Women have been telling Twitter for years that they endure a lot of
abuse on the platform. A new study from human rights watchdog Amnesty
International attempts to assess just how much. A lot, it turns out.
Amnesty
International is a global movement of more than 7 million people in
over 150 countries and territories who campaign to end abuses of human
rights.
About 7 percent of the tweets prominent women in
government and journalism receive were found to be abusive or
problematic. Women of color were 34 percent more likely to be targets
than white women. Black women specifically were 84 percent more likely
than white women to be mentioned in problematic tweets.
After an
analysis that eventually included almost 15 million tweets, Amnesty
International released the findings and in its report, described Twitter
as a “toxic place for women.” The organization, which is perhaps best
known for its efforts to free international political prisoners, has
turned its attention to tech firms lately, and it called on the social
network to “make available meaningful and comprehensive data regarding
the scale and nature of abuse on their platform, as well as how they are
addressing it.”
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“Twitter
has publicly committed to improving the collective health, openness,
and civility of public conversation on our service,” Vijaya Gadde,
Twitter’s head of legal, policy, and trust and safety, said in a
statement in response to the report. “Twitter’s health is measured by
how we help encourage more healthy debate, conversations, and critical
thinking. Conversely, abuse, malicious automation, and manipulation
detract from the health of Twitter. We are committed to holding
ourselves publicly accountable towards progress in this regard.”
Together
with Montreal-based AI startup Element AI, the project called “Troll
Patrol” started by looking at tweets aimed at almost 800 female
journalists and politicians from the U.S. and the U.K. It didn’t study
men. More than 6,500 volunteers analyzed 288,000 posts and labeled the
ones that contained language that was abusive or problematic (“hurtful
or hostile content” that doesn’t necessarily meet the threshold for
abuse).
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Each
tweet was analyzed by three people, according to Julien Cornebise, who
runs Element’s London office, and experts on violence and abuse against
women also spot-checked the volunteers’ grading. The project also wanted
to use those human judgments to build and test a machine-learning
algorithm that could flag abuse—in theory, the kind of thing a social
network like Twitter might use to protect its users.
Cornebise’s
team used machine learning to extrapolate the human-generated analysis
to a full set of 14.5 million tweets mentioning the same figures. They
also made sure the tweets examined by the volunteers were representative
and that the findings were accurate. Then his team used the data
created to train an abuse-detecting algorithm and compared the
algorithm’s conclusions to those of the volunteers and experts. This
kind of work is becoming increasingly important as companies
like Facebook Inc. and YouTube use machine learning to flag content that
needs moderation. In a letter responding to the Amnesty International
report, Twitter has called machine learning “one of the areas of
greatest potential for tackling abusive users,” the group said in the
report.
The algorithm Cornebise’s team built did pretty well,
he said, but not well enough to replace humans as content moderators.
Instead, machine learning can be one tool that helps the people in these
jobs. Defining abuse often requires an understanding of context or how
words are interpreted in certain parts of the world—judgement calls that
are harder to teach an algorithm.
“Abuse is itself very
contextual and perception of abuse can vary from region to region,” he
said. “There is too much subtlety and context and algorithms can’t solve
that yet.” Perhaps the women of Twitter could help them out.
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