Hey, everyone.
My name is Hendrik Heuer, and I'm
from the University of Bremen in Germany.
And my name is Oscar Alvarado from KU Leuven in Belgium.
In this video, we will provide a brief summary of our paper
"Middle-aged Video Consumers Beliefs About Algorithmic
Recommendations on YouTube."
We present joint work together with Vero Vanden Abeele,
Andreas Breiter, and Katrien Verbert.
This paper presents a sociotechnical perspective
on YouTube's algorithmic recommendation system
and provides actionable insights on how
algorithmic recommendation systems can and should
be explained.
We identified a research gap regarding
users' beliefs about video recommendation
systems such as YouTube.
This is important since YouTube has more than 2
billion monthly users.
In addition to that, 70% of the videos watched on YouTube
are recommended by their algorithmic curation system.
We focused on users with three main characteristics.
They only consume videos.
They were middle-aged and did not
grow up with social media and algorithmic recommendations,
and they had high education levels but no formal training
in computer science or related disciplines.
In our study, we conducted 18 same-structured interviews.
To maximize diversity, we decided
to interview people in three different countries
with high levels of YouTube usage; six users from Germany,
six users from Belgium, and six users from Costa Rica.
Using thematic analysis, we identified a variety
of influence factors that users recognized
in the context of video recommendation systems.
We organized these influence factors into four user beliefs.
The previous actions beliefs referred
to the previous actions of the current user that
could have an influence on the recommended videos.
Users believed that actions like liking, sharing, commenting,
subscribing, or searching for a video
can influence their recommendations.
The social media belief refers to the influence
that other users could have on the video recommendations.
Here, other users of the platform
are the actors that influenced the recommendations.
Some examples of how users believe other users
influence video recommendations are
actions such as their viewing activity, their likes
and dislikes, and commenting on other videos.
The recommender system beliefs referred to the influence
that the algorithm has on the video recommendations.
This user belief relates to how the algorithm computes
the similarity between users and the similarity between videos.
In addition to that, the influence of context
was mentioned.
Finally, the company policy belief
relates to the actions of the organization that
operates the algorithmic curation system.
This includes beliefs that some recommendations are paid for,
the possible influence of data sharing
practices between different companies,
as well as psychological experts that
are hired to keep users on the platform to increase profit.
This is novel because the influence of the company that
operates the platform has not been recognized in prior work.
This is also noteworthy as influence factors like paid
recommendations and psychological experts
are associated with a strong negative emotion.
Read the paper to learn more about why recommendation
systems are complex sociotechnical systems that
entail a lot more than a specific algorithm.
We argue that the most urgent risk is not a lack of a word
but an insufficient understanding of machine
learning-based curation systems.
To address these challenges and to design for a better
experience, explanations could show
whether the algorithm, a user's actions, other users' actions,
or the organization that runs the platform are
responsible for the recommendations
a user receives.
Designers could add control features
to allow users to review and correct
how their watching history influences the personalized
recommendations.
We invite other researchers to continue studying the user
beliefs further for YouTube and other platforms
like Twitter or Instagram.
Thank you very much for watching.
See you soon.
Bye.