Dear Ms.,
I thank you for your efforts. We are a WikiResearch group working in Sfax, Tunisia. Our
main project is to try to enrich medical information on Wikidata. I ask if we can
participate to the Research showcase next month.
Yours Sincerely,
Houcemeddine Turki
Medical Student, Faculty of Medicine of Sfax, University of Sfax, Tunisia
Undergraduate Researcher, UR12SP36
GLAM and Education Coordinator, Wikimedia TN User Group
Member, Wiki Project Med
Member, Wikimedia and Library User Group
Founder, WikiLingua Maghreb
Founder, TunSci
____________________
+21629499418
-------- Message d'origine --------
De : Janna Layton <jlayton(a)wikimedia.org>
Date : 2019/02/14 20:20 (GMT+01:00)
À : wikimedia-l(a)lists.wikimedia.org, analytics(a)lists.wikimedia.org,
wiki-research-l(a)lists.wikimedia.org
Objet : [Analytics] [Wikimedia Research Showcase] February 20 at 11:30 AM PST, 19:30 UTC
Hello everyone,
The next Research Showcase, “The_Tower_of_Babel.jpg” and “A Warm Welcome, Not a Cold
Start,” will be live-streamed next Wednesday, February 20, 2019, at 11:30 AM PST/19:30
UTC. The first presentation is about how images are used across language editions, and the
second is about new editors.
YouTube stream:
https://www.youtube.com/watch?v=_jpJIFXwlEg
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:
The_Tower_of_Babel.jpg: Diversity of Visual Encyclopedic Knowledge Across Wikipedia
Language Editions
By Shiqing He (presenting, University of Michigan), Brent Hecht (presenting, Northwestern
University), Allen Yilun Lin (Northwestern University), Eytan Adar (University of
Michigan), ICWSM'18.
Across all Wikipedia language editions, millions of images augment text in critical ways.
This visual encyclopedic knowledge is an important form of wikiwork for editors, a
critical part of reader experience, an emerging resource for machine learning, and a lens
into cultural differences. However, Wikipedia research--and cross-language edition
Wikipedia research in particular--has thus far been limited to text. In this paper, we
assess the diversity of visual encyclopedic knowledge across 25 language editions and
compare our findings to those reported for textual content. Unlike text, translation in
images is largely unnecessary. Additionally, the Wikimedia Foundation, through the
Wikipedia Commons, has taken steps to simplify cross-language image sharing. While we may
expect that these factors would reduce image diversity, we find that cross-language image
diversity rivals, and often exceeds, that found in text. We find that diversity varies
between language pairs and content types, but that many images are unique to different
language editions. Our findings have implications for readers (in what imagery they see),
for editors (in deciding what images to use), for researchers (who study cultural
variations), and for machine learning developers (who use Wikipedia for training
models).
A Warm Welcome, Not a Cold Start: Eliciting New Editors' Interests via
Questionnaires
By Ramtin Yazdanian (presenting, Ecole Polytechnique Federale de Lausanne)
Every day, thousands of users sign up as new Wikipedia contributors. Once joined, these
users have to decide which articles to contribute to, which users to reach out to and
learn from or collaborate with, etc. Any such task is a hard and potentially frustrating
one given the sheer size of Wikipedia. Supporting newcomers in their first steps by
recommending articles they would enjoy editing or editors they would enjoy collaborating
with is thus a promising route toward converting them into long-term contributors.
Standard recommender systems, however, rely on users' histories of previous
interactions with the platform. As such, these systems cannot make high-quality
recommendations to newcomers without any previous interactions -- the so-called cold-start
problem. Our aim is to address the cold-start problem on Wikipedia by developing a method
for automatically building short questionnaires that, when completed by a newly registered
Wikipedia user, can be used for a variety of purposes, including article recommendations
that can help new editors get started. Our questionnaires are constructed based on the
text of Wikipedia articles as well as the history of contributions by the already
onboarded Wikipedia editors. We have assessed the quality of our questionnaire-based
recommendations in an offline evaluation using historical data, as well as an online
evaluation with hundreds of real Wikipedia newcomers, concluding that our method provides
cohesive, human-readable questions that perform well against several baselines. By
addressing the cold-start problem, this work can help with the sustainable growth and
maintenance of Wikipedia's diverse editor community.
--
Janna Layton (she, her)
Administrative Assistant - Audiences & Technology
Wikimedia
Foundation<https://wikimediafoundation.org/>