Re: [Wikimedia-l] Research Showcase Wednesday, January 17, 2018

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Re: [Wikimedia-l] Research Showcase Wednesday, January 17, 2018

Dario Taraborelli-3
Hey all,

a reminder that the livestream of our monthly research showcase starts in
45 minutes (11.30 PT)

   - Video:
   - IRC: #wikimedia-research
   - Abstracts:


On Tue, Jan 16, 2018 at 9:45 AM, Lani Goto <[hidden email]> wrote:

> Hi Everyone,
> The next Research Showcase will be live-streamed this Wednesday, January
> 17, 2018 at 11:30 AM (PST) 19:30 UTC.
> YouTube stream:
> As usual, you can join the conversation on IRC at #wikimedia-research. And,
> you can watch our past research showcases here.
> This month's presentation:
> *What motivates experts to contribute to public information goods? A field
> experiment at Wikipedia*
> By Yan Chen, University of Michigan
> Wikipedia is among the most important information sources for the general
> public. Motivating domain experts to contribute to Wikipedia can improve
> the accuracy and completeness of its content. In a field experiment, we
> examine the incentives which might motivate scholars to contribute their
> expertise to Wikipedia. We vary the mentioning of likely citation, public
> acknowledgement and the number of views an article receives. We find that
> experts are significantly more interested in contributing when citation
> benefit is mentioned. Furthermore, cosine similarity between a Wikipedia
> article and the expert's paper abstract is the most significant factor
> leading to more and higher-quality contributions, indicating that better
> matching is a crucial factor in motivating contributions to public
> information goods. Other factors correlated with contribution include
> social distance and researcher reputation.
> *Wikihounding on Wikipedia*
> By Caroline Sinders, WMF
> Wikihounding (a form of digital stalking on Wikipedia) is incredibly
> qualitative and quantitive. What makes wikihounding different then
> mentoring? It's the context of the action or the intention. However, all
> interactions inside of a digital space has a quantitive aspect to it, every
> comment, revert, etc is a data point. By analyzing data points
> comparatively inside of wikihounding cases and reading some of the cases,
> we can create a baseline for what are the actual overlapping similarities
> inside of wikihounding to study what makes up wikihounding. Wikihounding
> currently has a fairly loose definition. Wikihounding, as defined by the
> Harassment policy on en:wp, is: “the singling out of one or more editors,
> joining discussions on multiple pages or topics they may edit or multiple
> debates where they contribute, to repeatedly confront or inhibit their
> work. This is with an apparent aim of creating irritation, annoyance or
> distress to the other editor. Wikihounding usually involves following the
> target from place to place on Wikipedia.” This definition doesn't outline
> parameters around cases such as frequency of interaction, duration, or
> minimum reverts, nor is there a lot known about what a standard or
> canonical case of wikihounding looks like. What is the average wikihounding
> case? This talk will cover the approaches myself and members of the
> research team: Diego Saez-Trumper, Aaron Halfaker and Jonathan Morgan are
> taking on starting this research project.
> --
> Lani Goto
> Project Assistant, Engineering Admin
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*Dario Taraborelli  *Director, Head of Research, Wikimedia Foundation • • @readermeter
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