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:
https://www.youtube.com/watch?v=L-1uzYYneUo
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