Your dog or cat or...hamster is cute.
But have you seen my fish??
Just look at those shimmering fins!
And I swear she swims to the beat of all my 90s hip hop jams.
We have to tell the world!
And now it’s easier than ever.
I’ve got plenty of her dancing montages on my phone, so first we head to YouTube to upload the video.
Then we share the video with our friends on social media.
A few “ha ha”s and “lol”s start to trickle in.
Our friends share it.
Their friends share it.
And before we know it our phone starts buzzing with all of the notifications.
Guess we’re social media “influencers” now.
Is this what it feels like to go viral?
Actually, it wasn’t all us.
Behind the screen, data drives everything on social media sites.
We willingly exchange our data — and the data of our network — for entertainment.
And it doesn’t just get used to help our fish ride the waves of fame and fortune, but
also to create ads and even do behavioral research.
I’m Jessica Pucci and welcome to Study Hall Data Literacy presented by Arizona State University
and Crash Course.
Social media platforms, like Twitter, YouTube, Instagram, and Facebook, work hard to curate
the content we see and entice us to interact with their content even more.
To do that, every social media platform relies on data.
Like, we hear a lot about mysterious algorithms.
And to be fair, many of them are shrouded in secrecy and layers of programming and code.
But applying an algorithm is just a fancy way of following instructions (potentially
many, many instructions) or a set of rules in a particular order.
It can sound like social media algorithms are super complicated and hard to understand,
but the basic idea is simple:
they’re rules that take what we like and use that to recommend more content.
Like when Instagram suspects that yeah, we would like to follow those 5 dancing animal
accounts, thank you.
But how do they know?
The algorithm can’t read minds, but it does do a good job of reading behavior.
Each social media platform has access to every click we make on their site and even how we got there.
Maybe we searched “grooving with Nemo” on Google or clicked the “Follow Me!”
link on that hip-hop halibut blog and that’s what took us to YouTube.
Each link we share, video we watch, or photo we heart is an interaction, or digital engagement,
that becomes a data point in the social media site’s database.
But what we engaged with isn’t the only data that gets recorded.
The social media site also knows when we engaged with the content and even where in the world we are.
They also know what other content and which other users we engaged with while on the site.
Your digital engagements plus the when, where, and who are used to train or teach the algorithm what we like.
Let’s see how it works in the Data Point.
No, the algorithm isn’t smart enough to know that you like funny fish videos,
but it does see that you click on videos with “funny” and “fish” in the title a
lot and watch the videos the whole way through.
Your actions show the algorithm what you’re willing to spend time watching.
And what you’re not. A sloth video? Ugh, so boring.
Yet the algorithm has no idea what a funny fish is — or that you think sloths are a snooze.
The video titles might as well be arbitrary symbols, and you like clicking on blue dots
but avoid red triangles at all costs.
Each video is defined by features or little data summaries — like words in the title
or tags, the length of the video,
or even info on the video maker and their other uploads — that get stuffed into the
algorithm and compared with new videos to guess what you might like to see next.
Profiles of features that show up in important videos —
like ones you watch the whole way through or videos you quickly skip —
form the algorithm’s training set and establish rules for your recommendations.
Like this video has similar features to the funny fish videos:
it’s posted by the same user whose video you just watched, the title is similar to
videos you’ve been watching lately, and its description mentions a song you just streamed.
Seems like a winner.
The algorithm will up-weight the chances you’ll like this video and put it in a prime spot in your feed.
And when you watch it and share with a friend, that reinforces the rules about what you like
and the process starts over.
Now this one...it’s posted by the same user, but it also shares a lot of features with
that sloth video you quickly shut off.
Down-weight, down-weight, down-weight!
What we like and dislike is valuable data, especially combined with the millions of other
users’ data -- like what they watched before or after something you liked.
That’s how we get those “people who liked this video also liked these three videos” recommendations.
Platforms like Twitter make this recommendation explicit.
You discover users you find interesting and “follow” them.
Then you get to see not just their tweets, but their likes, retweets, and replies.
Since these algorithms play a huge role in what content hits our various feeds, it’s
helpful to have at least a general sense of how they work.
Plus, if we know how we engage affects future recommendations, we can be wary of getting
stuck in “bubbles” where we never see viewpoints that are different from ours.
But at the same time, social networks, or the set of connections between people online,
are the foundation of social media platforms.
Depending on the size and density of our social networks, online conversations can spread
information very quickly and widely.
Related social posts can make a particular topic start “trending,” or be widely referenced,
if enough people join the conversation.
That also means that misinformation can spread before it has the time to be fact-checked,
debunked, or removed from the site.
We all think we’ll be data literate enough to know better — and maybe by the end of
this series we will be!
But spreading misinformation doesn’t have to happen as part of some malicious scheme.
Early in 2020, a study found that some celebrities were “super spreaders” of COVID-19-related
fake news even if they were trying to be helpful.
Lots of celebrities have tons of followers, so if they share a study without critically
evaluating it, falsehoods can circle the globe in just a click.
Harmful content like this that goes viral, or spreads through social networks quickly
and tallies a ton of views, definitely reminds us of the disease kind of viral.
And social media can be fun -- like watching two dudes experience Phil Collins for the
first time or literally anything where babies meet puppies.
But every “like” or “share” amplifies a post and is an opportunity to use our data
literacy skills for good.
It’s important to not just add to the noise, and to look for the original source of the
information and who else has shared it.
All that noise, though, and how it spreads across social networks can be revealing about
parts of society and social spaces.
Social scientists, data analysts, and other researchers use social media data to study
digital behavior and connectivity.
In 2016, Facebook celebrated its 12th birthday by testing the old adage that there are six
degrees of separation between people.
Yes, this idea even predates the Kevin Bacon version.
This means that given two random people, we can find a chain of friendships that connects
them with only six handshakes (virtual or otherwise).
So using all the Facebook data at their disposal, an in-house research team crunched the friendship pairs.
Turns out, we might be more tightly knit than we thought!
In the U.S., each pair of Facebook users are connected by about 3.46 degrees.
Although one could argue that a Facebook “friend” is not really the same as a “friend” IRL.
No we don’t have 0.46 of a connection hanging around.
These numbers represent an average — some pairs will have a shorter path between them,
and some will have a longer path.
Like two people geographically far apart probably have a longer path than two people in the
same neighborhood.
If you don’t work for a social media giant, you can still access this data for research
— and many academics do by sharing their findings with the social media companies.
Or if you have a lot of cash burning a hole in your pocket, you can also buy additional access.
Twitter data is especially popular among researchers because many tweets and accounts are public.
But the important thing to remember about social media research is that not everyone
is on social media.
According to an early 2019 Pew Research Center survey, about 73% of US adults have used YouTube,
69% have used Facebook, and just 22% have used Twitter.
Which means social media users might not make good samples of the world population.
So we should take those degrees of separation with a grain of salt.
We have to decide if we think people who aren’t on Facebook or Twitter are more or less connected
than the average social media user.
On one hand, someone off the grid probably doesn’t have many connections on or off screen.
But on the other hand, a Tik Tok aficionado may be “over” Facebook.
Forming these tight(er) knit social networks is one way social media platforms use data
to get us to engage more on their site.
But why do they want our engagement so badly anyway?
Well, in some ways, they can turn us into money.
Social media companies know a lot about us.
They can combine the first-party data that they’ve collected themselves with second-
and third-party data gathered
by business partners to get an even more complete picture of their users.
And advertisers want part of the action.
Just like you can buy ad space in a print newspaper or reserve time for a commercial
during the Olympics, you can buy ad space on a website.
The more traffic a site gets, the more valuable its ad space.
More engagement means more potential for advertising revenue.
Like if someone puts an ad on our fish video before our big break and we get 2 million
views, that’s amazing exposure for whatever they’re selling.
Advertisers don’t see individual profiles, but they do get access to aggregated data
about groups of users that share characteristics or preferences.
Then they pay sites to show targeted ads specifically to relevant groups.
So a jewelry company will want its ads shown to groups of people who engage frequently
with luxury content.
When we get ads for products we’re interested in or already like, maybe we’re not too
worried about all this data sharing.
The algorithm has probably upweighted features that relate to pets from all of our video
watching, so pet store ads start popping up as we’re online.
But hey, that led to finding a nice aquarium on sale.
But when ads get oddly specific, we can feel weirded out.
Something about being shown a custom ballet bar for our aquarium or fish-sized waterproof
headphones seems rather, well fishy.
It’s definitely a fine line to walk.
Advertisers have learned to be a bit subtler though, so if we see an ad that seems kinda
relevant, we might want to think about how our behavior online may have caused it to pop up.
It turns out social media isn’t just built on memes; it’s also built on data.
Today we learned that our interactions with social media sites become data.
And that data is then used to tailor what we see: content-wise and ad-wise.
But that data can also inform research about social networks and how information spreads.
This time we were all about sharing with our friends; next time we’ll keep our data to
ourselves and talk about data privacy.
Thanks for watching Study Hall: Data Literacy, which is produced by Arizona State University
and the Crash Course team at Complexly.
If you liked this video and want to keep learning with us here in Study Hall, be sure to subscribe.
You can learn more about ASU and the videos produced by Crash Course in the links in the description.
See you next time!