Cmp: Handful of “highly toxic” Wikipedia editors cause 9% of abuse on the site

[Razer]

Sounds familiar somehow...

"We've all heard anecdotes about trolling on Wikipedia and other social
platforms, but rarely has anyone been able to quantify levels and
origins of online abuse. That's about to change. Researchers with
Alphabet tech incubator Jigsaw worked with Wikimedia Foundation to
analyze 100,000 comments left on English-language Wikipedia. They found
predictable patterns behind who will launch personal attacks and when.

The goal of the research team was to lay the groundwork for an automated
system to "reduce toxic discussions" on Wikipedia. The team's work could
one day lead to the creation of a warning system for moderators. The
researchers caution that this system would require more research to
implement, but they have released a paper with some fascinating early
findings.

To make the supervised machine-learning task simple, the Jigsaw
researchers focused exclusively on ad hominem or personal attacks, which
are relatively easy to identify. They defined personal attacks as
directed at a commenter (i.e., "you suck"), directed at a third party
("Bill sucks"), quoting an attack ("Bill says Henri sucks"), or just
"another kind of attack or harassment." They used Crowdflower to
crowdsource the job of reviewing 100,000 Wikipedia comments made between
2004-2015. Ultimately, they used over 4,000 Crowdflower workers to
complete the task, and each comment was annotated by 10 different people
as an attack or not.

Once the researchers had their dataset, they trained a logistic
regression algorithm to recognize whether a comment was a personal
attack or not. "With testing, we found that a fully trained model
achieves better performance in predicting whether an edit is a personal
attack than the combined average of three human crowd-workers," they
write in a summary of their paper on Medium.
Who is launching personal attacks?

The researchers unleashed their algorithm on Wikipedia comments made
during 2015, constantly checking results for accuracy. Almost
immediately, they found that they could debunk the time-worn idea that
anonymity leads to abuse. Although anonymous comments are "six times
more likely to be an attack," they represent less than half of all
attacks on Wikipedia. "Similarly, less than half of attacks come from
users with little prior participation," the researchers write in their
paper. "Perhaps surprisingly, approximately 30% of attacks come from
registered users with over a 100 contributions." In other words, a third
of all personal attacks come from regular Wikipedia editors who
contribute several edits per month. Personal attacks seem to be baked
into Wikipedia culture.

The researchers also found that an outsized percentage of attacks come
from a very small number of "highly toxic" Wikipedia contributors. A
whopping 9% of attacks in 2015 came from just 34 users who had made 20
or more personal attacks during the year. "Significant progress could be
made by moderating a relatively small number of frequent attackers," the
researchers note. This finding bolsters the idea that problems in online
communities often come from a small minority of highly vocal users.

The algorithm was also able to identify a phenomenon often called the
"pile-on." They found that attacking comments are 22 times more likely
to occur close to another attacking comment. "Personal attacks cluster
together in time," the researchers write. "Perhaps because one personal
attack triggers another." Though this shouldn't be surprising to anyone
who has ever taken a peek at Twitter, being able to quantify this
behavior is a boon for machine learning. It means that an algorithm
might be able to identify a pile-on before it really blows up, and
moderators could come in to de-escalate before things get really ugly.

Depressingly, the study also found that very few personal attacks are
moderated. Only 17.9% of personal attacks lead to a warning or ban.
Attackers are more likely to be moderated if they have launched a number
of attacks or have been moderated before. But still, this is an abysmal
rate of moderation for the most obvious and blatant form of abuse that
can happen in a community.

The researchers conclude their paper by calling for more research.
Wikipedia has released a dump of all talk-page comments to the site
between 2004-1015 via Figshare, so other researchers will have access to
the same dataset that the Jigsaw team did. Understanding how attacks
affect other users is urgent, say the researchers. Do repeated attacks
lead to user abandonment? Are some groups attacked more often than
others? The more we know, the closer we get to having good tools to aid
moderators. Such tools, the researchers write, "might be used to help
moderators build dashboards that better visualize the health of
Wikipedia conversations or to develop systems to better triage comments
for review."

With linkage:
https://arstechnica.com/information-technology/2017/02/one-third-of-personal-attacks-on-wikipedia-come-from-active-editors/

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