I've started a thread on our "Revision scoring as a service" talk page regarding labeled conversation datasets & modeling work we could do.
See https://meta.wikimedia.org/wiki/Research_talk:Revision_scoring_as_a_service#...
On Sun, Nov 15, 2015 at 12:41 PM, Risker risker.wp@gmail.com wrote:
I am going to quote Joseph Reagle, who responded to a similarly titled threat on Wiki-en-L:
date:13 November 2015 at 13:48
It's been great that Riot games has had someone like Lin (an experimental psychologist) to think about issues of community and abuse. And I appreciate that Lin has been previously been so forthcoming about their experiences and findings.
But the much trumpeted League of Legends Tribunal has been down "for maintenance" for months, even before this article was published, with much discussion by the community of how it was broken. On this, Riot and Lin have said nothing.
Copying Joseph in case he wants to respond to some of the discussions here.
Risker/Anne
On 15 November 2015 at 10:36, Pharos pharosofalexandria@gmail.com wrote:
The figure quoted is quite interesting, but do we have a comparable
metric
for the Wikimedia projects? :
"... incidences of homophobia, sexism and racism ... have fallen to a combined 2 percent of all games"
2% sounds "low", but do we indeed know if this is better or worse than
us?
Would our comparable metric be the % of bigoted comments per article, per talk page discussion, per time that an editor spends at the project? I would think that encountering bigoted comments on 1 in 50 discussions
would
still be pretty significant.
Thanks, Pharos
On Sun, Nov 15, 2015 at 1:21 PM, Ziko van Dijk zvandijk@gmail.com
wrote:
Hello,
Just yesterday I had a long talk with a researcher about how to define and detect trolls on Wikipedia. E.g., whether "unintentional trolling" should be included or not.
In my opinion, it is not possible to detect by machine trollism, unkindness, harassment, mobbing etc., maybe with the exception of swear words. A lot of turntaking, deviation from the topic and other phenomena can be experienced by the participants as positive or as negative. You might need to ask them, and even then they might not be aware of a problem that works through in subtlety. Also, third persons not involved in the conversation can be effected negatively (look at ... page X... and you know why you don't like to contribute there).
Kind regards Ziko
2015-11-15 17:40 GMT+01:00 Katherine Casey <
fluffernutter.wiki@gmail.com
:
I'd be happy to offer my admin/oversighter experience and knowledge
to
help
you develop the labeling and such, Aaron! I just commented on
Andreas's
proposal on the Community Wishlist, but to summarize here: I see a
lot
of
potential pitfalls in trying to handle/generalize this with machine learning, but I also see a lot of potential value, and I think it's something we should be investigating.
-Fluffernutter
On Sun, Nov 15, 2015 at 11:32 AM, Aaron Halfaker <
ahalfaker@wikimedia.org>
wrote:
The League of Legends team collaborated with outside scientists to analyse their dataset. I would love to see the Wikimedia
Foundation
engage
in a similar research project.
Oh! We are! :) When we have time. :\ One of the projects that I'd
like to
see done, but I've struggled to find the time for is a common talk
page
parser[1] that could produce a dataset of talk page interactions.
I'd
like
this dataset to be easy to join to editor outcome measures. E.g.
there
might be "aggressive" talk that we don't know is problematic until
we
see
the kind of effect that it has on other conversation participants.
Anyway, I want some powerful utilities and datasets out there to
help
academics look into this problem more easily. For revscoring, I'd
like
to
be able to take a set of talk page diffs, have them classified in
Wiki
labels[2] as "aggressive" and the build a model for ORES[3] to be
used
however people see fit. You could then use ORES to do offline
analysis
of
discussions for research. You could use ORES to interrupt the a
user
before saving a change. I'm sure there are other clever ideas that
people
have for what to do with such a model that I'm happy to enable it
via
the
service. The hard part is getting a good dataset labeled.
If someone wants to invest some time and energy into this, I'm happy
to
work with you. We'll need more than programming help. We'll need a
lot of
help to figure out what dimensions we'll label talk page postings by
and to
do the actual labeling.
- https://github.com/Ironholds/talk-parser
- https://meta.wikimedia.org/wiki/Wiki_labels
- https://meta.wikimedia.org/wiki/ORES
On Sun, Nov 15, 2015 at 6:56 AM, Andreas Kolbe jayen466@gmail.com
wrote:
On Sat, Nov 14, 2015 at 9:13 PM, Benjamin Lees <
emufarmers@gmail.com>
wrote:
> This article highlights the happier side of things, but it
appears
> that Lin's approach also involved completely removing bad
actors:
> "Some players have also asked why we've taken such an aggressive > stance when we've been focused on reform; well, the key here is
that
> for most players, reform approaches are quite effective. But,
for
a
> number of players, reform attempts have been very unsuccessful
which
> forces us to remove some of these players from League
entirely."[0]
>
Thanks for the added context, Benjamin. Of course, banning bad
actors
that
they consider unreformable is something Wikipedia admins have
always
done
as well.
The League of Legends team began by building a dataset of
interactions
that
the community considered unacceptable, and then applied
machine-learning
to
that dataset.
It occurs to me that the English Wikipedia has ready access to
such
a
dataset: it's the totality of revision-deleted and oversighted
talk
page
posts. The League of Legends team collaborated with outside
scientists to
analyse their dataset. I would love to see the Wikimedia
Foundation
engage
in a similar research project.
I've added this point to the community wishlist survey:
https://meta.wikimedia.org/wiki/2015_Community_Wishlist_Survey#Machine-learn...
> P.S. As Rupert noted, over 90% of LoL players are male (how much
over
> 90%?).[1] It would be interesting to know whether this
percentage
has
> changed along with the improvements described in the article. >
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