Re: [Wiki-research-l] Difference between vandal fighting with vs. without tools

[WereSpielChequers]

Most of the vandalism I deal with nowadays I pick up when I am typo fixing.
I rarely check the same typo as frequently as once a fortnight, so a lot of
the vandalism I find is from over a week ago. That means it has got past
several layers of defences, including the watchlisters  (watch lists
default to 7 days).

But when in the past i have been active at recent changes I have honed in
on edits by editors with redlinked talkpages.If they made a good edit I'd
welcome them, if it was vandalism I'd warn them. Cluebot and users of
Huggle and Stiki are great at watching for edits by accounts and people who
have previously been warned, and if you are editing manually you are
wasting time trying to compete with them. But someone with a redlinked
talkpage is either a goodfaith editor, or a sufficiently sneaky vandal not
to be picked up by cluebot and the like.

On Mon, 24 Jun 2019 at 17:19, Haifeng Zhang  wrote:

> Hi all,
>
> This might be a known fact already.
>
> Does it take less time (on average) for an editor to identify a
> vandalistic edit when using counter-vandalism tools, e.g., Huggle or STiki?
> If so, what features of these tools support such decision?
>
>
> Thanks for your time,
>
> Haifeng Zhang
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Re: [Wiki-research-l] [Wikimedia Research Showcase] June 26, 2019 at 11:30 AM PST, 19:30 UTC

[Janna Layton]

Time correction:

The next Research Showcase will be live-streamed next Wednesday, June 26,
at *11:30 AM PDT/18:30 UTC*.

On Mon, Jun 24, 2019 at 4:11 PM Janna Layton  wrote:

> Hi all,
>
> The next Research Showcase will be live-streamed this Wednesday, June 26,
> at 11:30 AM PST/19:30 UTC. We will have three presentations this showcase,
> all relating to Wikipedia blocks.
>
> YouTube stream: https://www.youtube.com/watch?v=WiUfpmeJG7E
>
> As usual, you can join the conversation on IRC at #wikimedia-research. You
> can also watch our past research showcases here:
> https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase
>
> This month's presentations:
>
> Trajectories of Blocked Community Members: Redemption, Recidivism and
> Departure
>
> By Jonathan Chang, Cornell University
>
> Community norm violations can impair constructive communication and
> collaboration online. As a defense mechanism, community moderators often
> address such transgressions by temporarily blocking the perpetrator. Such
> actions, however, come with the cost of potentially alienating community
> members. Given this tradeoff, it is essential to understand to what extent,
> and in which situations, this common moderation practice is effective in
> reinforcing community rules. In this work, we introduce a computational
> framework for studying the future behavior of blocked users on Wikipedia.
> After their block expires, they can take several distinct paths: they can
> reform and adhere to the rules, but they can also recidivate, or
> straight-out abandon the community. We reveal that these trajectories are
> tied to factors rooted both in the characteristics of the blocked
> individual and in whether they perceived the block to be fair and
> justified. Based on these insights, we formulate a series of prediction
> tasks aiming to determine which of these paths a user is likely to take
> after being blocked for their first offense, and demonstrate the
> feasibility of these new tasks. Overall, this work builds towards a more
> nuanced approach to moderation by highlighting the tradeoffs that are in
> play.
>
>
> Automatic Detection of Online Abuse in Wikipedia
>
> By Lane Rasberry, University of Virginia
>
> Researchers analyzed all English Wikipedia blocks prior to 2018 using
> machine learning. With insights gained, the researchers examined all
> English Wikipedia users who are not blocked against the identified
> characteristics of blocked users. The results were a ranked set of
> predictions of users who are not blocked, but who have a history of conduct
> similar to that of blocked users. This research and process models a system
> for the use of computing to aid human moderators in identifying conduct on
> English Wikipedia which merits a block.
>
> Project page:
> https://meta.wikimedia.org/wiki/University_of_Virginia/Automatic_Detection_of_Online_Abuse
>
> Video: https://www.youtube.com/watch?v=AIhdb4-hKBo
>
>
> First Insights from Partial Blocks in Wikimedia Wikis
>
> By Morten Warncke-Wang, Wikimedia Foundation
>
> The Anti-Harassment Tools team at the Wikimedia Foundation released the
> partial block feature in early 2019. Where previously blocks on Wikimedia
> wikis were sitewide (users were blocked from editing an entire wiki),
> partial blocks makes it possible to block users from editing specific pages
> and/or namespaces. The Italian Wikipedia was the first wiki to start using
> this feature, and it has since been rolled out to other wikis as well. In
> this presentation, we will look at how this feature has been used in the
> first few months since release.
>
>
> --
> Janna Layton (she, her)
> Administrative Assistant - Audiences & Technology
> Wikimedia Foundation 
>


-- 
Janna Layton (she, her)
Administrative Assistant - Audiences & Technology
Wikimedia Foundation 
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Re: [Wiki-research-l] Monthly/Yearly Edits per country

[Kerry Raymond]

Maybe I am missing something but, while you can geolocate anonymous 
contributors (well to the extent you can reliably geolocate any IP address), 
you cannot geolocate logged-in users. Of course the WMF servers do know the IP 
addresses used by the logged-in users but this is suppressed for privacy 
reasons. So either you are seeing data based only on anonymous users (which is 
unlikely to be a representative sample) or the WMF have chosen to compile and 
release this data set. There is no way anyone else could compile the 
geolocations of all contributions.

So I am curious what the 2014 data you are pointing us to really represents.

Kerry

Sent from my iPad

> On 25 Jun 2019, at 5:29 pm, Adam Ferris  
> wrote:
> 
> Hello all,
> 
> I am new to this mailing list, and a new researcher so apologies if this is 
> not the right mailing list for this question, but I hope you might be able to 
> help me.
> 
> I am trying to recreate the map included below, with Wikipedia edits per 
> 10,000 internet users but with newer data, I was hoping year 2018 data that I 
> can then average per month, although it doesn’t have to be a Calendar year I 
> suppose, it could be Feb-2018 to Feb-2019.
> The key is that I would like newer data. I have looked at the sources of 
> these maps, and they all seem to end in 2013 or 2014. This source got me 
> close, but has no Edit data, only Viewing data.
> https://stats.wikimedia.org/wikimedia/squids/SquidReportPageViewsPerCountryBreakdown.htm
> 
> Could anyone help me in finding the right dataset to recreate this map? 
> (Image too heavy to be sent in email)
> Like this image:
> https://i0.wp.com/geonet.oii.ox.ac.uk/wp-content/uploads/sites/46/2016/09/Wikipedia_EditsPerMonth-1-1.png
> 
> From this article:
> https://www.oii.ox.ac.uk/blog/the-geography-of-wikipedia-edits/
> 
> I’m very grateful for any help you can offer me.
> 
> Very best,
> Adam
> 
> 
> 
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[Wiki-research-l] Monthly/Yearly Edits per country

[Adam Ferris]

Hello all,

I am new to this mailing list, and a new researcher so apologies if this is not 
the right mailing list for this question, but I hope you might be able to help 
me.

I am trying to recreate the map included below, with Wikipedia edits per 10,000 
internet users but with newer data, I was hoping year 2018 data that I can then 
average per month, although it doesn’t have to be a Calendar year I suppose, it 
could be Feb-2018 to Feb-2019.
The key is that I would like newer data. I have looked at the sources of these 
maps, and they all seem to end in 2013 or 2014. This source got me close, but 
has no Edit data, only Viewing data.
https://stats.wikimedia.org/wikimedia/squids/SquidReportPageViewsPerCountryBreakdown.htm

Could anyone help me in finding the right dataset to recreate this map? (Image 
too heavy to be sent in email)
Like this image:
https://i0.wp.com/geonet.oii.ox.ac.uk/wp-content/uploads/sites/46/2016/09/Wikipedia_EditsPerMonth-1-1.png

From this article:
https://www.oii.ox.ac.uk/blog/the-geography-of-wikipedia-edits/

I’m very grateful for any help you can offer me.

Very best,
Adam



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