Hi all,
We're really excited to announce the launch of the Wikimedia Research
Fund with the goal of diversifying the network of Wikimedia
researchers globally and supporting the Wikimedia Movement in deeper
understanding of the projects, decision making, and building new
technologies.
If you are a Wikimedia researcher or you are interested in becoming
one, you can apply for research funds (USD 2k-50K) until January 3,
2022. While all research proposals related to Wikimedia projects are
welcome, we particularly encourage studies on medium to small size
languages and communities, as well as in low resourced languages and
projects. You can even propose to repeat a past study in a given
language in another language!
More info at https://meta.wikimedia.org/wiki/Grants:Programs/Wikimedia_Research_%26_Tech…
.
Apply by January 3, 2022 and/or spread the word!
Big thanks to Emily Lescak for all her behind-the-scenes work to make
the launch of the Research Fund possible, and to the Community
Resources team at the Wikimedia Foundation for giving us the
opportunity and the funds.
If you have questions, please reach out to us at
research_fund(a)wikimedia.org, meta [1], here, or in one of our
upcoming office hours [2]! :)
Best,
the Research Fund committee chairs
Benjamin Mako Hill (University of Washington)
Leila Zia (Wikimedia Foundation)
[1] https://meta.wikimedia.org/wiki/Grants_talk:Start
[2] https://meta.wikimedia.org/wiki/Grants:Programs/Wikimedia_Research_%26_Tech…
Hello all,
The next Wikimedia Research Showcase will be on Wednesday, November 17, at
17:30 UTC (9:30am PST/12:30pm EST/ 18:30 CET). The topic is content
moderation.
Livestream: https://www.youtube.com/watch?v=Rx3xesDkp2o
*Amy S. Bruckman (Georgia Institute of Technology, USA)Is Deplatforming
Censorship? What happened when controversial figures were deplatformed,
with philosophical musings on the nature of free speech*
Abstract: When a controversial figure is deplatformed, what happens to
their online influence? In this talk, first, I’ll present results from a
study of the deplatforming from Twitter of three figures who repeatedly
broke platform rules (Alex Jones, Milo Yiannopoulos, and Owen Benjamin).
Second, I’ll discuss what happened when this study was on the front page of
Reddit, and the range of angry reactions from people who say that they’re
in favor of “free speech.” I’ll explore the nature of free speech, and why
our current speech regulation framework is fundamentally broken. Finally,
I’ll conclude with thoughts on the strength of Wikipedia’s model in
contrast to other platforms, and highlight opportunities for improvement.
*Nathan TeBlunthuis (University of Washington / Northwestern University,
USA)Effects of Algorithmic Flagging on Fairness. Quasi-experimental
Evidence from Wikipedia*
Abstract: Online community moderators often rely on social signals such as
whether or not a user has an account or a profile page as clues that users
may cause problems. Reliance on these clues can lead to "overprofiling bias
when moderators focus on these signals but overlook the misbehavior of
others. We propose that algorithmic flagging systems deployed to improve
the efficiency of moderation work can also make moderation actions more
fair to these users by reducing reliance on social signals and making norm
violations by everyone else more visible. We analyze moderator behavior in
Wikipedia as mediated by RCFilters, a system which displays social signals
and algorithmic flags, and estimate the causal effect of being flagged on
moderator actions. We show that algorithmically flagged edits are reverted
more often, especially those by established editors with positive social
signals, and that flagging decreases the likelihood that moderation actions
will be undone. Our results suggest that algorithmic flagging systems can
lead to increased fairness in some contexts but that the relationship is
complex and contingent.
https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase
--
Janna Layton (she/her)
Administrative Associate - Product & Technology
Wikimedia Foundation <https://wikimediafoundation.org/>
+++ Apologies for cross-postings +++
The Max Planck Institute for Demographic Research (MPIDR) is inviting applications from qualified and highly motivated students for a Summer Research Visit in the Lab of Digital and Computational Demography<http://demogr.mpg.de/en/laboratories/digital_and_computational_demography_5…>.
The goal of the Population and Social Data Science Summer Incubator Program is to enable discovery by bringing together data scientists and population scientists to work on focused, intensive and collaborative projects of broad societal relevance.
For a period of 3 months (June 1st - August 30th, 2022), participating students will work in small teams, with support from experienced mentors, towards a common research goal. For the Summer of 2022, the Incubator will be coordinated by Rumi Chunara (New York University and Max Planck Sabbatical Awardee), Emilio Zagheni, and Ugofilippo Basellini. This Summer, the focus of the program will be on risk prediction models, including concepts spanning demography, fragility, and social vulnerabilities.
Participating students will be exposed to best practices across social and data sciences while contributing to a hands-on project experience. All participants will also have access to lectures and participate in other scientific activities happening at MPIDR.
Applicants must be enrolled in a graduate or undergraduate program (at the time they visit MPIDR). The Incubator program values research teams that include early-career scientists from a range of disciplines and backgrounds, with complementary skill sets. Priority will be placed on bringing together a diverse pool of students. The total number of attendees will be defined based on resources and quality of applications. A number of mentors (with an approximate ratio of 3 students:1 mentor) will be solicited from MPIDR, NYU and other collaborators. The mentors, along with summer program coordinators, will provide seed project and data ideas, with flexibility for students to put forward their own ideas as well.
Successful candidates will have demonstrated ability to work on research projects independently and in interdisciplinary teams, and interest in research problems related to both data science and social sciences, broadly defined.
Applications must be submitted online via https://www.demogr.mpg.de/go/incubator and include the following documents:
1. Curriculum Vitae
2. Cover letter (Max 2 page)
- Please state why you are interested in spending the Summer at MPIDR, and in which ways you would benefit from participating in the Incubator program.
- Please articulate your research interests and briefly describe a project you have worked on, the motivation for it and your contribution.
- Please describe your technical skills, as well what you would like to learn over the course of the Summer visit.
1. Names and contact information for 2 academic references
In order to receive full consideration, applications should be received by January 15th 2022. Notifications will be sent out by March 1, 2022. This will be an in-person Summer program, and students will be expected to be in residence at MPIDR in Rostock for the period of the research visit, from June 1st- August 30th. Participants will be expected to devote most of their working time to the collaborative research project during that period. A hybrid approach will be considered in cases where travel is not possible due to extenuating circumstances. Selected students enrolled in a PhD program will be offered reimbursement for travel costs to/from Rostock, and a stipend. Selected students who are not enrolled in a PhD program will be offered reimbursement for travel costs to/from Rostock, a per diem, and lodging in Rostock.
For administrative questions please get in touch with Beatrice Michaelis (michaelis(a)demogr.mpg.de<mailto:michaelis@demogr.mpg.de>). For scientific questions please contact Emilio Zagheni (Zagheni(a)demogr.mpg.de<mailto:Zagheni@demogr.mpg.de>), Ugofilippo Basellini (basellini(a)demogr.mpg.de<mailto:basellini@demogr.mpg.de>) or Rumi Chunara (rumi.chunara(a)nyu.edu<mailto:rumi.chunara@nyu.edu>).
Our Institute values diversity and is keen to employ individuals from minorities.
The Max Planck Society is committed to increasing the number of individuals with disabilities in its workforce and therefore encourages applications from such qualified individuals. Furthermore, the Max Planck Society seeks to increase the number of women in those areas where they are underrepresented and therefore explicitly encourages women to apply.
----------
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Hello All,
I am doing research investigating the role of machine translation in
Wikipedia articles. I am having trouble with how to know if an article has
been deleted from Wikipedia. Specifically, I am getting a list of articles
from the cxtranslation list and I would like to know which articles are no
longer on Wikipedia. I see that there is the deletion log form
<https://en.wikipedia.org/wiki/Special:Log/delete> but is there an API or
some way to access something like this form so I could check if a mass
amount of articles have been deleted?
I have used the Media Wiki API <https://en.wikipedia.org/w/api.php> to get
articles and the API returns missing for some articles, but this does not
seem to be fully accurate for determining if an article has been deleted
because the API has returned 'missing' for articles that do exist.
To summarize, my main question is: given an article language edition and
article title, or an article pageid, is there an API to check if the
article has been deleted?
Any help would be greatly appreciated!
Thanks,
Doris Zhou
Dear all,
We are proud to announce that we will organize the masterclass “DBpedia
Knowledge Graph Tutorial for Beginners” at the Connected Data World
event. The masterclass will take place online on December 2, 2021 at
4:15pm CET and it targets existing and potential new users of DBpedia,
developers that wish to learn basics about the DBpedia Knowledge Graph
and learn how to replicate and deploy DBpedia on a local infrastructure
as well as Linked Data and Knowledge Graph adopters.
# Highlights
In this masterclass, participants will learn how to consume the DBpedia
Knowledge Graph (KG) with the least amount of effort. The masterclass
will introduce the DBpedia KG and explain its dataset partitions.
Particular focus will be put on “usability”. On a selected use case, we
will explain the process of working with DBpedia KG and DBpedia
technology stack (DBpedia Databus, DBpedia Spotlight, DBpedia Docker)
and illustrate the potential and the benefits of using DBpedia.
# Quick Facts
- Web URL:
https://2021.connected-data.world/talks/dbpedia-knowledge-graph-tutorial-fo…
<https://2021.connected-data.world/talks/dbpedia-knowledge-graph-tutorial-fo…>
- When: December 2, 2021 at 4:15pm CET
- Where: The tutorial will be organized online.
# Tickets
- Please register at the Connected Data World website to be part of the
masterclass. Get your ticket here:
https://2021.connected-data.world/checkout/select-tickets/
<https://2021.connected-data.world/checkout/select-tickets/>
# Organisation
- Jan Forberg, DBpedia
- Johannes Frey, DBpedia
- Milan Dojchinovski, DBpedia / Czech Technical University in Prague
- Julia Holze, InfAI, DBpedia
- Sebastian Hellmann, DBpedia
We are looking forward to meeting you online!
Kind regards,
Julia
on behalf of the DBpedia Association
