Dario and Aaron, thanks for letting us know about this. Is the research
available in writing for people who don't want to sit through the video?

Sarah

On Wed, Mar 16, 2016 at 12:55 PM, Aaron Halfaker <ahalfa...@wikimedia.org>
wrote:

> Reminder, this showcase is starting in 5 minutes.  See the stream here:
> https://www.youtube.com/watch?v=Xle0oOFCNnk
>
> Join us on Freenode at #wikimedia-research
> <http://webchat.freenode.net/?channels=wikimedia-research> to ask Andrei
> questions.
>
> -Aaron
>
> On Tue, Mar 15, 2016 at 12:53 PM, Dario Taraborelli <
> dtarabore...@wikimedia.org> wrote:
>
> > This month, our research showcase
> > <https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase#March_2016>
> hosts
> > Andrei Rizoiu (Australian National University) to talk about his work
> > <http://cm.cecs.anu.edu.au/post/wikiprivacy/> on *how private traits of
> > Wikipedia editors can be exposed from public data* (such as edit
> > histories) using off-the-shelf machine learning techniques. (abstract
> below)
> >
> > If you're interested in learning what the combination of machine learning
> > and public data mean for privacy and surveillance, come and join us this
> *Wednesday
> > March 16* at *1pm Pacific Time*.
> >
> > The event will be recorded and publicly streamed
> > <https://www.youtube.com/watch?v=Xle0oOFCNnk>. As usual, we will be
> > hosting the conversation with the speaker and Q&A on the
> > #wikimedia-research channel on IRC.
> >
> > Looking forward to seeing you there,
> >
> > Dario
> >
> >
> > Evolution of Privacy Loss in WikipediaThe cumulative effect of collective
> > online participation has an important and adverse impact on individual
> > privacy. As an online system evolves over time, new digital traces of
> > individual behavior may uncover previously hidden statistical links
> between
> > an individual’s past actions and her private traits. To quantify this
> > effect, we analyze the evolution of individual privacy loss by studying
> > the edit history of Wikipedia over 13 years, including more than 117,523
> > different users performing 188,805,088 edits. We trace each Wikipedia’s
> > contributor using apparently harmless features, such as the number of
> edits
> > performed on predefined broad categories in a given time period (e.g.
> > Mathematics, Culture or Nature). We show that even at this unspecific
> level
> > of behavior description, it is possible to use off-the-shelf machine
> > learning algorithms to uncover usually undisclosed personal traits, such
> as
> > gender, religion or education. We provide empirical evidence that the
> > prediction accuracy for almost all private traits consistently improves
> > over time. Surprisingly, the prediction performance for users who stopped
> > editing after a given time still improves. The activities performed by
> new
> > users seem to have contributed more to this effect than additional
> > activities from existing (but still active) users. Insights from this
> work
> > should help users, system designers, and policy makers understand and
> make
> > long-term design choices in online content creation systems.
> >
> >
> > *Dario Taraborelli  *Head of Research, Wikimedia Foundation
> > wikimediafoundation.org • nitens.org • @readermeter
> > <http://twitter.com/readermeter>
> >
> > _______________________________________________
> > Wiki-research-l mailing list
> > wiki-researc...@lists.wikimedia.org
> > https://lists.wikimedia.org/mailman/listinfo/wiki-research-l
> >
> >
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