure dopamine-infused entertainment for the masses, we might be missing out on content created by individuals and organisations that could’ve reached their already-earned subscribers, fans, and followers.
Today, social media need user-generated content more than ever. But they don’t need mediocre corporate content. They’d much rather have our budgets.
For PR, this is rough but not unfair.
Why PR Professionals Must Adapt
Many organisations have spent serious resources attracting subscribers, fans, and followers on social media. But the value of having subscribers, fans, and followers is quickly eroding.
As PR professionals, we must be rational.
We should compare the current situation with our long-standing relationship with traditional news media. Traditional news media is powerful but imperfect, so PR professionals must manage the media. The same goes for social media.
It’s not our place as professional communicators to tell journalists how to do their jobs, just as it isn’t our place to tell tech giants or content creators how to do their business.
Our job as PR professionals is to help organisations navigate the complexity of the modern media landscape. Algorithms are a natural part of that complexity.
The Silent Switch has profound and severe implications for our society. But our job as PR professionals isn’t to fix any of these problems but to help organisations communicate efficiently in times of change.
Our job is identifying significant changes where the Silent Switch is fundamental and finding ways to improve organisational communication.
PR Resource: The 7 Graphs of Algorithms
Types of Algorithm Graphs
Search engines, social networks, and online services with large user bases all have a wealth of data to design and optimise the user experience.
This data, when viewed through the lens of the seven types of graphs, forms the skeleton key to our digital identities:
The different graphs are typically weighted differently. For instance, some media companies will allow a fair degree of social graph content, while others almost none. Changes are constantly being enforced, and the Silent Switch might be the most notable example where media companies are shifting away from the social graph.
The media company can leverage these graphs using two main approaches:
Today, profiling seems to be the dominant approach amongst media companies.
Learn more: The 7 Graphs of Algorithms: You’re Not Unknown
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|Nord, L., & Strömbäck, J. (2002, January). Tio dagar som skakade världen. En studie av mediernas beskrivningar av terrorattackerna mot USA och kriget i Afghanistan hösten 2001. ResearchGate; Styrelsen för psykologiskt försvar. https://www.researchgate.net/publication/271014624_Tio_dagar_som_skakade_varlden_En_studie_av_mediernas_beskrivningar_av_terrorattackerna_mot_USA_och_kriget_i_Afghanistan_hosten_2001
|I’ve studied several in-depth interviews with MrBeast, and it’s clear that he’s a brilliant content creator that has been publishing videos on YouTube for a long time before his public breakthrough.