> > 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 <jayen...@gmail.com> wrote: > On Sat, Nov 14, 2015 at 9:13 PM, Benjamin Lees <emufarm...@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-learning_tool_to_reduce_toxic_talk_page_interactions > > > > > 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-requ...@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-requ...@lists.wikimedia.org?subject=unsubscribe>
