Tiktok, formerly known as Music.ly, has become an incredibly popular form of social media
for its relatability and authenticity.
Like all great things, it has its fair share of controversy and opaqueness.
Reddit’s CEO Steve Huffman and Facebook’s Sheryl Sandberg both have their doubts with
how the app stores and uses user data, with the US government even opening a national
security investigation into it.
Part of this fear stems from how shrouded Bytedance, the parent company of Tiktok, is
with their methods.
With the amount of money, exposure and fame tied to making it big on the app, it’s more
important than ever to really understand how virality algorithms actually work.
I recently came across a brilliant video on my feed by the user @Benthamite looking into
Tiktok’s supposed beauty algorithm.
In his clip he proposes that Tiktok rates videos for potential success based on the
attractiveness of those in it.
He also suggests a paper by Xie Et al on developing an algorithm to assess human beauty.
Algorithms to assess human beauty are nothing new.
There are multiple from different Chinese universities all using the same dataset and
building upon each other to produce more refined algorithms.
For years, machine learning algorithms have been able to be trained by humans to differentiate
between attractive and unattractive.
It’s something humans can do incredibly easily because it takes up a relatively large,
primal portion of our brain called the Fusiform Face Area.
We’re so good at it we rate faces in inanimate objects, aka Pareidolia.
For a machine however, pictures of faces are just pixels.
Facial recognition was achieved in the early 2000’s and typically use Histograms of Oriented
Gradients (HOG) where the processor detects sharp differences in pixel values as a facial
outline or feature.
This creates a bounding box that eliminates the useless background and saves on memory
usage enough such that your phone can even do it in realtime.
How does this relate back to Tiktok?
With a basic understanding of how computers see faces, lets look at how scientists or
more correctly, anthropometrists, the researchers who measure differences across ethnicities,
see faces.
In the exact same way actually.
It’s easy to see how identifying certain landmarks of the face, such as say nose width
(alar base width) or forehead inclination, you can assess how far their measurements
deviate from the average for their race.
As explained in virtually every video, averageness or Koinophillia is one of the four fundamental
traits for attractive faces.
In fact, using these landmarks, forensic anatomists can actually predict the ethnicity of deceased
remains, which I’ll cover in a future video.
By allowing the algorithm to measure and then evaluate the degree of fit with these ideal
averages, it can provide a ‘beauty score.’
A human can then evaluate if this result is accurate or not or how accurate the bounding
box was at selecting the face, and the machine can tune it’s parameters and try again.
This is the basis of facial machine learning.
With Bytedance being based in a country with abundant labour and now plentiful user data,
it’s no surprise that their proprietary algorithm would be very accurate at picking
winners.
Tiktok’s algorithm is expected to work in a similar way.
You give it a 2D input, it takes the geometric features of the face, compares them to anthropometric
ideals.
It also samples skin texture at points that most people have blemishes or irregularities
in and provides a combined score of the two.
However, instead of using a human rater to train it, it uses a deep learning neural network,
where you identify a few top level features of a pretty face and let the computer associate
similar aspects it thinks might matter as it works its way down.
Benthamite from Tiktok, has actually uploaded a similar algorithm which he found online
on TiktokBeautiful.com.
Using computer generated images from thispersondoesnotexist.com it gave a tight range of scores from 2.5 to
3.5 out of 5, which is to be expected because that’s where the bell curve of attractiveness
lies for most people.
Only 1 in a 100 or so would actually get a 4 or 5.
I’d agree with the ratings and say they’re pretty accurate.
In Benthamite’s video he argued that the study is based on white and asian faces which
is also true and fails to account for darker skin tones.
This is likely because of the skin sampling portion of the score where darker skin has
a greater range of contrast that the algorithm penalizes.
Also more obviously, the system isn’t trained with African anthropometric values so it’s
comparing them to Caucasian and Asian ones.
After reaching out to him, he confirmed it used the neural network one proposed in the
paper which uses a number of feature maps taken from attractive faces and compares how
your face is different to it.
What surprised me about the algorithm is the ability to measure skin smoothness and health.
In a previous video I covered the scientific importance of healthy skin and Xie Et al’s
algorithm does a pretty good job of identifying it.
This lady has deep nasolabial folds and uneven tones near her paranasal which rated considerably
lower than this young girl with very even tone and virtually no age related lines.
Moving back to Tiktok, it’s not clear how or how strongly this beauty score affects
video popularity.
I’d guess that much like Youtube’s algorithm, when you upload a video, the machine learning
process looks at a number of parameters, such as watch time, views, likes and comments in
a very short time frame, such as within the first 1 to 2 hours.
Videos with a greater momentary click through rate and engagement, are obviously catching
eyes and worth promoting to the trending page, or in Tiktok’s case, the for you page.
Both systems try to optimize viewership and only promote high quality content, because
views equals money.
It’s likely that this beauty score acts as one of the parameters on top of the rest
of watch time, views, etc and could help explain why everyone on your For You page appears
to be so disproportionately pretty.
Also, these creators get greater views by virtue of simply being eye candy, which the
Tiktok machine learning algorithm, overtime, would recognise as ‘high quality content’.
When so much of the platform is based on the creator’s personality, being attractive
does help them get their foot in the door.