Shadowing Practice: How Social Media Algorithms Actually Work (And How to Beat Them) - Learn English Speaking with Video

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Today, we're talking about the social media algorithm.

If you wanna get more views with less effort, it's critical you understand how the algorithms actually work.

And once you learn this, I guarantee you will never look at content the same way again.

In this video, I'm gonna break down how social algorithms work, why they pick certain videos to push over others,

and the specific things you can do to make them prioritize your content.

Now, this information is based on a ton of outlier data and comments made by the Instagram CEO himself.

is the latest and greatest for what's actually working right now

if you just follow this your content will perform way better by the way

if you don't know me my name is callaway i have a million followers i've done billions of views

and content is all i do all day long all right

first let's just start with how the algorithms actually work

and this is actually super helpful to understand once you hear

it it'll make a lot of sense social media companies only

have one goal to keep people on the platform as long as possible

when people stay longer they watch more ads and the companies Now to keep you on the platform longer,

they do their best to serve you the content they think you'll enjoy the most.

And that means the algorithm is just one giant matchmaker.

It's matching people with content.

If it does a good job at this matching, you're going to keep watching and stay.

But if it starts doing a bad job and it shows you stuff you don't want to watch, well then you're going to leave.

It sounds simple, but this is how social algorithms work in a nutshell.

Now here's why this matters for you.

If you want to hijack the algorithm and make it push is help it make better matches with your content.

So in this video, I'm gonna explain exactly how to do that.

And this really is the highest leverage algo hack you could ever learn.

Okay, now here's how this matchmaking process actually works under the hood

so we can understand exactly what to do to manipulate it.

When you post a video on social media, the very first thing the platform does is analyze what that video is about.

I call this a digital fingerprint.

Now, this analysis is multimodal, so it's watching your video with computer vision to understand what's going on visually.

It's listening to your video with audio fingerprinting to get a better understanding of the transcript and what's actually being said.

And it's also reading all the metadata, the caption, the hashtag, the creator, the location, anything else it can find.

in real time to build a single contextual understanding of the video.

This is called a topic mapping.

Now based on that topic mapping, the algorithm builds a fit score, which is its prediction for who it thinks will best like this video.

So at this point you've posted it, it's analyzed it, but it hasn't been shown to anyone yet.

So we're ready to start showing the video to people.

But this is where things get interesting

because obviously the algorithm doesn't just blast your video off to millions of people right off the bat, or you'd have millions of views.

hood.

The algorithm uses its fit score to pick roughly 200 people to show the video to first.

This is called the initial sample test group.

If it could rank all 100 million people that are on the app at one time, this is the group of 200 people it thinks will like the video the most.

Now, very important of these 200 people, most of them are non followers because the algorithm wants to test how well strangers react to your video.

It knows followers should like it because they well then that means this is a really good video.

This is why when people say followers don't matter anymore, they're kind of right.

They don't matter in this sampling process because most of those 200 people are non -followers.

They're strangers to you.

Now, based on the metrics from this initial sample group of 200 people, the algorithm is going to get positive, neutral, or negative data back.

Essentially of those 200, how many of them liked and watched the video?

What was the set of data?

If the data is positive, that means the algorithm's guess of the fit score was accurate.

And so, it knows exactly what type of person to push the video to further.

The next time it pushes, let's say it's 2 ,000 people.

And if that's good, then it's 20 ,000 people.

And if that's good, then it's 200 ,000 people.

And it just keeps going until the data starts coming back weaker.

Now, if the original data was neutral, kinda good, kinda bad, the algorithm will redo its fit score and push the video again.

But this time, only to maybe another group of 200.

It doesn't go crazy to 20 ,000, it just re -samples.

If the data is negative right off the bat 200, well then the algo is going to tighten up and stop pushing almost immediately.

And again, it slows down that push

because it doesn't want to risk alienating people and pushing them off the platform because they see a bad video.

So if you're in the 200 view jail or you post a video and it flops, what this really means is that the algorithm got bad data back from that initial sample group of 200.

Now, one more thing before we move on this, the reason why even million view want to watch it.

The data gets bigger, bigger, bigger, and then eventually it starts to fade off.

So in a nutshell, this is how social algorithms actually work under the hood.

This is what happens when you go to post a video.

You post it, it does the topic mapping, it samples with a group of roughly 200 people, and then it either boosts,

retries, or stops immediately based on how the sample data comes back.

So what does all this actually mean for you tactically?

Knowing this is happening under the hood, how can you best adjust your content strategy to take advantage and get the algorithm to push you more?

That's what we're going to go through right now.

If you want to hijack the algorithm to get more views, you only need to do two things.

Number one is to help the algorithm build a better fit score

so that it finds the best possible sample group of 200 people to go to first.

And then number two is to make sure that sample group actually likes and engages with your video.

Because if the sample group is the right fit and they actually like your video, well, then you're going to be in great shape

because the algo will get amazing data back and just keep boosting you to more and more people.

sure that sample group engages well.

This is essentially the systematic process for activating the algorithm and getting it to work for you.

Now, if you like how I'm breaking this down, kind of from like a scientific and psychology perspective, I actually just published a free guide doing the same thing for my entire content system.

It's from ideas to hooks all the way to monetization, science -based and data -backed.

This is the exact content system I ran last year to generate hundreds of millions of views

and millions in profit from content.

It's also the same thing I with business owners when I work with them one -on -one.

Completely free, my gift to you.

You can get it below or at the link shortformsystem .co.

All right, let's talk sample groups.

What can you do to help the algorithm build a better fit score and find a stronger sample group for your video?

Here's the answer.

Very simple.

All you have to do is consistently make videos about the same topic for the same audience avatar over

and over and over.

You want to become intentionally precise Here's why.

After a few similar videos in a row, the algorithm will start to understand that your channel talks about X topic for Y avatar profile.

It will then have built sample groups for all those previous videos

and have a very clear understanding of who to go back to from an avatar perspective.

The more this avatar group is the same over time, the more confident the algorithm can be when it dials this in.

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And this process of consistently making videos about the same few topics for the same avatar is called audience matching.

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And if there's one content principle I swear by, it's this.

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When you make videos for lots of different topics for several different avatars, the sample data comes back mixed and the algorithm gets confused.

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When it's confused, it pushes your video less because it doesn't want to risk bad fits to bad viewers.

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For example, imagine you made three videos, one on tech, then the next one on health trends, and then the next one on politics.

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algorithm would have no idea what your fourth video is gonna be about.

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And because of this, it doesn't know which of your previous three videos it should model its fit score after.

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So let's say your fourth video also ends up being about health trends.

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Chances are the algorithm is going to build a blended fit score across those first three videos.

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A little bit of people from tech, a little bit of people from health trends, and a little bit of people from politics.

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Not literally those people, but influence from who liked those videos.

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And when it does this, the fit score targeting for your fourth video is going to be a mix of all three.

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for the health trends video that also has tech and politics type viewers is going to come back weak.

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This will result almost certainly in your video flopping.

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What this means in simple terms is that

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if you want to help the algorithm find the right sample group and build a better fit score, you got to keep your topics and audience selection narrow consistently.

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And this means sometimes saying no to ideas that seem viral, but would resonate with the wrong audience.

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Even one viral hit to an audience outside of your core

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demo will result in the next several videos having poor sample data because it confuses the algorithm.

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This discipline in topic and audience selection is very important.

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And typically beginners that are starting out are spraying and praying all over the place and they don't have this.

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Okay.

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So that's one side of the equation.

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Very simply just narrow your topic and audience and your sample fit will go up.

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Now on the other side of the equation, the two part piece was making sure once you have that sample, that it actually engages well with your video.

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And that means they watch it, they like it, they save it, they share it, they comment, they repost all the engagement metrics,

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this side to make sure that initial sample data from these people comes back strong.

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Well, when the algorithm's gauging if it's strong or not, it's really only looking at three core metrics.

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The first one is average watch time.

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How long did someone watch in a number of seconds per video?

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And also by proxy percent completion, what percent of the video was completed on average?

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The second metric is engagement rate.

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So this is likes plus comments plus shares divided by views.

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And the third, which is really important there.

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In a session for a viewer, if they're on there for 60 minutes, how many of those 60 were spent watching your videos?

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That percentage is watch time, session share.

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You can't access this anywhere, but it's a critical metric that social algorithms use to know how influential your content is.

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So the million dollar question for you is how can you improve these metrics?

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What can you do in your video to make sure those metrics go up

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so the sample data comes back clean so that it just pushes your video to more people?

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Well, the short cheeky answer is if you want the metrics to go up, you just make better videos with better ideas, stronger hooks, better storytelling and more interesting visuals, obviously.

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But that's not helpful at all.

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So is there anything tactical you can do at a studs level to increase the effectiveness of those videos?

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And if you watch this channel a lot, you already know, of course there is.

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There's only four things you need to do to make your video better so that that engagement rate goes off the charts.

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Number one is that the topic needs to be relevant for the ideal viewer.

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It goes with the first piece I set.

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What you cover has to actually solve a problem that they have.

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If that's the case, engagement will go up.

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Number two, the information you share needs to be both non -obvious and tactically implementable.

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Is it new stuff they haven't heard before and can they actually use it to solve that problem?

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If those things are true, the engagement rate will go up.

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Number three, the viewer has to actually have a high absorption of the information you say.

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It can be on target and non -obvious, but if they can't actually understand what you're saying, then they can't apply it.

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So if they could apply it, the engagement rate will go up.

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And then number four, there needs to be a short distance to implement your recommendations.

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Tactically implementable means they can take a little bit of action and get a big result based on your promise.

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Now you won't typically hear people frame it in this way, but if your content If you have higher engagement,

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the data comes back more positive, they push it to more of the people, those people are people you want, and the flywheel spins.

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What this really means, those four things in layman's terms, you got to cover a core pain point or problem they have.

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You got to have something useful or interesting to say.

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You got to say it in a way they can actually understand, and they have to be able to take what you say and apply it on their own.

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Those are the four horsemen to driving good video performance.

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the algorithm to push you more.

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Pick an avatar, stick to it, narrow your topic selection, and then drive those four things home.

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When you do this, the sample group will stay dialed and they'll all engage with the video at a high rate.

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Incidentally, these four factors are also how you turn viewers into buyers.

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If you want people to buy, those four components also make sense to include in the video.

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They're kind of like the core DNA if you're trying to build a money machine with content.

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Now I'll say this, the easiest way to make sure you're picking the right topics

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that actually work for your avatar group is to just study the videos that are already working in your niche.

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It shocks me how few people actually do this, but in sandcastles .ai, you can build a group of competitor channels

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that are already crushing and just filter by outlier score to see all the best performing videos.

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There's now this feature where if you save the video to library, you can see all the the exact hook,

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the exact storytelling mechanics, everything about the video that drove the curiosity.

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You can then take that, remix it.

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Everything you need is right in there.

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So all you need to do if you're confused on which topics to pick for your avatar group, just go in Sandcastles and use this resource.

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It shocks me how few people are using data to make their topic decision.

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This basically guarantees that you're serving the right stuff to your audience.

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Now, before I end this video, I just want to include one more bonus topic around the algorithm because I know my Hopefully it made sense.

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Hopefully it helped you.

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You have action items to work on.

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But I just want to include one more thing at a tactical level

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that you can take away and really hammer value from this video.

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Another way to drive algorithmic push is to increase the number of comments on your video.

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Most people know this.

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There are five things you can do tactically to increase the number of comments you're getting.

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Number one is to take a hard stance on your topic.

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People typically comment when they violently agree or disagree, mostly disagree with whatever your stance is or perspective.

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If you play the middle and hedge, you're going to get fewer comments.

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So I recommend you pick a side pro or con and that will drive comments.

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Number two is to pick Pick the side that is the contrarian side.

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Like I said, people love to comment when they disagree, when they think you're wrong.

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If you pick the contrarian side, that means you think the majority of people are wrong, which means they'll think you're wrong.

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The majority of people will want to comment because they disagree with you.

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If you create more enemies, you drive more comments.

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Tip number three is to amplify the stance you take by ratcheting up the way you frame your points.

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If you said something like,

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this is the best way to cook which one is gonna drive a more violent discussion in the comments?

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The more extreme version, of course, always is.

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So that's how you ratchet up your stance.

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Tip number four is to build your topics around cult -loved brands, people, ideas, and movements.

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The more you talk about things people already have made up their opinion on, the faster they're willing to jump into the comments, especially if they disagree.

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For example, if you take a stance on Nike versus just the category of shoes, more people will have already made up their opinion whether they like or dislike Nike,

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Tip number five is to position your take or stance to drive significant emotion.

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The more people feel something when they watch your video, the more they're going to feel compelled to want to comment.

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Now, those five things around comment, that was just a little extra sprinkle to give you more tactics on how to drive activation

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and engagement to make the algorithm push you.

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All of those feed back to picking the right topic for the right group.

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So it's kind of like a sub point on what I just went through.

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Hopefully that's helpful and you can put that to use.

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All right, guys, that's all I've got for this video.

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As a recap, we covered a lot of ground.

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theoretically and fundamentally can hijack them to push your videos more.

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I tried my best to kind of demystify this black box and give you tactics that you can use to your advantage.

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As always, guys, I'm trying my absolute best to give you perspectives

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that most people don't cover in a tactical bite -sized way that you can actually put to work.

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What you typically see about the algorithm is talking about settings hacks or these little caption tweaks.

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None of that stuff actually works.

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If you think that actually works, you need to watch this video and your captions don't matter.

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The captions themselves don't matter.

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The only thing

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that matters is making great videos for a specific avatar group across a narrow band of topics over and over and over.

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That's the only thing that matters.

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That is the cake.

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Everything else is the icing.

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Focus on the cake.

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As a reminder, if you want access to my full content system, this is the exact blueprint I use with my own team.

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How I find ideas, how I write hooks, how I validate these things, how I do research, my entire funnel to turn viewers into dollars, Link below for free.

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My gift to you, shortformsystem .co.

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And if you have any other topics around social media growth, around content and content systems that you want me to cover or that you feel blocked on, just drop them in the comments.

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We use the comments to inform the database of videos that we make next.

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So anything you guys are stuck on, please put it in the comments.

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It will help greatly to inform what we should make.

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All right, guys, that's all we've got.

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We will see you on the next video.

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Peace.

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Woordenschat en spreektips bij deze les

Deze spreekles op niveau B2 is gebaseerd op de video “How Social Media Algorithms Actually Work”. Deze woorden komen het vaakst terug: algorithm, topic, sample, comment, content. Deze video bevat 237 zinnen en 3470 woorden om na te spreken. Het gesproken deel duurt 17:20. De spreker praat snel, ongeveer 200 woorden per minuut, dus reken op verbonden en ingeslikte klanken. 87% van de woorden hoort bij de 3.000 meest gebruikte Engelse woorden, dus de woordenschat is goed te volgen.

Belangrijke woorden in deze video

De 15 moeilijkste woorden uit de video, met uitspraak en betekenis:

Woord	Uitspraak	Betekenis
algorithm zelfstandig naamwoord	/ˈælɡəɹɪðm̩/	algoritme
avatar zelfstandig naamwoord	/ˌæv.əˈtɑː(ɹ)/	belichaming, avatar
metric bijvoeglijk naamwoord	/ˈmɛt.ɹɪk/	metriek
viewer zelfstandig naamwoord	/ˈvju.ɚ/	kijker, toeschouwer
stance zelfstandig naamwoord	/stæns/	houding
follower zelfstandig naamwoord	/ˈfɒləʊə(ɹ)/	volger, navolger
disagree werkwoord	/dɪsəˈɡɹi/	niet overeenkomen, niet overeenstemmen
caption zelfstandig naamwoord	/ˈkæp.ʃən/	titel, hoofding
hijack werkwoord	/ˈhaɪ.d͡ʒæk/	kapen
tactical bijvoeglijk naamwoord	/ˈtæktɪk(ə)l/	tactisch
fingerprint zelfstandig naamwoord	/ˈfɪŋɡɚˌpɹɪnt/	vingerafdruk
nutshell zelfstandig naamwoord	/ˈnʌt.ʃɛl/	notendop
outlier zelfstandig naamwoord	/ˈaʊtˌlaɪə(ɹ)/	buitenbeentje, alleenstaande
sandcastle zelfstandig naamwoord	/ˈsæn(d)ˌkæsəl/	zandkasteel
flop werkwoord	/flɑp/	instorten, inzakken

Phrasal verbs die je zult horen

Woord	Betekenis
come back werkwoord	terugkomen
break down werkwoord	stukgaan, kapot gaan
end up werkwoord	belanden, terechtkomen
go off werkwoord	afgaan
slow down werkwoord	afremmen

Zinnen om te herhalen

Korte, volledige zinnen uit de video die je in alledaagse gesprekken kunt gebruiken:

Today, we're talking about the social media algorithm.
That's what we're going to go through right now.
All right, let's talk sample groups.
All right, guys, that's all we've got.

Grammatica in deze video

De structuren die de spreker het meest gebruikt, met de exacte woorden uit de video:

Structuur	In de video
Voorwaardelijke zinnen if + zin, will/would + werkwoord — een voorwaarde en het gevolg	If that's the case, engagement will go
Lijdende vorm be + voltooid deelwoord — het gaat om wat er gebeurt, niet om wie het doet	is based · being said · is called
Present perfect have/has + voltooid deelwoord — iets uit het verleden dat nu nog telt	i've done · you've posted · have built
Betrekkelijke bijzinnen who / which + zin — extra informatie over een persoon of ding	score, which is · third, which is · wrong, which means

Uitspraak om op te letten

De spreker gebruikt 62 samentrekkingen en verkorte vormen, zoals you're, don't, they're. Spreek ze kort uit, zoals je ze hoort.

De “th”-klanken: algorithm /ˈælɡəɹɪðm̩/, algorithmic /ˌælɡəˈɹɪðmɪk/
De klanken “sh” en “zh”: caption /ˈkæp.ʃən/, nutshell /ˈnʌt.ʃɛl/, equation /ɪˈkweɪ.ʒən/, monetization /ˌmɒnətaɪˈzeɪʃən/, absorption /æbˈsɔɹp.ʃn̩/
Lange woorden — let op de klemtoon: contrarian /kənˈtɹɛ.ɹi.ən/, algorithmic /ˌælɡəˈɹɪðmɪk/, alienate /ˈeɪ.li.ə.neɪt/, demystify /diːˈmɪstɪfaɪ/, metadata /ˈmɛt.əˌdeɪ.tə/

Klanken die Nederlandstaligen lastig vinden:

Stemhebbende eindmedeklinker — /b/, /d/, /g/, /z/, /v/ niet verscherpen: analyze /ˈæn.əˌlaɪz/, prioritize /pɹaɪˈɒɹ.ə.taɪz/, hashtag /ˈhæʃˌtæɡ/, confuse /kənˈfjuːz/, stud /stʌd/
/æ/ — opener dan de Nederlandse “e”: algorithm /ˈælɡəɹɪðm̩/, avatar /ˌæv.əˈtɑː(ɹ)/, stance /stæns/, caption /ˈkæp.ʃən/, hijack /ˈhaɪ.d͡ʒæk/
/g/ — een harde plofklank, geen Nederlandse “g”: algorithm /ˈælɡəɹɪðm̩/, disagree /dɪsəˈɡɹi/, fingerprint /ˈfɪŋɡɚˌpɹɪnt/, algorithmic /ˌælɡəˈɹɪðmɪk/, beginner /bəˈɡɪnɚ/

Zo oefen je met deze video

Luister de hele video één keer zonder te spreken en noteer de woorden die je niet kent.
Begin op 0,75× snelheid, spreek zin voor zin na en ga terug naar normale snelheid zodra het makkelijk gaat.
Neem jezelf op en vergelijk met het origineel; let daarbij op woorden als algorithm, avatar, metric.

Wat is de Shadowing-techniek?

Shadowing is een wetenschappelijk onderbouwde taalleermethode die oorspronkelijk is ontwikkeld voor professionele tolkentraining en gepopulariseerd door polyglot Dr. Alexander Arguelles. De methode is eenvoudig maar krachtig: je luistert naar native Engelse audio en herhaalt het onmiddellijk hardop — als een schaduw die de spreker volgt met slechts 1–2 seconden vertraging. In tegenstelling tot passief luisteren of grammaticadrills, dwingt shadowing je hersenen en mondspieren om echte spraakpatronen tegelijkertijd te verwerken en te reproduceren. Onderzoek toont aan dat het de uitspraaknauwkeurigheid, intonatie, ritme, verbonden spraak, luisterbegrip en spreekvaardigheid aanzienlijk verbetert — waardoor het een van de meest effectieve methoden is voor IELTS Speaking-voorbereiding en echte Engelse communicatie.

Shadowing-techniek: lees de volledige stap-voor-stap-gids →

Shadowing Practice: How Social Media Algorithms Actually Work (And How to Beat Them) - Learn English Speaking with Video

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