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In the new issue of our monthly newsletter:
• Detecting spam, and pages to protect
• Editors' intelligence test scores related to article quality
• Three papers on Wikipedia citations meta.wikimedia.org/wiki/Resear

Pinned toot

In the new issue of our newsletter:
• Thanking editors makes them come back more, but not contribute more
• "Wikipedia's Network Bias" on abortion and other controversial topics
• The interests of designer drugs editors
meta.wikimedia.org/wiki/Resear

"Consensus-Based Encyclopedic Virtue: and the Production of Authority in Encyclopedias".

thesis by Ryan McGrady at NC State Communication, Rhetoric, & Digital Media.

repository.lib.ncsu.edu/bitstr

"Consensus-Based Encyclopedic Virtue: and the Production of Authority in Encyclopedias".

thesis by Ryan McGrady at NC State Communication, Rhetoric, & Digital Media.

See also the somewhat contrasting results of another recent study, which found that receiving thanks increases retention of new editors, but not the time contributed to Wikipedia: meta.wikimedia.org/wiki/Resear
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RT @Wikimedia
A @wikiresearch joint study found that thanking another editor just once on @Wikiped…
twitter.com/Wikimedia/status/1

"Zero-shot Learning for Relation Extraction" recognize new relations based on prototypical networks that are modified to utilize auxiliary information from , WordNet and Merriam Webster.

(Gong and Eldardiry, 2020)

arxiv.org/abs/2011.07126

RT @Wikimedia
A @wikiresearch joint study found that thanking another editor just once on @Wikipedia can increase the recipient's edit count by a factor of >1.5 over the next day. (That's right; we quantifiably confirmed the importance of gratitude.) wikimediafoundation.org/news/2

"Linking OpenStreetMap with Knowledge Graphs - Link Discovery for Schema-Agnostic Volunteered Geographic Information", using Wikidata and DBPedia.

(Tempelmeier and Demidova, 2020)

arxiv.org/pdf/2011.05841.pdf

"WikiPathways: connecting communities": analyzing the growth of WikiPathways, a database of biological pathways maintained by the scientific community and connected to .

paper: academic.oup.com/nar/advance-a
data: wikipathways.org/index.php/Wik

@xanderpico @egonwillighagen

RT @jacknunn
'What are systematic reviews?' - Is it a peer-reviewed journal article? Is it a Wikipedia page? It's both!

Proud to share this new paper published in the Wiki Journal of Medicine:

doi.org/10.15347/WJM/2020.005

@WikiJMed

RT @ReaderMeter
Srijan is an amazing human being. His lab has produced some of the best research on topics of relevance to the tech industry as a whole (studying vandalism, antisocial behavior, trolling, and misinformation) with a strong open source ethos. You should really join his lab👇🏻 twitter.com/srijankedia/status

RT @leonidobusch
Zur Einstimmung auf die Diskussion gleich: ein Link zu einem Thread über eine Schwerpunktausgabe des Wikimedia Forschungsnewsletters mit Studien zum Thema Gender Gap & Gender Bias in der Wikipedia: twitter.com/leonidobusch/statu twitter.com/WikimediaDE/status

RT @pgildersleve
Delighted to have a paper accepted at @icwsm 2021! 🎉
Doubly delighted to have quoted Jürgen Klopp in the paper.

"Modeling Collective Anticipation and Response on Wikipedia" with @RyotaKobayash11 @RenaudLambiotte & Takeaki Uno, coming soon!

"Collaborating on the Sum of All Knowledge Across Languages" Sharing more knowledge across languages would allow each edition to focus on their unique contributions, and yet improve their comprehensiveness and currency.
by @vrandezo for

wikipedia20.pubpub.org/pub/vyf

RT @xavidegr
Attention to this amazing preprint from my biotech excolleague @adorrego_r (co-author)! 😍

A novel platform for annotation of @wikidata items in scientific articles, focused in hackathon methodologies & engagement techniques for researchers. Great!

zenodo.org/record/4279611#.X7b

RT @sherzodhakimov
Visual Event Type Classification for @wikidata ontology

Source code, dataset and pre-trained models: github.com/TIBHannover/VisE twitter.com/WikiResearch/statu

"Ontology-driven Event Type Classification in Images" exploit structured information from to learn relevant event relations using deep neural networks.

(Müller-Budack et al, 2020)

arxiv.org/pdf/2011.04714.pdf
@sherzodhakimov

RT @frimelle
Thread with the lightening talks on YouTube of the @wikidata workshop 2020 (cc @wikiresearch) 🙌🥳 twitter.com/WikidataW/status/1

"CLIRMatrix: A massively large collection of bilingual and multilingual datasets for Cross-Lingual Information Retrieval" based on and

(Sun and Duh, 2020)

paper: aclweb.org/anthology/2020.emnl
data: github.com/ssun32/CLIRMatrix

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