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posted by on Monday February 13 2017, @06:11AM   Printer-friendly
from the sounds-familiar dept.

A joint study carried out by researchers from Alphabet's Jigsaw and the Wikimedia Foundation has analyzed all user comments left on Wikipedia in 2015 in order to identify how and why users launch in personal attacks, one of the many faces of online abuse. To analyze the gigantic trove of sample comments, researchers developed a machine learning algorithm that was able to identify and distinguish different forms of online abuse and personal attacks. In order for the algorithm to work, it had to be trained beforehand. For this, researchers used human users to classify a small batch of 100,000 comments, with each of the test comments passing through the hands of ten different humans. The resulted data classification allowed the algorithm to accurately distinguish between direct personal attacks (statements like "You suck!"), third-party personal attacks (statements like "Bob sucks!"), and indirect personal attacks (statements like "Henry said Bob sucks").

After training the algorithm and unleashing it on all Wikipedia 2015 user comments, researchers were able to identify personal attacks, and then collect data on the users that launched them. Their findings reveal that around 43% of all comments left on Wikipedia came from anonymous users, but most of these were one-time commenters, and the number of comments they left was 20 times smaller than comments left by registered users. Despite this, researchers discovered that anonymous users were six times more active in posting personal attacks, but in the end, they contributed to less than half of personal attacks, meaning a large number of personal attacks came from users with a registered identity on the site.

Of all personal attacks, researchers noted that about a tenth came from extremely active users, who had an activity level of 20+, the highest on the site. A closer look at the data revealed that 34 "highly toxic users" from this 20+ category were responsible for almost 9% of all personal attacks on the site. "By comparing these figures, we see that almost 80% of attacks come from the over 9000 users who have made fewer than 5 attacking comments," the research team noted, something that's somewhat normal, as everybody tends to get mad at one point or another. "However, the 34 users with a toxicity level of more than 20 are responsible for almost 9% of attacks," researchers noted.

Source:

https://www.bleepingcomputer.com/news/security/wikipedia-comments-destroyed-by-a-few-highly-toxic-users/


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  • (Score: 2) by AthanasiusKircher on Monday February 13 2017, @07:06PM

    by AthanasiusKircher (5291) on Monday February 13 2017, @07:06PM (#466707) Journal

    I'm not sure that 'real name' accounts behave better.

    They do overall. Even your link supports this, noting a 30% aggregate reduction in "swearing and 'anti-normative' behavior" after real-name policies were introduced. To be clear, I'm NOT arguing in favor of real-name policies (which I think also have many problems) or necessarily arguing against anonymous commenting.

    The general pattern (at least from the articles I've seen) is that real names or pseudonyms tends to embolden a small percentage of users who like to actually be associated with their troll-like behavior. On average, though, real-name or pseudonymous users as a group tend to be LESS likely to "misbehave" than anonymous users. That's exactly what we see again in this Wikipedia study: overall, anonymous users are significantly more likely to make personal attacks, but there are also a small number of "heavy" users who ARE logged in and nevertheless have a disproportionate share of the bad comments.

    This is truly basic psychology. Most people tend to conform to social norms and they have a stronger incentive to do so when their reputation follows them. But for a small fraction (i.e., sociopathic trolls), they actually love the negative attention.

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