Predicting IQ and Personalities

 3 things you will Learn
from this post:

~ Your Facebook data has
value, and bad guys are using it

~ We designed an
algorithm that used Facebook data to predict your IQ and personalities

~ By using Matrix
Completion technique in data preprocessing, we boosted our prediction by a lot!

 

‍Our digital footprint (the social media data) is huge. And our digital footprint is valuable, and extremely valuable to those big data companies. 

If you've listened to the news lately, you'd know that you might be one of the 87
million users
whose data was used by Cambridge
Analytica
for target advertising for
the Trump campaign
, and the data was accessed illegally.

 

And there are larger
implications about your data.

 

What can FB data predict
about you?

A lot, it turns out...

 

It can predict your
demographic information (i.e. your age), your psychographic information (i.e.
your personality), and your habits and background information, even your
relationship status. Isn’t that scary?

 

Before I talk about our
research, I want to establish what type of previous research is out there for
predicting attributes about rationality.
has introduced us this great scheme to use. It uses user-like sparse
matrix of Facebook to predict people user’s personality, age, and gender.

Their steps are as
follows:

~ Trim the user-like
sparse matrix to limit only Users with 50 + likes and (Facebook post) Likes
with 150 + likes

~ Reduce the dimension
of the data from high dimension (one hot encoding of Likes) to 50 linear
components using SVD

~ Use linear regression
to train, test, cross-validate, and predict personalities

 

 

Using Linear Regression, they were able
to predict the

scores (OCEAN score).   Each user here
took the big 5 personality test online, and received a score.

Here’s
the
if you
want to take it

 

Comparing its prediction
to its true score (which is based on their online test and is arbitrary),
gets a decent correlation
(r). It shows how well the prediction values correlate with the true data.

 

Here's how they perform.
And we are going to reintroduce them when we are showing our values

 

What we
are curious is that how can we improve its prediction? Should we use better
model? Or can we pre-process the data better?

Take a step back, and I
want to introduce you to my friend Joe. Joe is a fictional character that we
created. Although he’s my friend, I don’t know him very well. In fact, the only
information I have about him are the 26 Facebook posts that he clicked “Like”.

I know that he likes
posts such as “Coca-Cola”, “Fly the American Flag”, and “Disney”. So he get a
“1” on those cells.

 But we have no idea on Joe’s opinion on the
Beatles or Katie Perry. So he got a “0” on all other 36,000 cells.

 

Previously, those cell
has been treated as the value 0, meaning that Joe has seen every single of the
36,000 Likes but liked only 26 of them, that’s not a good way of coding it.

Since we do not know
whether Joe has seen the posts but not Liked it, or he has not seen it at all,
it’s better to err on the side that he hasn’t seen them at all. It’s not likely
that he has seen even 7,000 (5%) of those 36,000 specific posts.

 

Wouldn’t it be nice to
know what’s the chance that he like the other 36,000 posts?

 

And that’s exactly what
we did. With our algorithm, we predict that Joe has 39% probability to like
Katie Perry, 37% probability to like the Beatle, and only 1% chance to like the
Hayao Miyazaki, the anime god. But how did we do that?

 

Here we encounter the
problem of
. Sparse matrix is a
matrix that has a lot of “0”s. In this user-like matrix, 99.6% of the cells are
“0”.  But if we treat those “0”s as
missing data, how can we guess a value for each and impute them?

 

It is at this point that
we realize our problem is not unlike the
, a classic matrix completion
problem. Basically Netflix wanted to improve its movie recommendation
algorithm. So it throw out this massive sparse matrix of user-movie data, and
offered $1 million to the team that could best predict how users would rate
each of their unseen movies.

The winning team used a
statistical method called Alternate Least Squares (ALS).

 

 Here’s the algorithm (a YouTube tutorial) :

~ factoring (SVD) Matrix
R into matrix U for Users and matrix L for Likes

~ iteratively fill in
the U and L until error is minimized

~ Multiply U and L
together to get back R_imputed.

~ All the missing cells
are now imputed with a number between 0 - 1 (for probability of like)

 

A famous Stanford
professor by the name of Trevor Hastie wrote a package called
,  which revolutionized the computation and
accuracy to realize the ALS matrix completion.

He responded! 

 

The code is actually
really easy.  For the full
implementation, see this other blog post I write.

library(softImpute)
 M1 = as(M,"Incomplete") # change dgCMatrix type to incomplete
 fit = softImpute(M1, rank.max = 10,type = 'als', lambda = 15, trace.it = TRUE, maxit = 10)
 Mimp=complete(M1,fit)

 

So, how does our model
perform?

Just by this
pre-possessing step along, we are able to have an at least 10% improvement on
features Age, IQ, Openness, and Neuroticism.

 

So, what do you think
Joe’s IQ is? Does he have a low IQ, or a high IQ? Based on those likes.

 

Well, we predicted that
Joe has a low IQ (the blue line), one standard deviation (std) below of the
median IQ (the grey line)  of our sample.
And actually, Joe has one of the lowest IQ in our data (the green line) . His
IQ is only 87, two standard deviations from the median.

 

We can do the same for
all his personality traits. If you look at the arrow of the prediction, all are
pointing at the same direction as the true value.

It shows that if all use
all our predictions as the binary classifier of the features, we would have
predicted "correctly" that Joe is low in Openness, Extraversion, and
Conscientiousness. He is high on Neuroticism. We did not correctly predict that
he is low on agreeableness. 

 

The accuracy of this
classification is not a coincidence. It does not hold only for Joe. for
attributes for all users, 63% of the classifications were predicted correctly.

 

 

After becoming omniscient gods, what can we do with it?

Today, May 3, 2018, as
I’m writing this blog, Cambridge Analytica shut down, 2 months after the data
leak scandal.

 

We do not tolerate
companies like Cambridge Analytica to exploit social media data for voter
manipulation. But by correctly predicting your personality and intelligence
informations, Organization can also make the world a better place.

 

can enhance matching-making by
connecting people with compatible personalities

 

can email you coupon of the goods
you actually want to buy.

can connect people with charities whose cause they truly care about.

So, after seeing what the good guys and the
bad guys can do using your Facebook data, would you be willing to share it, or
hide it?