NAB
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.
article here
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.
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.
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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