For
YouTube viewers dissatisfied with the videos the platform has recommended
to them, pressing the “dislike” button may not make a big difference, according
to a new research report.
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YouTube has said users have numerous ways to
indicate that they disapprove of content and do not want to watch similar
videos. But all of those controls are relatively ineffective, researchers at
the Mozilla Foundation said in a report published Tuesday. The result was that
users continued receiving unwanted recommendations on YouTube, the world’s
largest video site.
Researchers found that YouTube’s “dislike” button
reduced similar, unwanted recommendations only 12 percent, according to their
report, titled “Does This Button Work?” Pressing “Don’t recommend channel” was
43 percent effective in reducing unwanted recommendations, pressing “not
interested” was 11 percent effective and removing a video from one’s watch
history was 29 percent effective.
The researchers analyzed more than 567 million
YouTube video recommendations with the help of 22,700 participants. They used a
tool, RegretReporter, that Mozilla developed to study YouTube’s recommendation
algorithm. It collected data on participants’ experiences on the platform. But
the participants were not representative of all YouTube users because they
voluntarily downloaded the tool.
Jesse McCrosky, one of the researchers who conducted
the study, said YouTube should be more transparent and give users more
influence over what they see.
“Maybe we should actually respect human autonomy and
dignity here, and listen to what people are telling us, instead of just
stuffing down their throat whatever we think they’re going to eat,” McCrosky
said in an interview.
YouTube defended
its recommendation system. “Our controls do not filter out entire topics or
viewpoints, as this could have negative effects for viewers, like creating echo
chambers,” Elena Hernandez, a spokesperson for YouTube, said in a statement.
“Mozilla’s report doesn’t take into account how our systems actually work, and
therefore it’s difficult for us to glean many insights.”
YouTube also said its own surveys have shown that
users were generally satisfied with the recommendations they saw, and that the
platform has tried to not prevent recommendations of all content related to a
topic, opinion, or speaker. The company also said it was looking to collaborate
with more academic researchers under its researcher program.
One research participant asked YouTube on January 17
not to recommend content like a video about a cow trembling in pain, which
included an image of a discolored hoof. On March 15, the user received a
recommendation for a video titled “There Was Pressure Building in This Hoof,”
which again included a graphic image of the end of a cow’s leg. Other examples
of unwanted recommendations included videos of guns, violence from the war in
Ukraine, and
Tucker Carlson’s show on Fox News.
The researchers also detailed an episode of a
YouTube user expressing disapproval of a video called “A Grandma Ate Cookie
Dough for Lunch Every Week. This Is What Happened to Her Bones.” For the next
three months, the user continued seeing recommendations for similar videos
about what happened to people’s stomachs, livers, and kidneys after they
consumed various items.
“Eventually, it always comes back,” one user said.
Ever since it developed a recommendation system,
YouTube has shown each user a personalized version of the platform that
surfaces videos its algorithms determine viewers want to see based on past
viewing behavior and other variables. The site has been scrutinized for sending
people down rabbit holes of misinformation and political extremism.
In July 2021, Mozilla published research that found
that YouTube had recommended 71 percent of the videos that participants had
said featured misinformation, hate speech and other unsavory content.
YouTube has said its recommendation system relies on
numerous “signals” and is constantly evolving, so providing transparency about
how it works is not as easy as “listing a formula.”
“A number of signals build on each other to help inform our
system about what you find satisfying: clicks, watch time, survey responses, sharing,
likes, and dislikes,” Cristos Goodrow, a vice president of engineering at
YouTube, wrote in a corporate blog post last September.
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