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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

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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

See also the review of this paper in our newsletter: "Popularity does not breed quality (and vice versa)"
RT @lorenterveen
Why I picked this reading. "Misalignment between supply and demand of quality content in peer production communities" @nettrom, Vivek Ranjan, @lorenterveen, @bhecht. We wanted to see whether Wikipedia editors' effort and readers' interests were ali…

In the new issue of our monthly newsletter:
• Take an AI-generated flashcard quiz about Wikipedia
• Wikipedia's anti-feudalism
and 8 other recent research publications

"World Influence of Infectious Diseases From Wikipedia Network Analysis" mapping the sensitivity of world countries to specific diseases, integrating their influence over their history, including the times of ancient Egyptian mummies!

(Rollin et al, 2019)

RT @omerbenj
What did we find?

1) Wikipedia's coronavirus articles cited (a whole lot) more open-access than Wikipedia did in general
2) Despite a (massive) deluge of COVID19 preprints, Wikipedia cited (slightly) less on coronavirus than it did generally

RT @DrCloern
Wikipedia as an outlet for increasing equity in the communication of scientific information.

@aslo_org @AGU_OS @CERFScience @AGUbiogeo @_Oceanography @BenthosNews

RT @lorenterveen
Why I picked this reading. "Why We Read Wikipedia" @ph_singer, @f_lemmerich, Robert West, Leila Zia, Ellery Wulczyn, @mstrohm, @jure. Seems important to know why people read to determine the extent to which Wikipedia is satisfying readers' motivations!

RT @nickmvincent
@wikiresearch @bhecht @Wikimedia @Wikipedia @google @Bing @PSAGroupNU @grouplens @nu_hci @nueecs @ACM_CSCW An interesting (maybe obvious to some?) implication: for a lot of searches, since you're likely to end up on Wikipedia anyway, you can probably get away with searching directly in Wikipedia (for instance using Firefox's search bar, Duckduckgo's "!w", or the Wikipedia app)!

"A Deeper Investigation of the Importance of Wikipedia
Links to Search Engine Results" the impact of Wikipedia extends way beyond the site: e.g. search engines are highly reliant on free content created by volunteers

(@nickmvincent and @bhecht, )

RT @ReaderMeter
"Curation and Dissemination of Biological Occurrences of Chemical Structures through Wikidata" -- via @biorxivpreprint

RT @omerbenj
With coronavirus being deemed an , @JonathanSobel1, @rona_av and myself set out to find out: Which sources informed Wikipedia's massive pool of COVID-19 articles during the pandemic's first wave @wikiresearch

RT @e__migrante
"Language-agnostic Topic Classification for Wikipedia". An algorithm (and dataset) with topics for Wikipedia articles across all languages. With @_isaaclj , @martgerlach

RT @JonathanSobel1
I'm delighted to share our last preprint entitled "Meta-Research: Citation needed? and the pandemic" Thanks to @omerbenj and @rona_av for the amazing work!

RT @srijankedia
Call for student scholarships to attend the The Web Conference 2021 (WWW 2021 ). Please apply by March 5th.

@TheWebConf @jure

RT @egonwillighagen
happy to have contributed to this new work (preprint): "Open Natural Products Research: Curation and Dissemination of Biological Occurrences of Chemical Structures through Wikidata"

RT @ruptho_
During this pandemic, Wikipedia has been going strong as editors increased their contributions - our preprint ( covered by @stokel in "New Scientist":

\w @manoelribeiro tsantos @f_lemmerich @mstrohm @cervisiarius @dhelic

RT @pk_plus_plus
Do you know tones of "toxic" comments? knows-- (1/5) I did a small experiment using @kaggle's @jigsaw toxic-comment dataset and find that -- Toxic comments are highly "overexpressed" in swear words, sexuality, anger, negativity, which are mostly directed to 2nd person...

"WebRED: Effective Pretraining And Finetuning For Relation Extraction On The Web", a large human annotated dataset and a method to extract relations from a variety of text found on the Web.

(Ormandi et al, 2021)

Today is @wikiresearch's 9th birthday!

We’ve shared with you an average of 1.9 @Wikimedia research updates per day over the past 9 years, and we hope you enjoyed them!


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