4: Push Versus Pull (cont'd)
Yes, I get the irony.
[This is an installment of a serial article that is being gathered here. -D ]
In the first half of this article, we talked about intentional content discovery, where the reader/watcher/listener was deliberately selecting the media she would enjoy, or at least choosing an outlet that reflected her inclinations (“I listen to alternative rock, so I exclusively tune into 99.1 FM”). Intentional content discovery on the web pretty quickly centered on Google, putting the search engine company in a position of media control historically held only by governments. As a result, content creators and businesses had to adapt to Google’s algorithms along with advertisers’ requirements, or face obscurity.
III. The Rise of Push Media: Personalized Content Delivery
If you’ve been watching, and if you read WEP you probably have, you’ve noticed that media consumption has become decidedly passive; doomscrolling, checking the feed, zombie-scrolling. The origins of this behavior are mundane: users leave websites when they hit the end of a scroll - so how about the scroll never ends?
A likable person saying what you want to hear makes the company more profit than a hardened journalist telling you what you need to know.
The endless scroll started life in social media, but even friends and friends-of-friends have a limited output. Hungry for greater retention, and using the methods of targeting content already available from our friends in ad tech, user-generated content (“social media”) companies began to cross-populate content that was relevant into our social feeds - and here comes the dopamine and amygdala-activation that spells the downfall of civilization. This algorithmic approach to keeping our attention is insanely sticky, lucrative, and relatively inexpensive. This is what I call “push” media. In my house that’s Youtube, TikTok, Instagram, Facebook, etc - in yours it may be Netflix and Twitter (X).
The algorithms work by processing really big amounts of user data, including demographic information, browsing history, and engagement patterns, to deliver highly personalized content feeds optimized for each individual user. “Engagement patterns” is a catch-all term for the explicit actions we take, like sharing or thumbs-downing, as well as more subtle things like view duration or dwell time.
I’m [mostly] resisting the urge to make social commentary here. The realities of reinforcement-learning and confirmation bias in our excitingly-reimagined 1984 have been well dissected. The implications to the business of content, however, are worth exploring:
As push media becomes more and more separate from, and indeed hostile to, pull media, the voracious algorithms are no longer hindered by traditional distinctions like “news versus entertainment” or “expert versus dilettante” or “true versus false.” The only metric is popularity.
The Pew Research Center found that 55% of adults under 30 get news from social media often or sometimes; I would posit that if you dial that back to under 25, the number is higher.
There’s a blurring of information regarding the consumption of news via video versus text, the relevant studies seeming to focus on an outmoded “social versus traditional media” division, but from available evidence seems fair to say that at least 35% of news consumption is from video sources, probably higher.
What’s most important is that the approach for the content creator is wildly different. A likable person saying what you want to hear makes the company more profit than a hardened journalist telling you what you need to know.
IV. Ok so
So what do we have now? We have the consumption and the money moving away from the editor-curated news article on the trusted site and toward the video clip on the algorithm-controlled content aggregator. We have erosion of the traditional distinctions (truthful, authoritative, exclusive) among media, replaced with almost-subconscious reactions to items whose source and veracity is utterly opaque. We have a mad scramble to chase the eyeballs and the ad revenues when we have barely caught our breath from the last one. And we have, as we speak, even newer monetization models for content: direct-to-creator subscriptions and sponsors (like Patreon and Substack1); “shoppable posts” and other direct commerce integration opportunities, and of course revenue sharing from the content aggregators themselves.
We also have some controversial results. Echo-chambers and filter bubbles have increasingly hard edges, and certain groups have proven susceptible to manipulation. The lack of transparency surrounding the algorithms themselves, and the data they collect and process, makes some people uncomfortable (we’ll cover privacy in a later chapter article ). And there is something to be said for exploring the impact of obsessive scrolling behavior on the mental health of individuals and families.
And we’re just getting started. Have you heard of AI?
Worth Every Penny is on Substack. But it’s free, because I love you, even though you never share these articles. It’s okay. Really.