Very interesting read (via Brandon Harris):
http://recode.net/2015/07/07/doing-something-about-the-impossible-problem-of...
"the vast majority of negative behavior ... did not originate from the persistently negative online citizens; in fact, 87 percent of online toxicity came from the neutral and positive citizens just having a bad day here or there."
"... incidences of homophobia, sexism and racism ... have fallen to a combined 2 percent of all games. Verbal abuse has dropped by more than 40 percent, and 91.6 percent of negative players change their act and never commit another offense after just one reported penalty."
I have plenty of ideas how to apply this to Wikipedia, but I am sure Dario and his team as well :) - and some opportunity for the communities to use such results.
Cheers, Denny
Really interesting - thanks for sharing!
On Fri, Nov 13, 2015 at 10:12 PM, Denny Vrandečić vrandecic@gmail.com wrote:
Very interesting read (via Brandon Harris):
http://recode.net/2015/07/07/doing-something-about-the-impossible-problem-of...
"the vast majority of negative behavior ... did not originate from the persistently negative online citizens; in fact, 87 percent of online toxicity came from the neutral and positive citizens just having a bad day here or there."
"... incidences of homophobia, sexism and racism ... have fallen to a combined 2 percent of all games. Verbal abuse has dropped by more than 40 percent, and 91.6 percent of negative players change their act and never commit another offense after just one reported penalty."
I have plenty of ideas how to apply this to Wikipedia, but I am sure Dario and his team as well :) - and some opportunity for the communities to use such results.
Cheers, Denny _______________________________________________ Wikimedia-l mailing list, guidelines at: https://meta.wikimedia.org/wiki/Mailing_lists/Guidelines Wikimedia-l@lists.wikimedia.org Unsubscribe: https://lists.wikimedia.org/mailman/listinfo/wikimedia-l, mailto:wikimedia-l-request@lists.wikimedia.org?subject=unsubscribe
We're discussing this on the Research mailing list, among others. (: Pine
On Fri, Nov 13, 2015 at 2:12 PM, Denny Vrandečić vrandecic@gmail.com wrote:
Very interesting read (via Brandon Harris):
http://recode.net/2015/07/07/doing-something-about-the-impossible-problem-of...
"the vast majority of negative behavior ... did not originate from the persistently negative online citizens; in fact, 87 percent of online toxicity came from the neutral and positive citizens just having a bad day here or there."
"... incidences of homophobia, sexism and racism ... have fallen to a combined 2 percent of all games. Verbal abuse has dropped by more than 40 percent, and 91.6 percent of negative players change their act and never commit another offense after just one reported penalty."
I have plenty of ideas how to apply this to Wikipedia, but I am sure Dario and his team as well :) - and some opportunity for the communities to use such results.
Cheers, Denny
Analytics mailing list Analytics@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/analytics
I was skeptical of even reading this article, but it actually seems pretty insightful. It also seems more relevant to Wikipedia than I was expecting: "The answer had to be community-wide reform of cultural norms. We had to change how people thought about online society and change their expectations of what was acceptable.... How do you introduce structure and governance into a society that didn’t have one before?"
It has some interesting ideas about using science to change the social dynamics of online communities and leveraging the work of academics who want to work on these problems. Some of the techniques they used remind me of Aaron's revision scoring. I wonder if there's any chance we could talk with them or some of their researchers.
On Fri, Nov 13, 2015 at 3:12 PM, Denny Vrandečić vrandecic@gmail.com wrote:
Very interesting read (via Brandon Harris):
http://recode.net/2015/07/07/doing-something-about-the-impossible-problem-of...
"the vast majority of negative behavior ... did not originate from the persistently negative online citizens; in fact, 87 percent of online toxicity came from the neutral and positive citizens just having a bad day here or there."
"... incidences of homophobia, sexism and racism ... have fallen to a combined 2 percent of all games. Verbal abuse has dropped by more than 40 percent, and 91.6 percent of negative players change their act and never commit another offense after just one reported penalty."
I have plenty of ideas how to apply this to Wikipedia, but I am sure Dario and his team as well :) - and some opportunity for the communities to use such results.
Cheers, Denny
Analytics mailing list Analytics@lists.wikimedia.org https://lists.wikimedia.org/mailman/listinfo/analytics
interesting. i read 90% male, 85% between 16 and 30 years of age, 12 mio players a day, 1 bio hours played a month (2012). they had a tribunal which is switched off since a year. the market is 54 mio usd a month for multiplayer online battle arena (moba) in the united states. league of legends earns 120 mio usd per month, out of a monthly player base of over 60 mio, which is 3 times more player than dota2, and 6 times more income than dota2 (beginning of 2015).
* http://www.ign.com/articles/2012/10/15/riot-games-releases-awesome-league-of... * http://www.kitguru.net/gaming/development/jon-martindale/league-of-legends-t... * http://www.polygon.com/2014/5/27/5723446/women-in-esports-professional-gamin... * http://venturebeat.com/2015/03/24/dota-2-makes-18m-per-month-for-valve-but-l...
On Sat, Nov 14, 2015 at 8:25 AM, Ryan Kaldari rkaldari@wikimedia.org wrote:
I was skeptical of even reading this article, but it actually seems pretty insightful. It also seems more relevant to Wikipedia than I was expecting: "The answer had to be community-wide reform of cultural norms. We had to change how people thought about online society and change their expectations of what was acceptable.... How do you introduce structure and governance into a society that didn’t have one before?"
It has some interesting ideas about using science to change the social dynamics of online communities and leveraging the work of academics who want to work on these problems. Some of the techniques they used remind me of Aaron's revision scoring. I wonder if there's any chance we could talk with them or some of their researchers.
On Fri, Nov 13, 2015 at 3:12 PM, Denny Vrandečić vrandecic@gmail.com wrote:
Very interesting read (via Brandon Harris):
http://recode.net/2015/07/07/doing-something-about-the-impossible-problem-of...
"the vast majority of negative behavior ... did not originate from the persistently negative online citizens; in fact, 87 percent of online toxicity came from the neutral and positive citizens just having a bad day here or there."
"... incidences of homophobia, sexism and racism ... have fallen to a combined 2 percent of all games. Verbal abuse has dropped by more than 40 percent, and 91.6 percent of negative players change their act and never commit another offense after just one reported penalty."
I have plenty of ideas how to apply this to Wikipedia, but I am sure Dario and his team as well :) - and some opportunity for the communities to use such results.
Thanks for the article, Denny.
A couple of weeks ago, I posted a proposal to use machine learning to identify problematic talk page behaviour at the English Wikipedia's Village Pump.[1]
The ideas seem roughly equivalent: the main aim is to make people aware of it when they are about to engage in counterproductive behaviour, and to ensure there is more timely feedback and outside input.
Personally, I think the community needs a push like this in order to make that cultural shift. It is encouraging to learn that such an effort can yield tangible results in practice (something a few of the commenters at the Village Pump were doubtful about).
Please review the linked Village Pump discussion, and provide input on how and whether this could be made to work in Wikipedia.
[1] https://en.wikipedia.org/wiki/Wikipedia:Village_pump_(proposals)#Proposed:_T... Permalink: https://en.wikipedia.org/w/index.php?title=Wikipedia:Village_pump_(proposals...
On Fri, Nov 13, 2015 at 3:12 PM, Denny Vrandečić vrandecic@gmail.com wrote:
Very interesting read (via Brandon Harris):
http://recode.net/2015/07/07/doing-something-about-the-impossible-problem-of...
"the vast majority of negative behavior ... did not originate from the persistently negative online citizens; in fact, 87 percent of online toxicity came from the neutral and positive citizens just having a bad
day
here or there."
"... incidences of homophobia, sexism and racism ... have fallen to a combined 2 percent of all games. Verbal abuse has dropped by more than
40
percent, and 91.6 percent of negative players change their act and never commit another offense after just one reported penalty."
I have plenty of ideas how to apply this to Wikipedia, but I am sure
Dario
and his team as well :) - and some opportunity for the communities to
use
such results.
Wikimedia-l mailing list, guidelines at: https://meta.wikimedia.org/wiki/Mailing_lists/Guidelines Wikimedia-l@lists.wikimedia.org Unsubscribe: https://lists.wikimedia.org/mailman/listinfo/wikimedia-l, mailto:wikimedia-l-request@lists.wikimedia.org?subject=unsubscribe
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]
A little context about League of Legends (I haven't played in a couple years, so my apologies if anything I say is out of date): * In an average game you are thrust onto a team with 4 complete strangers you will probably never meet again, and must work together to defeat the other team. * Individual player mistakes hurt the team, often a lot. Think making an error at the World Series in baseball. * A typical game lasts 20-50 minutes. If you leave the game before it finishes, you will be punished. (After 20 minutes your team can surrender if 4 of your players agree to do so.) * Some games affect your global ranking relative to all other players.
These game mechanics promote a form of tension which is part of the excitement of the game but which is also sometimes stressful (if, say, you're doing really badly and your team doesn't want to quit). By and large, Wikipedia's mechanics seem very different from this, there are a few areas where users are pushed into a more hostile role with one another. In those narrow cases, like the village pump, I could maybe see benefits from trying to re-engineer interactions, but I'm skeptical that this will somehow engineer a cultural shift.
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.
P.P.S. In League you have to pay if you want to transfer your account from one region to another. I'm sure we could resolve all ENGVAR disputes once and for all by adding some region locking. :-)
[0] http://www.polygon.com/2014/7/21/5924203/league-of-legends-ban-code-2500-tox... [1] http://majorleagueoflegends.s3.amazonaws.com/lol_infographic.png
On Fri, Nov 13, 2015 at 5:12 PM, Denny Vrandečić vrandecic@gmail.com wrote:
Very interesting read (via Brandon Harris):
http://recode.net/2015/07/07/doing-something-about-the-impossible-problem-of...
"the vast majority of negative behavior ... did not originate from the persistently negative online citizens; in fact, 87 percent of online toxicity came from the neutral and positive citizens just having a bad day here or there."
"... incidences of homophobia, sexism and racism ... have fallen to a combined 2 percent of all games. Verbal abuse has dropped by more than 40 percent, and 91.6 percent of negative players change their act and never commit another offense after just one reported penalty."
I have plenty of ideas how to apply this to Wikipedia, but I am sure Dario and his team as well :) - and some opportunity for the communities to use such results.
Cheers, Denny _______________________________________________ Wikimedia-l mailing list, guidelines at: https://meta.wikimedia.org/wiki/Mailing_lists/Guidelines Wikimedia-l@lists.wikimedia.org Unsubscribe: https://lists.wikimedia.org/mailman/listinfo/wikimedia-l, mailto:wikimedia-l-request@lists.wikimedia.org?subject=unsubscribe
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.
Indeed.
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.
1. https://github.com/Ironholds/talk-parser 2. https://meta.wikimedia.org/wiki/Wiki_labels 3. 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.
Indeed. _______________________________________________ Wikimedia-l mailing list, guidelines at: https://meta.wikimedia.org/wiki/Mailing_lists/Guidelines Wikimedia-l@lists.wikimedia.org Unsubscribe: https://lists.wikimedia.org/mailman/listinfo/wikimedia-l, mailto:wikimedia-l-request@lists.wikimedia.org?subject=unsubscribe
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.
Indeed. _______________________________________________ Wikimedia-l mailing list, guidelines at: https://meta.wikimedia.org/wiki/Mailing_lists/Guidelines Wikimedia-l@lists.wikimedia.org Unsubscribe: https://lists.wikimedia.org/mailman/listinfo/wikimedia-l, mailto:wikimedia-l-request@lists.wikimedia.org?subject=unsubscribe
Wikimedia-l mailing list, guidelines at: https://meta.wikimedia.org/wiki/Mailing_lists/Guidelines Wikimedia-l@lists.wikimedia.org Unsubscribe: https://lists.wikimedia.org/mailman/listinfo/wikimedia-l, mailto:wikimedia-l-request@lists.wikimedia.org?subject=unsubscribe
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.
Indeed. _______________________________________________ Wikimedia-l mailing list, guidelines at: https://meta.wikimedia.org/wiki/Mailing_lists/Guidelines Wikimedia-l@lists.wikimedia.org Unsubscribe: https://lists.wikimedia.org/mailman/listinfo/wikimedia-l, mailto:wikimedia-l-request@lists.wikimedia.org?subject=unsubscribe
Wikimedia-l mailing list, guidelines at: https://meta.wikimedia.org/wiki/Mailing_lists/Guidelines Wikimedia-l@lists.wikimedia.org Unsubscribe: https://lists.wikimedia.org/mailman/listinfo/wikimedia-l, mailto:wikimedia-l-request@lists.wikimedia.org?subject=unsubscribe
-- Karen Brown user:Fluffernutter
*Unless otherwise specified, any email sent from this address is in my volunteer capacity and does not represent the views or wishes of the Wikimedia Foundation* _______________________________________________ Wikimedia-l mailing list, guidelines at: https://meta.wikimedia.org/wiki/Mailing_lists/Guidelines Wikimedia-l@lists.wikimedia.org Unsubscribe: https://lists.wikimedia.org/mailman/listinfo/wikimedia-l, mailto:wikimedia-l-request@lists.wikimedia.org?subject=unsubscribe
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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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.
Indeed. _______________________________________________ Wikimedia-l mailing list, guidelines at: https://meta.wikimedia.org/wiki/Mailing_lists/Guidelines Wikimedia-l@lists.wikimedia.org Unsubscribe:
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-- Karen Brown user:Fluffernutter
*Unless otherwise specified, any email sent from this address is in my volunteer capacity and does not represent the views or wishes of the Wikimedia Foundation* _______________________________________________ Wikimedia-l mailing list, guidelines at:
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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. >
Indeed. _______________________________________________ Wikimedia-l mailing list, guidelines at: https://meta.wikimedia.org/wiki/Mailing_lists/Guidelines Wikimedia-l@lists.wikimedia.org Unsubscribe:
https://lists.wikimedia.org/mailman/listinfo/wikimedia-l
,
<mailto:wikimedia-l-request@lists.wikimedia.org
?subject=unsubscribe>
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,
<mailto:wikimedia-l-request@lists.wikimedia.org
?subject=unsubscribe>
-- Karen Brown user:Fluffernutter
*Unless otherwise specified, any email sent from this address is in
my
volunteer capacity and does not represent the views or wishes of the Wikimedia Foundation* _______________________________________________ Wikimedia-l mailing list, guidelines at:
https://meta.wikimedia.org/wiki/Mailing_lists/Guidelines
Wikimedia-l@lists.wikimedia.org Unsubscribe:
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