Is Recommendation Media the New Standard?

READ MORE: The End of Social Media and the Rise of Recommendation Media (Michael Mignano)

Last month, Meta changed its algorithms in what’s considered such a seismic shakeup that some commentators are calling it the end of social media.

Mark Zuckerberg’s announcement on July 21 heralded a switch from feeds essentially curated by friends and followers to a “discovery engine [that] will recommend the content we think you’ll care most about.”

In short, your social media feed on Facebook is no longer defined by followers sending you content but by Facebook itself dictating what the content it thinks relevant to you.

And Facebook is late to the game. This recommendation-based model of content distribution is already in play at arch-rival TikTok, on YouTube, and already on Facebook sister-site Instagram.

“Recommendation media [is] the new standard for content distribution on the internet,” declares Michael Mignano, co-founder of the podcasting platform Anchor, and an astute observer of what this means to creators and the future of the social network.

In a post on Medium, Mignano outlines why he thinks Facebook has altered its approach to its recommendation algorithm. One reason is to scale back the massive cost in content moderation incurred by giant platforms like Facebook.

“If a bad actor wants to share problematic content on social media, the content can spread fast because of the guaranteed distribution to the person’s network of friends,” he says.

In turn, this generates massive costs for platforms, “in the form of gigantic moderation teams made of tens of thousands of people, severe damage to platforms’ brands, and openings for competition to find more efficient means for distributing content.

“No platform has been better at exploiting the weaknesses of social media than TikTok, the platform which popularized algorithmic content distribution and gave birth to what I call, recommendation media.”

What is Recommendation Media?

As defined by Mignano, “recommendation media” content isn’t distributed to networks of connected people as the primary means of distribution. Instead, the main mechanism for the distribution of content is through “opaque, platform-defined algorithms that favor maximum attention and engagement from consumers.”

The exact type of attention these recommendations seek is always defined by the platform and often tailored specifically to the user who is consuming content.

“In social media, creators maintain programming power over what gets seen and when. But in recommendation media, the platform is always in control.”

For example, if the platform determines that someone loves movies, that person will likely see a lot of movie-related content because that’s what captures that person’s attention best. This means platforms can also decide what consumers won’t see, such as problematic or polarizing content.

“It’s ultimately up to the platform to decide what type of content gets recommended, not the social graph of the person producing the content,” Mignano says. “In contrast to social media, recommendation media is not a competition based on popularity; instead, it is a competition based on the absolute best content.”

The Impact on Creators

While, in social media, people see content from their friends regardless of the quality of the content, in recommendation media content distribution is optimized for engagement. This, says Mignano, results in very little waste in a feed, and consumption patterns are highly efficient.

In social media, creators maintain programming power over what gets seen and when. But in recommendation media, the platform is always in control.

Since a platform is in control of what content gets served to who and when, there’s no expectation that a creator’s social network is guaranteed to see their content. Therefore, platforms can also choose what not to program, and there’s little creators can do or say to counteract this.

“In recommendation media, the algorithm is understood to be the final decision maker about what gains traction and what doesn’t. This gives platforms far more leverage to hide unwanted content and therefore reduce the need for massive moderation teams.

“It’s not that these teams are no longer needed; they’re simply not needed to the same scale as in social media because distribution for certain types of content can be eliminated from a platform without changing the underlying structure of content distribution.”

In social media, creators have the programming power. As a result, “social media is effectively a “competition based on popularity, not on quality of content”. It favors the creators with the biggest followings; the bigger the following, the bigger the potential for distribution and influence.

But in recommendation media, the best content for each consumer wins. This means that consumers are always being recommended and actively served content best suited for them, creating a superior consumption experience at all times.

As Mignano points out, influencers with huge social media followings might be expected to lose some of this power with recommendation media. Kylie Jenner, with more than 360 million followers on Insta, recently posted about her displeasure with Instagram prioritizing recommended videos over photos from friends.

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Three Predictions About the Impact of Recommendation Media:

1. Explosive Growth

Since there’s no guaranteed distribution for content through friend graphs in recommendation media, creators are incentivized to seek engagement elsewhere when they’re not getting it from the platform where they created content. Where do they turn for that engagement? Other platforms. This is why you often see so much TikTok content being shared to platforms like Instagram, Twitter, and Facebook, he says. Creators are sharing content to networks where they already have audiences.

“This has a second order effect of driving massive growth to the original platform. As an example, each time content from TikTok is shared on Twitter, a user who wants to consume that content clicks through to consume it on TikTok. This not only drives engagement on TikTok, but when the content consumer isn’t already a user of TikTok, it drives new user acquisition as well.”

Imagine this dynamic occurring tens of millions of times, each time someone shares content from a recommendation media platform, and it’s easy to see how this can result in sky-high growth potential.

2. SVODs Open to the Creator Economy

In order to be able to match the exact right content with the exact right person, a platform needs an ocean of content, including extremely niche content for every person on the planet.

“The only way to have that much content is to be an open creation platform where users of the platform are able to create on the platform,” says Mignano, who expects Netflix and similar platforms to let anyone create, not just the professional studios.

3. Synthetic Media at Scale

 If recommendation media is about platforms having more control over the consumer experience, it’s not hard to imagine that platforms will ultimately seek even more efficiency by making their own content.

But to do this at the scale of an open creation platform, such as TikTok or Instagram, platforms won’t be able to rely on humans. Mignano looks instead to advances in AI-generated or synthetic media. OpenAI’s DALL-E 2 showed just what is possible today in terms of automated creation of still images. It is just a hop, skip and a jump for technologies like it to generate animated or video content at scale.

“As the cost of AI content-creation solutions come down, I expect platforms to create more synthetic media over time to create even more perfect fit content for the right users at the right time.”

Mignano signs off with this thought. Is social media gone for good? Or does this create an opportunity for a challenger to take a contrarian approach and bring social media back from the dead?

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