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

References

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