How Social Media Algorithms Actually Work (And How to Beat Them)

0:00Today we're talking about the social

0:01media algorithm. If you want to get more

0:03views with less effort, it's critical

0:05you understand how the algorithms

0:07actually work. And once you learn this,

0:09I guarantee you will never look at

0:11content the same way again. In this

0:13video, I'm going to break down how

0:14social algorithms work, why they pick

0:16certain videos to push over others, and

0:19the specific things you can do to make

0:21them prioritize your content. Now, this

0:23information is based on a ton of outlier

0:25data and comments made by the Instagram

0:28CEO himself. So, this is the latest and

0:30greatest for what's actually working

0:32right now. If you just follow this, your

0:34content will perform way better. By the

0:36way, if you don't know me, my name is

0:37Callaway. I have a million followers,

0:38I've done billions of views, and content

0:40is all I do all day long. All right,

0:43first, let's just start with how the

0:44algorithms actually work. And this is

0:46actually super helpful to understand.

0:48Once you hear it, it'll make a lot of

0:49sense. Social media companies only have

0:52one goal, to keep people on the platform

0:54as long as possible. When people stay

0:56longer, they watch more ads, and the

0:58companies make more money. It's as

0:59simple as that. Now, to keep you on the

1:01platform longer, they do their best to

1:03serve you the content they think you'll

1:05enjoy the most. And that means the

1:07algorithm is just one giant matchmaker.

1:10It's matching people with content. If it

1:12does a good job with this matching,

1:13you're going to keep watching and stay.

1:15But if it starts doing a bad job and it

1:17shows you stuff you don't want to watch,

1:19well then you're going to leave. It

1:20sounds simple, but this is how social

1:22algorithms work in a nutshell. Now,

1:24here's why this matters for you. If you

1:25want to hijack the algorithm and make it

1:28push your video more, all you have to do

1:30is help it make better matches with your

1:32content. [music] So, in this video, I'm

1:34going to explain exactly how to do that.

1:36And this really is the highest leverage

1:37algo hack you could ever learn. Okay,

1:39now here's how this matchmaking process

1:41actually works under the hood, so we can

1:43understand exactly what to do to

1:45manipulate it. When you post a video on

1:47social media, the very first thing the

1:49platform does is analyze what that video

1:51is about. I call this a digital

1:53fingerprint. Now, this analysis is

1:55multimodal, so it's watching your video

1:57with computer vision to understand

1:59what's going on visually. It's listening

2:02to your video with audio fingerprinting

2:04to get a better understanding of the

2:05transcript and what's actually being

2:06said, and it's also reading all the

2:08metadata, the caption, the hashtag, the

2:11creator, the location, anything else it

2:12can find. It then combines all that

2:15information together in real time to

2:17build a single contextual understanding

2:19of the video. This is called a topic

2:22mapping. Now, based on that topic

2:23mapping, the algorithm builds a fit

2:26score, which is its prediction for who

2:28it thinks will best like this video. So,

2:30at this point, you've posted it, it's

2:32analyzed it, but it hasn't been shown to

2:34anyone yet. So, we're ready to start

2:35showing the video to people. But, this

2:37is where things get interesting, because

2:38obviously the algorithm doesn't just

2:40blast your video off to millions of

2:41people right off the bat, or you'd have

2:43millions of views. This is what actually

2:45goes on under the hood. The algorithm

2:47uses its fit score to pick roughly 200

2:50people to show the video to first. This

2:52is called the initial sample test group.

2:55If it could rank all 100 million people

2:58that are on the app at one time, this is

3:00the group of 200 people it thinks will

3:02like the video the most. Now, very

3:04important, of these 200 people, most of

3:06them are non-followers, because the

3:09algorithm wants to test how well

3:10strangers react to your video. It knows

3:12followers should like it, cuz they

3:13already follow you, but if strangers

3:15like it, too, well, then that means this

3:17is a really good video. This is why when

3:19people say followers don't matter

3:21anymore, they're kind of right. They

3:22don't matter in the sampling process,

3:24because most of those 200 people are

3:26non-followers. They're strangers to you.

3:28Now, based on the metrics from this

3:29initial sample group of 200 people, the

3:31algorithm is going to get positive,

3:33neutral, or negative data back.

3:35Essentially, of those 200, how many of

3:37them liked and watched the video? What

3:39was the set of data? If the data is

3:41positive, that means the algorithm's

3:43guess of the fit score was accurate. And

3:46so, it knows exactly what type of person

3:48to push the video to further. The next

3:50time it pushes, let's say it's 2,000

3:52people. And if that's good, then it's

3:5420,000 people. And if that's good, then

3:56it's 200,000 people. And it just keeps

3:58going until the data starts coming back

4:00weaker. Now, if the original data was

4:02neutral, kind of good, kind of bad, the

4:04algorithm will redo its fit score and

4:06push the video again. But this time,

4:08only to maybe another group of 200. It

4:10doesn't go crazy to 20,000. It just

4:13resamples. If the data is negative right

4:15off the bat with those 200, well then

4:17the algo's going to tighten up and stop

4:19pushing almost immediately. And again,

4:21it slows down that push because it

4:23doesn't want to risk alienating people

4:25and pushing them off the platform

4:27because they see a bad video. So, if

4:28you're in the 200 view jail, or you post

4:31a video and it flops, what this really

4:33means is that the algorithm got bad data

4:35back from that initial sample group of

4:37200. Now, one more thing before we move

4:39on this. The reason why even million

4:41view banger videos eventually slow down

4:43is because even those run out of people

4:46that want to watch it. The data gets

4:48bigger, bigger, bigger, and then

4:49eventually it starts to fade off. So, in

4:51a nutshell, this is how social

4:52algorithms actually work under the hood.

4:55This is what happens when you go to post

4:56a video. You post it, it does the topic

4:58mapping, it samples with a group of

5:01roughly 200 people, and then it either

5:03boosts, retries, or stops immediately

5:06based on how the sample data comes back.

5:08So, what does all this actually mean for

5:10you tactically? Knowing this is

5:12happening under the hood, how can you

5:13best adjust your content strategy to

5:15take advantage and get the algorithm to

5:17push you more? That's what we're going

5:19to go through right now.

5:22If you want to hijack the algorithm to

5:24get more views, you only need to do two

5:26things. Number one is to help the

5:28algorithm build a better fit score, so

5:30that it finds the best possible sample

5:32group of 200 people to go to first. And

5:35then number two is to make sure that

5:36sample group actually likes and engages

5:38with your video. Because if the sample

5:40group is the right fit, and they

5:42actually like your video, well then

5:43you're going to be in great shape

5:44because the algo will get amazing data

5:46back and just [music] keep boosting you

5:48to more and more people. So, what I'm

5:49going to do now is break down the

5:51tactics for how to trigger both of those

5:53things. Helping find the right sample

5:55group, and then helping make sure that

5:57sample group engages well. This is

5:59essentially the systematic process for

6:01activating the algorithm and getting it

6:03to work for you. Now, if you like how

6:05I'm breaking this down, kind of from

6:06like a scientific and psychology

6:08perspective, I actually just published a

6:10free guide doing the same thing for my

6:12entire content system. It's from ideas

6:15to hooks all the way to monetization.

6:16Science-based and data-backed. This is

6:18the exact content system I ran last year

6:21to generate hundreds of millions of

6:22views and millions in profit from

6:24content. It's also the same thing I

6:26install with business owners when I work

6:28with them one-on-one. Completely free,

6:30my gift to you. You can get it below or

6:32at the link shortformsytem.co.

6:34All right, let's talk sample groups.

6:36What can you do to help the algorithm

6:38build a better fit score and find a

6:40stronger sample group for your video?

6:42Here's the answer, very simple. All you

6:44have to do is consistently make videos

6:46about the same topic for the same

6:48audience avatar over and over and over.

6:51You want to become intentionally precise

6:53and narrow with the topics you pick and

6:55how you position. Here's why. After a

6:58few similar videos in a row, the

7:00algorithm will start to understand that

7:02your channel talks about X topic for Y

7:05avatar profile. It will then have built

7:07sample groups for all those previous

7:09videos and have a very clear

7:10understanding of who to go back to from

7:13an avatar perspective. The more this

7:15avatar group is the same over time, the

7:17more confident the algorithm can be when

7:20it dials this in. Now, this process of

7:22consistently making videos about the

7:23same few topics for the same avatar is

7:26called audience matching. And if there's

7:28one content principle I swear by, it's

7:30this. When you make videos for lots of

7:32different topics for several different

7:34avatars, the sample data comes back

7:36mixed and the algorithm gets confused.

7:38When it's confused, it pushes your video

7:40less because it doesn't want to risk bad

7:42fits to bad viewers. For example,

7:45imagine you made three videos. One on

7:47tech, then the next one on health

7:48trends, and then the next one on

7:50politics. The algorithm would have no

7:52idea what your fourth video is going to

7:54be about. And because of this, it

7:56doesn't know which of your previous

7:57three videos it should model its fit

7:59score after. So, let's say your fourth

8:01video also ends up being about health

8:03trends. Chances are the algorithm's

8:04going to build a blended fit score

8:06across those first three videos. A

8:08little bit of people from tech, a little

8:10bit of people from health trends, and a

8:11little bit of people from politics. Not

8:12literally those people, but influence

8:15from who liked those videos. And when it

8:17does this, the fit score targeting for

8:19your fourth video is going to be a mix

8:21of all three. And so, when it pushes it,

8:23of course, the sample data for the

8:25health trends video that also has tech

8:27and politics type viewers is going to

8:29come back weak. This will result almost

8:32certainly in your video flopping. What

8:34this means in simple terms is that if

8:35you want to help the algorithm find the

8:38right sample group and build a better

8:40fit score, you got to keep your topics

8:42and audience selection narrow

8:43consistently. And this means sometimes

8:45saying no to ideas that seem viral, but

8:48would resonate with the wrong audience.

8:50Even one viral hit to an audience

8:52outside of your core demo will result in

8:55the next several videos having poor

8:57sample data because it confuses the

8:59algorithm. This discipline in topic and

9:01audience selection is very important,

9:03and typically beginners that are

9:04starting out are spraying and praying

9:06all over the place, and they don't have

9:07this. Okay, so that's one side of the

9:09equation. Very simply, just narrow your

9:11topic and audience, and your sample fit

9:13will go up. Now, on the other side of

9:15the equation, the two-part piece was

9:16making sure, once you have that sample,

9:18that it actually engages well with your

9:20video. And that means they watch it,

9:22they like it, they save it, they share

9:24it, they comment, they repost, all the

9:26engagement metrics, as many as we can

9:27possibly get.

9:28>> [music]

9:28>> So, what can we do on this side to make

9:30sure that initial sample data from these

9:32people comes back strong? Well, when the

9:34algorithm's gauging if it's strong or

9:37not, it's really only looking at three

9:38core metrics. The first one is average

9:41watch time. How long did someone watch

9:43in a number of seconds per video? And

9:45also, by proxy, percent completion. What

9:47percent of the video was completed on

9:49average? The second metric is engagement

9:51rate. So, this is likes plus comments

9:53plus shares divided by views. And the

9:55third, which is really important and

9:57nobody can access, is called watch time

9:59session share. In a session for a

10:01viewer, if they're on there for 60

10:02minutes, how many of those 60 were spent

10:04watching your videos? That percentage is

10:07watch time session share. You can't

10:09access this anywhere, but it's a

10:10critical metric that social algorithms

10:12use to know how influential your content

10:14is.

10:15>> [music]

10:15>> So, the million-dollar question for you

10:16is how can you improve these metrics?

10:18What can you do in your video to make

10:20sure those metrics go up so the sample

10:22data comes back clean, so that it just

10:23pushes your video to more people? Well,

10:25a short, cheeky answer is if you want

10:27the metrics to go up, you just make

10:29better videos with better ideas,

10:31stronger hooks, better storytelling, and

10:32more interesting visuals, obviously.

10:35But, that's not helpful at all. So, is

10:36there anything tactical you can do at a

10:38studs level to increase the

10:40effectiveness of those videos? And if

10:42you watch this channel a lot, you

10:44already know, of course there is.

10:45There's only four things you need to do

10:47to make your video better so that that

10:48engagement rate goes off the charts.

10:51>> [music]

10:51>> Number one is that the topic needs to be

10:52relevant for the ideal viewer. This is

10:55obvious, and it goes with the first

10:56piece I said. What you cover has to

10:58actually solve a problem that they have.

11:00If that's the case, engagement will go

11:02up. Number two, the information you

11:04share needs to be both non-obvious and

11:07tactically implementable. Is it new

11:09stuff they haven't heard before, and can

11:10they actually use it to solve that

11:12problem? If those things are true, the

11:14engagement rate will go up. Number

11:16three, the viewer has to actually have a

11:18high absorption of the information you

11:20say. It could be on target and

11:22non-obvious, but if they can't actually

11:23understand what you're saying, then they

11:25can't apply it. So, if they could apply

11:27it, the engagement rate will go up. And

11:28then number four, there needs to be a

11:30short distance to implement your

11:32recommendations. Tactically

11:33implementable means they can take a

11:35little bit of action and get a big

11:37result based on your promise. Now, you

11:39won't typically hear people frame it in

11:41this way, but if your content has those

11:42four attributes, I guarantee you're

11:45going to have higher engagement. If you

11:46have higher engagement, the data comes

11:48back more positive, they push it to more

11:50of the people, those people are people

11:51you want, and the flywheel spins. What

11:53this really means, those four things in

11:55layman's terms, you got to cover a core

11:57pain point or problem they have. You got

11:59to have something useful or interesting

12:01to say. You got to say it in a way they

12:03can actually understand, and they have

12:05to be able to take what you say and

12:07apply it on their own. Those are the

12:09four horsemen to driving good video

12:11performance. If you do this, you're set,

12:14and that's really all you need to hijack

12:15the algorithm to push you more. Pick an

12:18avatar, stick to it, narrow your topic

12:20selection, and then drive those four

12:22things home. When you do this, the

12:24sample group will stay dialed, and

12:25they'll all engage with the video at a

12:27high rate. Incidentally, these four

12:29factors are also how you turn viewers

12:31into buyers. If you want people to buy,

12:33those four components also make sense to

12:35include in the video. They're kind of

12:36like the core DNA if you're trying to

12:38build a money machine with content. Now,

12:40I'll say this, the easiest way to make

12:42sure you're picking the right topics

12:44that actually work for your avatar group

12:46is to just study the videos that are

12:49already working in your niche. It shocks

12:51me how few people actually do this, but

12:53in sandcastles.ai, you can build a group

12:56of competitor channels that are already

12:58crushing, and just filter by outlier

13:00score to see all the best performing

13:02videos. There's now this feature where

13:04if you save the video to library, you

13:07can see all the attributes. I'm talking

13:09transcript, topic, the exact hook, the

13:12exact storytelling mechanics, everything

13:14about the video that drove the

13:15curiosity. You can then take that, remix

13:17it. Everything you need is right in

13:19there. So, all you need to do if you're

13:20confused on which topics to pick for

13:23your avatar group, just go in

13:24sandcastles and use this resource. It

13:26shocks me how few people are using data

13:29to make their topic decision. This

13:31basically guarantees that you're serving

13:32the right stuff to your audience. Now,

13:34before I end this video, I just want to

13:35include one more bonus topic around the

13:38algorithm. Because I know my explanation

13:39is a little bit theoretical, hopefully

13:41it made sense, hopefully it helped you,

13:42you have action items to work on. But, I

13:44just want to include one more thing at a

13:46tactical level that you can take away

13:48and really hammer value from this video.

13:50Another way to drive algorithmic push is

13:52to increase the number of comments on

13:54your video. Most people know this. There

13:56are five things you can do tactically to

13:58increase the number of comments you're

14:00getting. Number one is to take a hard

14:02stance on your topic. People typically

14:04comment when they violently agree or

14:06disagree, mostly disagree, with whatever

14:09your stance is or perspective. If you

14:11play the middle and hedge, you're going

14:13to get fewer comments. So, I recommend

14:15you pick a side, pro or con, and that

14:17will drive comments. [music] Number two

14:19is to pick the side that is the

14:21contrarian side. Like I said, people

14:22love to comment when they disagree, when

14:24they think you're wrong. If you pick the

14:26contrarian side, that means you think

14:27the majority of people are wrong, which

14:29means they'll think you're wrong. The

14:31majority of people will want to comment

14:32because they disagree with you. If you

14:34create more enemies, you drive more

14:36comments. Tip number three is to amplify

14:38the stance you take by ratcheting up the

14:40way you frame your points. If you said

14:42something like, "This is the best way to

14:44cook pasta." versus, "This pasta is

14:47better than all the mom and pop pasta

14:49shops in the world." Which one is going

14:50to drive a more violent discussion in

14:52the comments? The more extreme version,

14:54of course, always is. So, that's how you

14:56ratchet up your stance. Tip number four

14:58is to build your topics around

14:59cult-loved brands, people, ideas, and

15:03movements. The more you talk about

15:04things people already have made up their

15:06opinion on, the faster they're willing

15:08to jump into the comments, especially if

15:10they disagree. For example, if you take

15:12a stance on Nike versus just the

15:13category of shoes, more people will have

15:16already made up their opinion whether

15:17they like or dislike Nike, and it will

15:19trigger them to comment. Tip number five

15:20is to position your take or stance to

15:22drive significant emotion. The more

15:25people feel something when they watch

15:26your video, the more they're going to

15:27feel compelled to want to comment. Now,

15:30those five things around comment, that

15:31was just a little extra sprinkle to give

15:33you more tactics on how to drive

15:35activation and engagement to make the

15:38algorithm push you. All of those feed

15:40back to picking the right topic for the

15:42right group. So, it's kind of like a sub

15:44point on what I just went through.

15:45Hopefully that's helpful and you can put

15:46that to use. All right, guys. That's all

15:48I've got for this video. As a recap, we

15:49covered a lot of ground. We really broke

15:51down the ins and outs of how social

15:52algorithms work and how you

15:54theoretically and fundamentally can

15:57hijack them to push your videos more. I

15:59tried my best to kind of demystify this

16:01black box and give you tactics that you

16:04can use to your advantage. As always,

16:05guys, I'm trying my absolute best to

16:07give you perspectives that most people

16:08don't cover in a tactical bite-size way

16:11that you can actually put to work. What

16:12you typically see about the algorithm is

16:14talking about settings, hacks, or these

16:16little caption tweaks. None of that

16:18stuff actually works. If you think that

16:20actually works, you need to watch this

16:21video again. Posting time does not

16:23matter. Hashtags in your captions don't

16:25matter. The captions themselves don't

16:27matter. The only thing that matters is

16:29making great videos for a specific

16:31avatar group across a narrow band of

16:33topics over and over and over. That's

16:35the only thing that matters. That is the

16:37cake. Everything else is the icing.

16:39Focus on the cake. As a reminder, if you

16:41want access to my full content system,

16:43this is the exact blueprint I use with

16:45my own team. How I find ideas, how I

16:47write hooks, how I validate these

16:49things, how I do research, my entire

16:51funnel to turn viewers into dollars,

16:53literally everything. I've got it linked

16:54below for free, my gift to you,

16:56shortformsytem.co.

16:58And if you have any other topics around

17:00social media growth, around content and

17:02content systems that you want me to

17:04cover or that you feel blocked on, just

17:06drop them in the comments. We use the

17:08comments to inform the database of

17:09videos that we make next. So, anything

17:11you guys are stuck on, please put it in

17:13the comments. It will help greatly to

17:14inform what we should make. All right,

17:16guys. That's all we've got. We will see

17:18you on the next video. Peace.