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Social Media Algorithms and How They Rule Our Lives

A social media algorithm is not your friend—and it must be managed.

Cover photo: @jerrysilfwer


We all know how social media algorithms work, right?

Most people think they know how social media algorithms work, but they don’t. This art­icle will shed some light on how algorithms use iter­at­ive test­ing — not soph­ist­ic­ated technology.

As a digit­al strategist, I’ve closely mon­itored social media algorithms for two dec­ades. I’ve learnt that algorithms aren’t your friends — and we must man­age them.

Here we go:

We Need Sorting and Labelling

You inter­act with social media, and the plat­form own­er col­lects your user data to serve you more con­tent to keep you engaged and thus increase your expos­ure to third-party advertising. 

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 

Order is neces­sary, of course, since there’s a lot of con­tent to struc­ture:

Social Media Algorithms | Data-Driven PR | Doctor Spin
In a wired world of online abund­ance, gate­keep­ing is critical.

Unfortunately, the actu­al inner work­ings of a social media algorithm have a much dark­er side. And yes, “dark­ness” is a reas­on­able ana­logy because these algorithms are being kept secret for many reasons.

Behind these cur­tains of secrecy, we don’t find myri­ads of com­plex com­put­ing lay­ers but man­u­fac­tured fil­ters designed by real people with per­son­al agendas. 

We are con­stantly shap­ing, sug­gest­ing, nudging, and presenting.

Social Algorithms are Not “Personal”

As users, we have a gen­er­al idea of how algorithms work, but only a hand­ful of people know. To pre­vent indus­tri­al espi­on­age, we can safely assume that most social net­works are mak­ing sure that no one developer has full access to the entirety of an algorithm.

And even if you’re a Facebook pro­gram­mer, how would you know exactly how Google’s algorithm works?

One might assume you have a per­son­al Facebook algorithm stored on a serv­er some­where. An algorithm that tracks you per­son­ally and learns about you and your beha­viour. And the more it knows about you, the bet­ter it under­stands you. But this is not exactly how it works — for a good reason.

Humans are notori­ously bad at con­sciously know­ing ourselves and under­stand­ing oth­ers. And our think­ing is riddled with uncon­scious biases. 

However com­plex, apply­ing vari­ous types of machine learn­ing to learn about users and their inter­ac­tions on the indi­vidu­al level would be both slow and expens­ive. Few social media users would be patient enough to endure such a lengthy pro­cess through tri­al and error.

Anyone famil­i­ar with data min­ing will know that more advanced scrap­ing tech­niques, like sen­ti­ment ana­lys­is from social media mon­it­or­ing, will require large data sets. Hence, the term “big data.” 1Batrinca, B., Treleaven, P. Social media ana­lyt­ics: a sur­vey of tech­niques, tools and plat­forms. AI & Soc, 89 – 116 (2015).‑0549‑4

A social media algorithm gets immense power primar­ily from har­vest­ing data from large users sim­ul­tan­eously and over time, not from cre­at­ing bil­lions of self-con­tained algorithms. 

Put anoth­er way: Algorithms are fig­ur­ing out human­ity at scale, not your behaviour.

Our Lack of Consistency

No one argues the fact that you are an indi­vidu­al. But is your beha­viour con­sist­ent from inter­ac­tion to inter­ac­tion? The answer is prob­ably no. How you act and react will likely be much more con­tex­tu­al and situ­ation­al — at least con­cern­ing your per­ceived unique­ness and self-identification.

Social net­works are lim­it­ing your options to tweak “your algorithm” your­self. I would be all over the pos­sib­il­ity of adjust­ing Facebook’s news­feed or Google’s search res­ults in fine detail using vari­ous boolean rule­sets. However, that would prob­ably only teach the mas­ter social algorithm more about human pre­ten­sions — and little else.

Accurate per­son­al algorithms that fol­low us from ser­vice to ser­vice could out­per­form all oth­er algorithms. I would prob­ably ask my algorithm only to show me ser­i­ous art­icles writ­ten in peer-reviewed pub­lic­a­tions or pub­lished by well-edu­cated authors with proven track records. But if the algorithm does­n’t take it upon itself to show me some funny cat memes or weird YouTube clips now and then, I would prob­ably be bored quite quickly.

When we under­stand that the social media algorithms aren’t try­ing to fig­ure you out, it fol­lows to ask how well they’re doing in fig­ur­ing out humanity.

Why Social Media Algorithms Aren’t Better

Social media algorithms are pro­gress­ing — painstak­ingly. Understanding human beha­viour at the macro level is not a task to be underestimated.

  • Google struggles to show rel­ev­ant search res­ults, and it still isn’t uncom­mon for users to search quite a bit before find­ing the inform­a­tion they seek. 
  • Facebook struggles with users com­plain­ing about what they’re being shown in the newsfeeds. 
  • Spotify struggles to sug­gest new music and often misses the mark by a mile.
  • LinkedIn is strug­gling to be busi­ness-rel­ev­ant while at the same time per­son­ally enga­ging (i.e. not boring). 
  • Instagram is strug­gling not to make people feel bad about themselves. 
  • Pinterest is strug­gling with inter­pret­ing per­son­al visu­al taste and intent. 
  • Netflix struggles with sug­gest­ing what to watch (“Why on Earth would I want to see Jumanji 2?”).
  • Amazon is strug­gling with telling us what to buy (“Please stop; I regret click­ing on those purple bath tow­els by mis­take a year ago!”).

The prac­tic­al approach to these struggles is straight­for­ward:
Eliminate the guesswork.

Social Algorithms: The Silent Switch

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Algorithms: The Silent Switch

Not that long ago, social media algorithms would deliv­er organ­ic reach accord­ing to a dis­tri­bu­tion that looked like this:

The Silent Switch - Doctor Spin - The PR Blog.001
Social media algorithms before the silent switch (click to enlarge).

Today, social media algorithms deliv­er organ­ic reach more like this:

The Silent Switch - Doctor Spin - The PR Blog.002
Social media algorithms after the silent switch (click to enlarge).

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: 2Nord, 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. … Continue read­ing

  • Aggravation. As a res­ult of media logic, the news media will exag­ger­ate events, con­cepts, and ideas to make them seem more ser­i­ous and/​or dan­ger­ous than they are.
  • Simplification. As a res­ult of media logic, the news media will dumb down events, con­cepts, and ideas to make them seem more under­stand­able than they are.
  • Polarisation. Because of media logic, the news media por­trays events, con­cepts, and ideas as more conflicting/​provocative than they are.
  • Intensification. As a res­ult of media logic, the news media will sen­sa­tion­al­ise events, con­cepts, and ideas to make them more inter­est­ing than they are.
  • Concreteness. Because of media logic, news media will report events, con­cepts, and ideas more straight­for­wardly than they are.
  • Personalisation. As a res­ult of media logic, the news media will over-emphas­ise the role of named indi­vidu­als in con­junc­tion with events, con­cepts, and ideas.
  • Stereotypisation. Because of media logic, the news media frames events, con­cepts, and ideas as more aligned with con­ven­tion­al perceptions/​opinions than they are.

Social Media Logic Effects

  • Programmability. Social media logic enables and encour­ages users to cre­ate and manip­u­late con­tent, lead­ing to a tailored por­tray­al of events, con­cepts, and ideas that might not fully rep­res­ent reality.
  • Popularity. Driven by social media logic, con­tent that gains ini­tial pop­ular­ity can dis­pro­por­tion­ately influ­ence pub­lic per­cep­tion, regard­less of accur­acy or completeness.
  • Connectivity. Social medi­a’s inter­con­nec­ted nature, rein­forced by social media logic, facil­it­ates the rap­id spread of inform­a­tion, often without suf­fi­cient veri­fic­a­tion, lead­ing to a dis­tor­ted under­stand­ing of events and ideas.
  • Datafication. The social media logic of con­vert­ing inter­ac­tions into data points emphas­ises quan­ti­fi­able aspects of events, con­cepts, and ideas, poten­tially over­look­ing their qual­it­at­ive nuances.

Our job as PR pro­fes­sion­als is to help organ­isa­tions nav­ig­ate the media land­scape and com­mu­nic­ate more effi­ciently, espe­cially dur­ing times of change.

Learn more: The Silent Switch: How Algorithms Have Changed

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Virality Through Real-Time Testing

A dom­in­at­ing fea­ture of today’s social media algorithms is real-time testing. 

If you pub­lish any­thing, the algorithm will use its data to test your con­tent on a small stat­ist­ic­al sub­set of users. If their reac­tions are favour­able, the algorithm will show your pub­lished con­tent to a slightly lar­ger sub­set — and then test again. And so on.

Suppose your pub­lished con­tent has vir­al poten­tial, and your track record as a pub­lish­er has gran­ted you enough plat­form author­ity to sur­pass crit­ic­al mass. In that case, your con­tent will spread like rings on water through­out more extens­ive sub­sets of users. 

How the Instagram Algorithm Works
Via Marketing Tools.

This test­ing algorithm isn’t as math­em­at­ic­ally com­plex as one might think from a pro­gram­ming stand­point. The most robust approach to increas­ing vir­al­ity is redu­cing cycle times.

YouTube’s algorithm argu­ably does well in cycle times, but the real star of online vir­al­ity is the Chinese plat­form TikTok.

Social Media Algorithms - Silent Shift - Doctor Spin - The PR Blog
How algorithms iter­ate to max­im­ise engage­ment and con­tent quality.

Read also: What You Need To Know About TikTok’s Algorithm

And this is where it gets pitch black. The com­plex­ity and gate­keep­ing prowess of today’s social media algorithms don’t primar­ily stem from their cre­at­ive use of big data and high-end arti­fi­cial intel­li­gence but from the blunt use of syn­thet­ic filters.

The social media algorithms could be much more com­plex using machine learn­ing, nat­ur­al lan­guage pro­cessing, and arti­fi­cial intel­li­gence com­bined with human psy­cho­logy neur­al net­work mod­els. Especially if we allow these pro­to­cols to be indi­vidu­al across ser­vices, we will enable them to lie to us just a bit. 

But, no. Instead, the social media algorithms of today are sur­pris­ingly straight­for­ward and based on real-time iter­at­ive test­ing. Today, vir­al­ity is con­trolled mainly via the use of added filters.

Underestimating the Effects of Filters

The algorithmic com­plex­ity is primar­ily derived from humans manu­ally adding fil­ters to algorithms in their con­trol. These fil­ters are tested on smal­ler sub­sets before rolling out on lar­ger scales.

Most of us have heard cre­at­ors on Instagram, TikTok, and some­times YouTube com­plain about being “shad­ow­banned” when their reach sud­denly dwindles from one day to the next — for no appar­ent reas­on. Sometimes, this might be due to changes to the mas­ter algorithm, but most cre­at­ors are prob­ably affected by newly added filters.

Please make no mis­take about it: Filters are power­ful. No mat­ter how well a piece of con­tent would nego­ti­ate the mas­ter algorithm — if a piece of con­tent gets stuck in a fil­ter, it’s going nowhere. And these fil­ters aren’t the out­put of some ultra-smart algorithm; humans add them with cor­por­ate or ideo­lo­gic­al agendas.

There is no inform­a­tion over­load, only fil­ter fail­ure.”
— Clay Shirky

TikTok serves as one of the darkest examples of algorithmic abuse. 

Leaked intern­al doc­u­ments revealed how TikTok added fil­ters to lim­it con­tent by people clas­si­fied as non-attract­ive or poor. 

And yes, this is where the dark­ness comes into full effect — when human agen­das get added into the algorithmic mix.

One doc­u­ment goes so far as to instruct mod­er­at­ors to scan uploads for cracked walls and “dis­rep­ut­able dec­or­a­tions” in users’ own homes — then to effect­ively pun­ish these poorer TikTok users by arti­fi­cially nar­row­ing their audi­ences.”
Source: The Intercept 3Biddle, S., Paulo Victor Ribeiro, & Dias, T. (2020, March 16). TikTok Told Moderators to Suppress Posts by “Ugly” People and the Poor to Attract New Users. The Intercept. … Continue read­ing

The grim irony is that adding fil­ters is rel­at­ively straight­for­ward from a pro­gram­ming perspective. 

We often con­sider algorithms advanced black boxes that oper­ate almost above human com­pre­hen­sion. But with reas­on­ably exact algorithms, it is arti­fi­cial fil­ters we need to watch out for and consider.

The Power of Perception Management

Since we can­not change real­ity, let us change the eyes which see real­ity.”
— Nikos Kazantzakis

What would hap­pen if Google and Facebook filtered away a spe­cif­ic day? Everything that refers to that day wouldn’t pass any iter­at­ive tests any­more. Any con­tent from that day would be shad­ow­banned. And search engine res­ults pages would deflect any­thing related to that par­tic­u­lar day.

To para­phrase a pop­u­lar TikTok meme, “How would you know?”

No one makes decisions based on the actu­al real­ity; we all make decisions based on our lim­ited under­stand­ing of that real­ity. Hence, if you con­trol some parts of that real­ity, you indir­ectly con­trol what people do, say, or even think. 4Lippmann, Walter. 1960. Public Opinion (1922). New York: Macmillan.

This is the truth about how social media algorithms are con­trolling our lives:

Social media algorithms influ­ence our per­cep­tions of real­ity and, by exten­sion, our atti­tudes and behaviours.

Learn more: Walter Lippmann: Public Opinion and Perception Management

The First Rule of Social Media Algorithms

Social net­works don’t want us talk­ing and ask­ing ques­tions about their algorithms des­pite being at the core of their busi­nesses. Because 1) they need to keep them secret, 2) they are blunter than we might think, 3) their com­plex­ity is mani­fes­ted primar­ily by arti­fi­cial fil­ters, and 4) they don’t want to dir­ect our atten­tion at how much gate­keep­ing power they yield.

And both journ­al­ists and legis­lat­ors aren’t exactly hard at expos­ing these appar­ent demo­crat­ic weak­nesses; journ­al­ists want their lost gate­keep­ing power back and legis­lat­ors because they see ideo­lo­gic­al oppor­tun­it­ies to gain con­trol over these filters.

Any PR pro­fes­sion­al knows that the news media has an agenda. And that we must man­age that agenda. Otherwise, it might spin out of control. 

A social media algorithm can be suc­cess­fully nego­ti­ated and some­times work for you or your organ­isa­tion. But an algorithm with its fil­ters will nev­er be your friend. 

Social net­works are “good” in the same way the news media is “object­ive”, or politi­cians are “altru­ist­ic”. As pub­lic rela­tions pro­fes­sion­als, we should act accord­ingly and man­age the social media algorithms — just as we man­age journ­al­ists and legislators.

Read also: Social Media: The Good, The Bad, The Ugly

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Thanks for read­ing. Please sup­port my blog by shar­ing art­icles with oth­er com­mu­nic­a­tions and mar­ket­ing pro­fes­sion­als. You might also con­sider my PR ser­vices or speak­ing engage­ments.

PR Resource: More Social Media

PR Resource: The 7 Graphs of Algorithms

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Types of Algorithm Graphs

Search engines, social net­works, and online ser­vices typ­ic­ally have a wealth of user data to optim­ise the user experience.

Here are examples of dif­fer­ent types of graphs that social media algorithms use to shape desired behaviours:

  • Social graph. The media com­pany can access your friend list and push their con­tent (or favoured con­tent) into your feed.
  • Interest graph. The media com­pany can access your interests (top­ics, per­sons of interest, dif­fi­culty level, format pref­er­ences, on-plat­form-spe­cif­ic beha­viours, etc.) from your usage history.
  • Predictive graph. The media com­pany can access all graphs from users not con­nec­ted to you but with whom you share a stat­ist­ic­al like­ness and show their pre­ferred con­tent to you.
  • Prescriptive graph. The media com­pany can push con­tent into your feed to manip­u­late your over­all emo­tion­al exper­i­ence when using the platform.
  • Trend graph. The media com­pany can push con­tent into your feed based on what seems to be trend­ing on the platform.
  • Contextual graph. The media com­pany can access con­tex­tu­al data like loc­a­tion, weath­er, cal­en­dar events, affil­i­ations, world events, and loc­al events.
  • Commercial graph. The media com­pany can access data on how you and oth­ers like you inter­act with com­mer­cial content.

The dif­fer­ent graphs are typ­ic­ally weighted dif­fer­ently. For instance, some media com­pan­ies allow a fair degree of social graph con­tent, while oth­ers offer almost none. Changes are con­stantly being enforced, and the silent switch might be the most not­able example of a media com­pany shift­ing away from the social graph. 5Silfwer, J. (2021, December 7). The Silent Switch — A Stealthy Death for the Social Graph. Doctor Spin | The PR Blog. https://​doc​tor​spin​.net/​s​i​l​e​n​t​-​s​w​i​t​ch/

The media com­pany can lever­age these graphs using two main approaches:

  • Matching. The media com­pany can use vari­ous graphs to gen­er­ate your social feed. Depending on the com­plex­ity of the ana­lys­is, this approach is slow and expens­ive with react­ive (unpre­dict­able) results.
  • Profiling. The media com­pany can use vari­ous graphs to place you in stat­ist­ic­al sub­groups, allow­ing con­tent to iter­ate to the right audi­ence. This approach is fast and cheap with pro­act­ive (pre­dict­able) results.

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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PR Resource: The Electronic Age

The Electronic Age according to Marshall McLuhan.
The Electronic Age accord­ing to Marshall McLuhan.
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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.

Marshall McLuhan - Cambridge University - Digital-First
Marshall McLuhan at Cambridge University, circa 1940.

McLuhan sug­gests divid­ing human civil­isa­tion into four epochs:

  • Oral Tribe Culture. Handwriting marks the begin­ning of the end of the Oral Tribe Culture. The Oral Tribe Culture per­sists but without its former prominence.
  • Manuscript Culture. Printing marks the begin­ning of the end of the Manuscript Culture. The Manuscript Culture per­sists but without its former prominence.
  • Gutenberg Galaxy. Electricity marks the begin­ning of the end of the Gutenberg Galaxy. The Gutenberg Galaxy per­sists but without its former prominence.
  • Electronic Age. Today, we reside in the Electronic Age. Possibly, we haven’t exper­i­enced the begin­ning of this age’s decline yet.

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 6McLuhan, 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. My belief is that soci­ety is unlikely to revert to the Gutenberg Galaxy.

Thus, digit­al-first is the way for pub­lic rela­tions, too.

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

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1 Batrinca, B., Treleaven, P. Social media ana­lyt­ics: a sur­vey of tech­niques, tools and plat­forms. AI & Soc, 89 – 116 (2015).‑0549‑4
2 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​.researchg​ate​.net/​p​u​b​l​i​c​a​t​i​o​n​/​2​7​1​0​1​4​6​2​4​_​T​i​o​_​d​a​g​a​r​_​s​o​m​_​s​k​a​k​a​d​e​_​v​a​r​l​d​e​n​_​E​n​_​s​t​u​d​i​e​_​a​v​_​m​e​d​i​e​r​n​a​s​_​b​e​s​k​r​i​v​n​i​n​g​a​r​_​a​v​_​t​e​r​r​o​r​a​t​t​a​c​k​e​r​n​a​_​m​o​t​_​U​S​A​_​o​c​h​_​k​r​i​g​e​t​_​i​_​A​f​g​h​a​n​i​s​t​a​n​_​h​o​s​t​e​n​_​2​001
3 Biddle, S., Paulo Victor Ribeiro, & Dias, T. (2020, March 16). TikTok Told Moderators to Suppress Posts by “Ugly” People and the Poor to Attract New Users. The Intercept. https://​thein​ter​cept​.com/​2​0​2​0​/​0​3​/​1​6​/​t​i​k​t​o​k​-​a​p​p​-​m​o​d​e​r​a​t​o​r​s​-​u​s​e​r​s​-​d​i​s​c​r​i​m​i​n​a​t​i​on/
4 Lippmann, Walter. 1960. Public Opinion (1922). New York: Macmillan.
5 Silfwer, J. (2021, December 7). The Silent Switch — A Stealthy Death for the Social Graph. Doctor Spin | The PR Blog. https://​doc​tor​spin​.net/​s​i​l​e​n​t​-​s​w​i​t​ch/
6 McLuhan, 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
Jerry Silfwer
Jerry Silfwer
Jerry Silfwer, alias Doctor Spin, is an awarded senior adviser specialising in public relations and digital strategy. Currently CEO at Spin Factory and KIX Communication Index. Before that, he worked at Kaufmann, Whispr Group, Springtime PR, and Spotlight PR. Based in Stockholm, Sweden.

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