My name is Arunesh Mathur and I'm excited to talk to you about my dissertation
Over the past few years we have seen a rising tide of online manipulation
where powerful actors on the web use a variety of different tactics
to negatively influence users and subvert their decision decision-making process
While the phenomena of manipulation itself isn't new the architecture of the
web has allowed a variety of manipulative tactics to flourish and thus harm users
As a result, this has required research that examines these practices in more detail
In my dissertation I asked three research questions
What are the common manipulative practices on the web that manifest in the user interface
How can we identify and measure these user interfaces at scale
And what can we do to mitigate the spread of these user interfaces on the web
I present case studies that characterize and quantify three manipulative user interfaces
My research shows that it is possible to characterize and quantify these interfaces
because they share common traits
As a result we can use web automation and machine learning methods to identify them at scale
In the first study I examined the prevalence of undisclosed online endorsements
So what is an endorsement?
Here is one example where a YouTuber by the name of Gohan the Husky
is promoting a vacuum cleaner and encouraging his audience to purchase the cleaner
through a link in the description of the video
This is an example of an affiliate link -- a link that earns Gohan money
when people use it to buy the advertised product this
Of course is a perfectly lawful practice but various government regulations require
content creators like Gohan to disclose their relationship with advertisers to their audience
so that the audience is not misled into believing that they are watching non-advertising content
To quantify how often content creators like Gohan disclose
I assembled a data set of nearly half a million YouTube videos and two million Pinterest pins
I then resolved all the URLs present in these videos and pins
and then I used unsupervised machine learning methods to cluster the video and print
descriptions and then I extracted the clusters that corresponded to endorsement disclosures
I found that nearly 90 percent of all affiliate marketing endorsements
are not disclosed by content creators to their audience,
thus violating the FTC's guidelines as and other regulations around the world
The most prevalent type of disclosure
is also explicitly discouraged by the FTC and least understood by users
In the second study I examined the prevalence
of so-called dark pattern user interfaces in shopping websites
Dark patterns are user interfaces that benefit an online service
by influencing users into making decisions that they would otherwise not make
There exist a variety of dark pattern interfaces that have been characterized
and defined in various taxonomies that have been created by researchers
In this study I examined how often dark patents appeared in 11,000 of
the most popular shopping websites worldwide
To identify dark patterns at scale I developed a shopping bot
that visited websites and extracted pieces of text amongst other information
I then used unsupervised machine learning methods to cluster this content
and then extracted all those clusters that corresponded to dark patterns
I discovered nearly 1800 dark patterns on 1200 shopping websites
These together represent fifteen types and seven categories of dark patterns
Some of the dark patterns I discovered included fake countdown timers
and interfaces called trick questions that confuse users into giving up their privacy
In the third study I examined the prevalence of
dark patterns and clickbait in political campaign emails
Emails are an important part of how political candidates running for office
as well as political organizations raise money here in the United States
At the same time there has been a state of reporting describing how these emails often
contain manipulative and deceptive user interfaces
I quantified the prevalence of these interfaces in emails from
about 20,000 candidates running for office and organizations active in the 2020 election cycle
I developed a bot that automatically signed up for emails from these candidates
as well as organizations and I gathered a data set of nearly 100,000 political emails
I developed machine learning classifiers to automatically
detect and quantify manipulative user interfaces in these emails
I found that nearly 40 percent of all the emails from the median sender
contained at least one manipulative interface some centers largely avoided these tactics
while others used them very very often
Some of the dark patterns I discovered in campaign emails included
those that obscured the sender's name to users thus creating a sense of curiosity as
well as clickbait techniques that create a false sense of urgency to drive users to donate money
To conclude my dissertation makes the following contributions
It presents automated data collection and analysis techniques
to measure manipulative user interfaces at scale
It contributes data sets recording every instance of these manipulative user interfaces
And it shows that the scale of measurement enables new
insights into what these user interfaces look like
I have communicated the findings of my research to various policymakers such as the Federal
Trade Commission, the Advertising Standards Agency. and the Norwegian Consumer Council
These agencies have since released reports and guidelines
to rein in on the user interfaces that I described in my dissertation
I've also released code and data from my research so that other researchers
can build upon the research that I present in here
Please feel free to reach out to me if you are interested in
learning more about the studies in my dissertation
Thank you so much for listening