What You Need to Know About TikTok’s Algorithm

TikTok’s algorithm does­n’t work the way most people think.

TikTok, an app for mak­ing and shar­ing short videos with oth­ers, is one of the world’s most pop­u­lar social media plat­forms. But how does its algorithm work? 

This art­icle will exam­ine TikTok’s algorithm and explore why it’s so suc­cess­ful for vir­al videos.

Here we go:

The Success of TikTok

Since its cre­ation in 2017, TikTok has become one of the world’s most pop­u­lar social media plat­forms. The formerly Musical​.ly app has over 750 mil­lion users and is espe­cially pop­u­lar with teens and young adults. ¹TikTok will have 750 mil­lion monthly users world­wide in 2022, accord­ing to Insider Intelligence’s fore­cast. It is now the third-largest social net­work of the Big Five world­wide (Facebook, Instagram, … Continue read­ing

The app, owned by Chinese com­pany ByteDance, is a video-shar­ing plat­form where users can share short videos of them­selves with oth­ers on the app. 

TikTok’s algorithm is con­stantly chan­ging and evolving to bet­ter match users with videos they will enjoy.

How Social Media Has Changed

The Silent Switch

All social media algorithms are built dif­fer­ently and are con­stantly being developed. At the same time, social media users’ beha­viours are evolving.

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

This has been a fun­da­ment­al but silent switch.

How Social Media Algorithms Used To Behave

For more than a dec­ade, social media algorithms would deliv­er organ­ic reach accord­ing to a dis­tri­bu­tion that looked some­thing like this:

This dis­tri­bu­tion of organ­ic reach enabled organ­isa­tions to use social media des­pite not being “media companies.”

How Social Media Algorithms Behave Today

Today, after the silent shift, social media algorithms deliv­er organ­ic reach more like this:

The increased com­pet­i­tion and soph­ist­ic­a­tion among con­tent cre­at­ors par­tially explain this new type of dis­tri­bu­tion. However, going vir­al is still just as pos­sible for anyone.

How does this work?

The Single Content Algorithm

How can a social net­work pre­dict what users will like? 

Content from a trus­ted cre­at­or trus­ted by a large com­munity of fol­low­ers used to be the lead­ing indic­at­or of future per­form­ance. But today, social net­works have found a bet­ter way to pre­dict con­tent success.

The single con­tent algorithm = when social net­works demote con­tent cre­at­or author­ity to pro­mote single con­tent per­form­ance to max­im­ise user engage­ment for ad revenue.

The single con­tent algorithm presents newly pub­lished con­tent to a lim­ited audi­ence sample size:

If the newly pub­lished con­tent tests suc­cess­fully, the social media algorithm pushes that con­tent to a slightly lar­ger stat­ist­ic­al sub­set. And so on.

This iter­at­ive pro­cess means that single pieces of con­tent worthy of going vir­al will go vir­al, a) even if it takes a longer time, and b) regard­less of the con­tent cre­at­or’s num­ber of followers.

Learn more: The Silent Switch

Misconceptions About TikTok’s Algorithm

Many people have mis­con­cep­tions about how TikTok’s algorithm works. They believe the algorithm is based solely on tim­ing and engagement. 

However, this is not the case. ByteDance is basing the algorithm on an iter­at­ive pro­cess that tests dif­fer­ent audi­ence sub­sets to see which videos are more likely to be popular. 

Wait. What?

Sure, the TikTok algorithm is, to an extent, designed to sur­face the most rel­ev­ant con­tent for users. It con­siders vari­ous factors, such as how often a user has inter­ac­ted with a par­tic­u­lar cre­at­or or video, the time of day, and the user­’s location.

But there’s more to the TikTok algorithm. Much more.

How People Think About TikTok’s Algorithm

Here’s how most people think that the TikTok algorithm works:

TikTok’s algorithm tracks your activ­ity on the app. Based on this data, it uses advanced machine learn­ing to build a psy­cho­lo­gic­al pro­file on you, the user, to serve you con­tent spe­cific­ally to max­im­ise your engagement.

If the TikTok algorithm builds psy­cho­lo­gic­al pro­files on each user, here’s what that type of AI com­put­ing would entail:

Whenever your “per­son­al” algorithm is fired off, your smart­phone app must call TikTok’s cent­ral­ised machine-learn­ing net­work. Then, the cent­ral net­work must pro­cess all vari­ables, decide on an out­come, and deliv­er the res­ult to your smart­phone.

From a busi­ness per­spect­ive, the pro­cess comes with two sig­ni­fic­ant draw­backs:

1. AI serv­er calls are expens­ive. ²“Sufficient algorithm per­form­ance is one of the major cost factors to con­sider because accur­ate pre­dic­tions require sev­er­al rounds of tun­ing ses­sions, which raises the cost of imple­ment­ing AI … Continue read­ing
2. Individual human beha­viour is still an AI chal­lenge. ³“Human beings are most often an integ­rated part of com­plex sys­tems. In order to describe such a sys­tem with appro­pri­ate accur­acy it is neces­sary to mod­el the human com­pon­ents with the same accur­acy … Continue read­ing

Two great examples of cur­rent com­mer­cially viable AI cap­ab­il­it­ies are Siri and Alexa. Your spoken words are uploaded to the cloud, ana­lysed using arti­fi­cial intel­li­gence, and the out­come is then delivered back to your device. Siri and Alexa are excit­ing tech­no­lo­gies but have quite lim­ited capabilities.

What if there’s a way to design a social media algorithm that’s a) faster, b) cheap­er, and c) more accurate?

How the TikTok Algorithm Works

Here’s how a faster, cheap­er, and more accur­ate social media algorithm operates:

TikTok, the video-shar­ing app, has been shad­ow­ban­ning users who post con­tent incon­sist­ent with its guidelines. Their videos do not appear in the app’s search res­ults, and their fol­low­ers do not see their posts. TikTok has not clearly explained why this is hap­pen­ing, but the com­pany is likely try­ing to avoid leg­al trouble.

Consequences for Users and Creators

You might won­der what this means for the users and con­tent creators.

To bet­ter explain, we must estab­lish a baseline, i.e. have some­thing to com­pare the out­comes of today’s social media algorithms.

Here’s how social media algorithms used to work:

Historically, social net­works used algorithms to match past user beha­viour with past con­tent cre­at­or content.

In the early days of social media, social net­works did­n’t have the tech­nic­al cap­ab­il­ity to run live beha­viours against live con­tent. So, they had to rely on his­tor­ic­al data accu­mu­lated over time on users and con­tent creators.

In sum­mary, we have dis­cussed three gen­er­al types of algorithms:

Matching based on his­tor­ic­al data = This is how most social media algorithms oper­ated in the past.

Live serv­er-side pro­cessing based on indi­vidu­al inputs = This is how many people (and the news media) think social media algorithms oper­ate.

Iterative test­ing on pre-built audi­ences = This is how most of today’s social media algorithms work.

Hopefully, we will soon see more live serv­er-side pro­cessing based on indi­vidu­al inputs. However, noth­ing indic­ates that this is how social media algorithms oper­ate today.

So, what does this mean? For users and con­tent cre­at­ors, the primary out­come is still the same:

Consistency = With his­tor­ic­al match­ing and iter­at­ive test­ing, con­sist­ency is the most prom­in­ent approach to get­ting stable and some­what pre­dict­able res­ults. Live serv­er-side pro­cessing would allow users and con­tent cre­at­ors to exper­i­ment (and reach rel­ev­ant audi­ences dynam­ic­ally), but we’re not there yet.

But most oth­er algorithmic out­comes are dif­fer­ent today:

Authority = Having a large fol­low­ing mat­ters less today because iter­at­ive test­ing will push single con­tent based on their live scores, not the con­tent cre­at­or’s mer­its. It pro­motes attract­ive thumb­nails, click­bait head­lines, second-by-second user reten­tion, etc. — and less on cre­at­or clout.

Timing and decay = Iterative tests can pro­duce a slow burn, allow­ing con­tent to go vir­al more slowly. Historical match­ing requires con­tent cre­at­ors and users to be act­ive with­in the same time inter­vals for a piece of con­tent to pre­serve its momentum.

Competition = With iter­at­ive test­ing, there are few­er short­cuts. To a great­er extent than before, every con­tent com­petes with every oth­er part. And with the trend of show­ing users more con­tent from non-fol­lowed cre­at­ors, com­bined with increas­ingly more avail­able con­tent over­all, com­pet­i­tion will only get more challenging.

Iterative Testing Pushed to the Limit

A reas­on­able asser­tion at this point is to con­clude that social media algorithms favour eas­ily digest­ible con­tent that has been dumbed down to a bare minimum. 

Today’s social media algorithms seem to favour fant­ast­ic­al and atten­tion-grabbing con­tent — almost to the point of becom­ing par­od­ies of themselves.

Well, yes.
This is sort of true.
But not quite.

TikTok is the lead­ing example of push­ing the bound­ar­ies of iter­at­ive testing.

The TikTok algorithm favours con­tent requir­ing very little of the user­’s men­tal effort. And this is, obvi­ously, suc­cess­ful for pro­du­cing enter­tain­ing (and addict­ing) user experiences.

However, Google Search is on the oth­er side of this spec­trum, with a heav­ier lean towards his­tor­ic­al match­ing. Building author­ity through traffic, links, and ana­lyt­ics data for SEO suc­cess still matters.

But this is why social net­works like Facebook, YouTube, and Instagram struggle with their algorithms. They’re posi­tioned between TikTok and Google Search.

On the one hand, they want to emu­late TikTok’s massive algorithmic suc­cess by serving eas­ily digest­ible and fant­ast­ic­al video shorts. On the oth­er hand, they also need to pre­serve their relevance:

What good is a friend app without friends? (Facebook)
What good is a long-form video app without long-form videos? (YouTube)
What good is a photo app without pho­tos? (Instagram)

How To Improve Your TikTok Content

You’re prob­ably read­ing this blog post to under­stand how to nego­ti­ate the TikTok algorithm suc­cess­fully as a con­tent creator.

Great!

When you have a deep­er under­stand­ing of how TikTok’s algorithm works, it becomes easi­er to fig­ure out the right formula:

Stay con­sist­ent and only make minor iter­at­ive tweaks to your videos based on your res­ults. Perhaps your thumb­nails aren’t grabbing atten­tion? Make tweaks and post ten videos using bet­ter thumb­nails. Did it work? If not, try anoth­er thumb­nail approach for ten videos. Otherwise, move on to the next tweak.

Repeat.

Maybe you’re now won­der­ing where to start.
I would­n’t worry too much.

See my point? The con­scious act of iter­at­ively improv­ing your con­tent out­put is more para­mount than your choice of where exactly to start.

PR Resource: The 7 Graphs of Algorithms

PR Resource: The Electronic Age

Enter: The Electronic Age

Human cul­ture is often described based on our access to pro­duc­tion tech­no­lo­gies (e.g., the Stone Age, the Bronze Age, and the Iron Age).

According to Marshall McLuhan and the Toronto School of Communication Theory, a bet­ter ana­lys­is would be to view soci­et­al devel­op­ment based on the prom­in­ence of emer­ging com­mu­nic­a­tions technologies.

McLuhan’s Four Epochs

McLuhan sug­gests divid­ing human civil­isa­tion into four epochs:

“The Gutenberg Galaxy is a land­mark book that intro­duced the concept of the glob­al vil­lage and estab­lished Marshall McLuhan as the ori­gin­al ‘media guru’, with more than 200,000 cop­ies in print.”
Source: Modern Language Review ⁵McLuhan, M. (1963). The Gutenberg galaxy: the mak­ing of typo­graph­ic man. Modern Language Review, 58, 542. https://​doi​.org/​1​0​.​2​3​0​7​/​3​7​1​9​923

As a PR pro­fes­sion­al and lin­guist, I sub­scribe to the concept of the Electronic Age. I firmly believe soci­ety is unlikely to revert to the Gutenberg Galaxy.

Like the rest of soci­ety, the PR industry must com­mit to digit­al-first, too. Mark my words: It’s all-in or bust.

Read also: The Electronic Age and the End of the Gutenberg Galaxy