[Wiki-research-l] Re: Wikimedia Research Showcase June 21 at 16:30 UTC

[Pablo Aragón]

Hi all,

A friendly reminder that this is starting in about 30 minutes. We hope you
can join us!

Best,

On Thu, Jun 15, 2023 at 10:53 AM Pablo Aragón  wrote:

> Hi again,
>
> There was an error in the previous message: the title of the second
> presentation is *“How do you represent my gender? Challenges and
> opportunities from the Wikidata Gender Diversity project”*.
>
> Hope you can join us!
>
> Warm regards,
>
> On Thu, Jun 15, 2023 at 9:16 AM Pablo Aragón 
> wrote:
>
>> Hi all,
>>
>> The next Research Showcase, with the theme of *Wikimedia and LGBTQIA+*,
>> will be live-streamed Wednesday, June 21 at 16:30 UTC. Find your local time
>> here <https://zonestamp.toolforge.org/1687365012>.
>>
>> YouTube stream: https://www.youtube.com/watch?v=AOD2ZdxRNfo
>>
>> You can join the conversation on IRC at #wikimedia-research or on the
>> YouTube chat.
>>
>> This month's presentations:
>>
>>- *Multilingual Contextual Affective Analysis of LGBT People
>>Portrayals in Wikipedia*
>>- *Speaker*: Chan Park, Carnegie Mellon University
>>   - *Abstract*: In this talk, I present our research on analyzing
>>   the portrayal of LGBT individuals in their biographies on Wikipedia, 
>> with a
>>   particular focus on subtle word connotations and cross-cultural
>>   comparisons. We aim to address two primary research questions: 1) How 
>> can
>>   we effectively measure the nuanced connotations of words in 
>> multilingual
>>   texts, which reflect sentiments, power dynamics, and agency? 2) How 
>> can we
>>   analyze the portrayal of a specific group, such as the LGBT community, 
>> and
>>   compare these portrayals across different languages? To answer these
>>   questions, we collect the Multilingual Contextualized Connotation 
>> Frames
>>   dataset, comprising 2,700 examples in English, Spanish, and Russian. We
>>   also develop a new multilingual model based on pre-trained multilingual
>>   language models. Additionally, we devise a matching algorithm to 
>> construct
>>   a comparison corpus for the target corpus, isolating the attribute of
>>   interest. Finally, we showcase how our developed models and constructed
>>   corpora enable us to conduct cross-cultural analysis of LGBT People
>>   Portrayals on Wikipedia. Our results reveal systematic differences in 
>> how
>>   the LGBT community is portrayed across languages, surfacing cultural
>>   differences in narratives and signs of social biases.
>>   - *Paperː* Park, C. Y., Yan, X., Field, A., & Tsvetkov, Y. (2021,
>>   May). Multilingual contextual affective analysis of LGBT people 
>> portrayals
>>   in Wikipedia. In Proceedings of the International AAAI Conference on 
>> Web
>>   and Social Media (Vol. 15, pp. 479-490).
>>   <https://arxiv.org/pdf/2010.10820.pdf>
>>
>>
>>- *Visual gender biases in Wikipediaː A systematic evaluation across
>>the ten most spoken languages*
>>   - *Speaker*: Daniele Metilli, University College London
>>   - *Abstract*: Wikidata Gender Diversity (WiGeDi) is a one-year
>>   project funded through the Wikimedia Research Fund. The project is 
>> studying
>>   gender diversity in Wikidata, focusing on marginalized gender 
>> identities
>>   such as those of trans and non-binary people, and adopting a queer and
>>   intersectional feminist perspective. The project is organised in three
>>   strands — model, data, and community. First, we are looking at how the
>>   current Wikidata ontology model represents gender, and the extent to 
>> which
>>   this representation is inclusive of marginalized gender identities. We 
>> are
>>   analysing the data stored in the knowledge base to gather insights and
>>   identify possible gaps and biases. Finally, we are looking at how the
>>   community has handled the move towards the inclusion of a wider 
>> spectrum of
>>   gender identities by studying a corpus of user discussions through
>>   computational linguistics methods. This presentation will report on the
>>   current status of the Wikidata Gender Diversity project and the 
>> envisioned
>>   outcomes. We will discuss the main challenges that we are facing and 
>> the
>>   opportunities that our project will potentially enable, on Wikidata and
>>   beyond.
>>   - *Paperː* Metilli D. & Paolini C. (in press). ‘Non-binary gender
>

[Wiki-research-l] Re: Wikimedia Research Showcase June 21 at 16:30 UTC

[Pablo Aragón]

Hi again,

There was an error in the previous message: the title of the second
presentation is *“How do you represent my gender? Challenges and
opportunities from the Wikidata Gender Diversity project”*.

Hope you can join us!

Warm regards,

On Thu, Jun 15, 2023 at 9:16 AM Pablo Aragón  wrote:

> Hi all,
>
> The next Research Showcase, with the theme of *Wikimedia and LGBTQIA+*,
> will be live-streamed Wednesday, June 21 at 16:30 UTC. Find your local time
> here <https://zonestamp.toolforge.org/1687365012>.
>
> YouTube stream: https://www.youtube.com/watch?v=AOD2ZdxRNfo
>
> You can join the conversation on IRC at #wikimedia-research or on the
> YouTube chat.
>
> This month's presentations:
>
>- *Multilingual Contextual Affective Analysis of LGBT People
>Portrayals in Wikipedia*
>- *Speaker*: Chan Park, Carnegie Mellon University
>   - *Abstract*: In this talk, I present our research on analyzing the
>   portrayal of LGBT individuals in their biographies on Wikipedia, with a
>   particular focus on subtle word connotations and cross-cultural
>   comparisons. We aim to address two primary research questions: 1) How 
> can
>   we effectively measure the nuanced connotations of words in multilingual
>   texts, which reflect sentiments, power dynamics, and agency? 2) How can 
> we
>   analyze the portrayal of a specific group, such as the LGBT community, 
> and
>   compare these portrayals across different languages? To answer these
>   questions, we collect the Multilingual Contextualized Connotation Frames
>   dataset, comprising 2,700 examples in English, Spanish, and Russian. We
>   also develop a new multilingual model based on pre-trained multilingual
>   language models. Additionally, we devise a matching algorithm to 
> construct
>   a comparison corpus for the target corpus, isolating the attribute of
>   interest. Finally, we showcase how our developed models and constructed
>   corpora enable us to conduct cross-cultural analysis of LGBT People
>   Portrayals on Wikipedia. Our results reveal systematic differences in 
> how
>   the LGBT community is portrayed across languages, surfacing cultural
>   differences in narratives and signs of social biases.
>   - *Paperː* Park, C. Y., Yan, X., Field, A., & Tsvetkov, Y. (2021,
>   May). Multilingual contextual affective analysis of LGBT people 
> portrayals
>   in Wikipedia. In Proceedings of the International AAAI Conference on Web
>   and Social Media (Vol. 15, pp. 479-490).
>   <https://arxiv.org/pdf/2010.10820.pdf>
>
>
>- *Visual gender biases in Wikipediaː A systematic evaluation across
>the ten most spoken languages*
>   - *Speaker*: Daniele Metilli, University College London
>   - *Abstract*: Wikidata Gender Diversity (WiGeDi) is a one-year
>   project funded through the Wikimedia Research Fund. The project is 
> studying
>   gender diversity in Wikidata, focusing on marginalized gender identities
>   such as those of trans and non-binary people, and adopting a queer and
>   intersectional feminist perspective. The project is organised in three
>   strands — model, data, and community. First, we are looking at how the
>   current Wikidata ontology model represents gender, and the extent to 
> which
>   this representation is inclusive of marginalized gender identities. We 
> are
>   analysing the data stored in the knowledge base to gather insights and
>   identify possible gaps and biases. Finally, we are looking at how the
>   community has handled the move towards the inclusion of a wider 
> spectrum of
>   gender identities by studying a corpus of user discussions through
>   computational linguistics methods. This presentation will report on the
>   current status of the Wikidata Gender Diversity project and the 
> envisioned
>   outcomes. We will discuss the main challenges that we are facing and the
>   opportunities that our project will potentially enable, on Wikidata and
>   beyond.
>   - *Paperː* Metilli D. & Paolini C. (in press). ‘Non-binary gender
>   representation in Wikidata’. In: Provo A., Burlingame K. & Watson B.M.
>   Ethics in Linked Data. Litwin Books.
>   <https://wigedi.com/chapter.pdf>
>
> You can watch our past Research Showcases here: 
> https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase
>
>
> Hope you can join us!
>
> Warm regards,
>
> --
>
> *Pablo Aragón (he/him)*
> Research Scientist
> Wikimedia Foundation
> https://research.wikimedia.org
>
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[Wiki-research-l] Wikimedia Research Showcase June 21 at 16:30 UTC

[Pablo Aragón]

Hi all,

The next Research Showcase, with the theme of *Wikimedia and LGBTQIA+*,
will be live-streamed Wednesday, June 21 at 16:30 UTC. Find your local time
here <https://zonestamp.toolforge.org/1687365012>.

YouTube stream: https://www.youtube.com/watch?v=AOD2ZdxRNfo

You can join the conversation on IRC at #wikimedia-research or on the
YouTube chat.

This month's presentations:

   - *Multilingual Contextual Affective Analysis of LGBT People Portrayals
   in Wikipedia*
   - *Speaker*: Chan Park, Carnegie Mellon University
  - *Abstract*: In this talk, I present our research on analyzing the
  portrayal of LGBT individuals in their biographies on Wikipedia, with a
  particular focus on subtle word connotations and cross-cultural
  comparisons. We aim to address two primary research questions: 1) How can
  we effectively measure the nuanced connotations of words in multilingual
  texts, which reflect sentiments, power dynamics, and agency? 2)
How can we
  analyze the portrayal of a specific group, such as the LGBT
community, and
  compare these portrayals across different languages? To answer these
  questions, we collect the Multilingual Contextualized Connotation Frames
  dataset, comprising 2,700 examples in English, Spanish, and Russian. We
  also develop a new multilingual model based on pre-trained multilingual
  language models. Additionally, we devise a matching algorithm to
construct
  a comparison corpus for the target corpus, isolating the attribute of
  interest. Finally, we showcase how our developed models and constructed
  corpora enable us to conduct cross-cultural analysis of LGBT People
  Portrayals on Wikipedia. Our results reveal systematic differences in how
  the LGBT community is portrayed across languages, surfacing cultural
  differences in narratives and signs of social biases.
  - *Paperː* Park, C. Y., Yan, X., Field, A., & Tsvetkov, Y. (2021,
  May). Multilingual contextual affective analysis of LGBT people
portrayals
  in Wikipedia. In Proceedings of the International AAAI Conference on Web
  and Social Media (Vol. 15, pp. 479-490).
  <https://arxiv.org/pdf/2010.10820.pdf>


   - *Visual gender biases in Wikipediaː A systematic evaluation across the
   ten most spoken languages*
  - *Speaker*: Daniele Metilli, University College London
  - *Abstract*: Wikidata Gender Diversity (WiGeDi) is a one-year
  project funded through the Wikimedia Research Fund. The project
is studying
  gender diversity in Wikidata, focusing on marginalized gender identities
  such as those of trans and non-binary people, and adopting a queer and
  intersectional feminist perspective. The project is organised in three
  strands — model, data, and community. First, we are looking at how the
  current Wikidata ontology model represents gender, and the
extent to which
  this representation is inclusive of marginalized gender
identities. We are
  analysing the data stored in the knowledge base to gather insights and
  identify possible gaps and biases. Finally, we are looking at how the
  community has handled the move towards the inclusion of a wider
spectrum of
  gender identities by studying a corpus of user discussions through
  computational linguistics methods. This presentation will report on the
  current status of the Wikidata Gender Diversity project and the
envisioned
  outcomes. We will discuss the main challenges that we are facing and the
  opportunities that our project will potentially enable, on Wikidata and
  beyond.
  - *Paperː* Metilli D. & Paolini C. (in press). ‘Non-binary gender
  representation in Wikidata’. In: Provo A., Burlingame K. & Watson B.M.
  Ethics in Linked Data. Litwin Books. <https://wigedi.com/chapter.pdf>

You can watch our past Research Showcases here:
https://www.mediawiki.org/wiki/Wikimedia_Research/Showcase


Hope you can join us!

Warm regards,

-- 

*Pablo Aragón (he/him)*
Research Scientist
Wikimedia Foundation
https://research.wikimedia.org
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[Wiki-research-l] Re: [Analytics] [Wikimedia Research Showcase] March 15

[Pablo Aragón]

Hi all,

A friendly reminder that the Wikimedia Research Showcase on Gender and
Equity will be this Wednesday!

We hope that some of you can join the livestream.

Best,

On Fri, Mar 10, 2023 at 4:36 PM Emily Lescak  wrote:

> Hi all,
>
> The next Research Showcase, focused on Gender and Equity on Wikipedia,
> will be live-streamed Wednesday, March 15, at 9:30 AM PST / 16:30 UTC. Find
> your local time here .
>
> YouTube stream: https://www.youtube.com/watch?v=lw4MzJgDIzo
>
> 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:
> Men Are elected, women are marriedː events gender bias on Wikipedia
> By *Jiao Sun, University of Southern California*Human activities can be
> seen as sequences of events, which are crucial to understanding societies.
> Disproportional event distribution for different demographic groups can
> manifest and amplify social stereotypes, and potentially jeopardize the
> ability of members in some groups to pursue certain goals. In this paper,
> we present the first event-centric study of gender biases in a Wikipedia
> corpus. To facilitate the study, we curate a corpus of career and personal
> life descriptions with demographic information consisting of 7,854
> fragments from 10,412 celebrities. Then we detect events with a
> state-of-the-art event detection model, calibrate the results using
> strategically generated templates, and extract events that have asymmetric
> associations with genders. Our study discovers that the Wikipedia pages
> tend to intermingle personal life events with professional events for
> females but not for males, which calls for the awareness of the Wikipedia
> community to formalize guidelines and train the editors to mind the
> implicit biases that contributors carry. Our work also lays the foundation
> for future works on quantifying and discovering event biases at the corpus
> level.
>
>- Paperː Sun, J. & Peng, N. (2021). Men Are Elected, Women Are
>Married: Events Gender Bias on Wikipedia. Proceedings of the 59th Annual
>Meeting of the Association for Computational Linguistics and the 11th
>International Conference on Natural Language Processing, 350-360.
>
>
>
> Twitter reacts to absence of women on Wikipediaː a mixed-methods analysis
> of #VisibleWikiWomen campaignBy *Sneh Gupta, Guru Gobind Singh
> Indraprastha University*Digital gender divide (DGD) is visible in access,
> participation, representation, and biases against women embedded in
> Wikipedia, the largest digital reservoir of co-created content. This
> article examined the content of #VisibleWikiWomen, a global digital
> advocacy campaign aimed at encouraging inclusion of women voices in the
> global technology conversation and improving digital sustainability of
> feminist data on Wikipedia. In a mixed-methods study, Sentiment Analysis
> followed by a Feminist Critical Discourse Analysis of the campaign tweets
> reveals how digital gender divide manifested in the public response. An
> overwhelming majority of tweets expressed positive sentiment towards the
> objective of the campaign. An inductive reading of the coded tweets (n =
> 1067) generated five themes: Feminist Activism, Invisibility &
> Marginalization of Women, Technology for Women Empowerment, Gendered
> Knowledge Inequity, and Power Dynamics in the Digital Sphere. Twitter
> discourse presented many agitated digital users calling out the epistemic
> injustice on Wikipedia that goes beyond the invisibility of women. Their
> tweets reveal that they want an equal social platform inclusive of women of
> color and varied identities currently absent in the Wikipedia universe.
> Extracting ideas, values, and themes from new media campaigns holds
> unparalleled potential in the diffusion of interventions and messages on a
> larger scale.
>
>- Paperː Gupta, S., & Trehan, K. (2022). Twitter reacts to absence of
>women on Wikipedia: a mixed-methods analysis of #VisibleWikiWomen campaign.
>Media Asia, 49(2), 130-154.
>
> 
>
> Warm regards,
>
> Emily
>
> --
> Emily Lescak (she / her)
> Senior Research Community Officer
> The Wikimedia Foundation
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>
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[Wiki-research-l] Re: Wikimedia natural experiments

[Pablo Aragón]

What a great initiative, thanks Isaac!

I just added some references and included *Campaigns* as a category in the
list.

On Wed, Apr 27, 2022 at 12:10 AM Nate TeBlunthuis  wrote:

> Thanks for compiling these Isaac! Public domain day seems like a
> particularly interesting one to look into more deeply.
>
> --
>
> Nate
>
> On 4/26/22 11:14, Isaac Johnson wrote:
> > As part of some discussions with Jim Maddock (see his recent thread
> > <
> https://lists.wikimedia.org/hyperkitty/list/wiki-research-l@lists.wikimedia.org/thread/INT2D2XGJ4YO2NVYBNKOOQMVI6OXKG32/
> >),
> > I started a meta page to catalog some of the many natural experiments
> >  that impact the
> > Wikimedia projects. I've seeded it with some examples (censorship,
> > Wikipedia Zero, lockdowns, Public Domain Day, new data centers, etc.) but
> > am certain that there are many more out there. If folks on this list know
> > of other examples, please edit the page below boldly or respond on thread
> > and I'll migrate examples onto the page.
> >
> > https://meta.wikimedia.org/wiki/Research:Natural_experiments
> >
> > Best,
> > Isaac
> >
> --
> Nate TeBlunthuis PhD Candidate Department of Communication University of
> Washington https://teblunthuis.cc
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[Wiki-research-l] Re: What's your favorite text about general research frameworks?

[Pablo Aragón]

Hi Andrew,

Thanks for sharing this question and the two references. In the field of
Computational Social Science, [1-3] are key references to me, I hope they
inspire you too.

Best,

[1] Salganik, M. J. (2019). Bit by bit: Social research in the digital age.
Princeton University Press. https://www.bitbybitbook.com

[2] González-Bailón, S. (2017). Decoding the social world: Data science and
the unintended consequences of communication. MIT Press.
https://mitpress.mit.edu/books/decoding-social-world

[3] Lazer, D. M., Pentland, A., Watts, D. J., Aral, S., Athey, S.,
Contractor, N., ... & Wagner, C. (2020). Computational social science:
Obstacles and opportunities. Science, 369(6507), 1060-1062.

On Thu, Feb 3, 2022 at 5:28 PM Andrew Green  wrote:

> Hi all,
>
> I hope this is the right place to ask this question!
>
> I was wondering if folks who are doing (or are interested in) research
> about Wikipedia might like to share texts that they feel best describe
> the general research frameworks they use (or might like to use).
>
> I'd love to hear about any texts you like, regardless of format
> (textbook, paper, general reference, blog post, etc.).
>
> It seems a lot of work about Wikipedia uses approaches from
> Computational Social Science. The main references I have for that are
> [1] and [2].
>
> I'm especially interested in links between Computational Social Science
> and frameworks from more traditional social sciences and cognitive science.
>
> Many thanks in advance! :) Cheers,
> Andrew
>
> [1] Cioffi-Revilla, C. (2017) /Introduction to Computational Social
> Science. Principles and Applications. Second Edition./ Cham,
> Switzerland: Springer.
>
> [2] Melnik, R. (ed.) (2015)/Mathematical and Computational Modeling.
> With Applications in Natural and Social Sciences, Engineering, and the
> Arts/. Hoboken, U.S.A.: Wiley.
>
> --
> Andrew Green (he/him)
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[Wiki-research-l] Re: Edit Summary Stats / Research?

[Pablo Aragón]

Hi Isaac,

I am currently reviewing work on spam detection on Wikipedia. West et al.
(2011)  found that *the
length (in characters) of the revision summary* was one of the features
with the greatest weight in the final classifier.

Best,

On Wed, Aug 4, 2021 at 11:46 PM Isaac Johnson  wrote:

> Thanks all for the feedback! If anyone thinks of more, by all means send
> over.
>
> > 1. One of the reasons why any suggestion that we make edit summaries
> compulsory is that as long as they are optional, blank edit summaries are a
> great way to identify vandals.
> This is a pretty interesting point. For further context, I'm asking because
> I'm mentoring a researcher who will be looking into edit summary usage and
> I wanted to make sure we weren't asking questions that had already been
> answered elsewhere. The research is still in the formative stages of
> figuring out what additional research might be useful and just having a
> better understanding of the distribution of edit types. When I think of
> tools / interventions based on what little I know, however, it's mainly
> along the lines of what sorts of edit tags (or similar filters) could be
> auto-generated to further contextualize edit summaries. Helping editors
> quickly match their edit to templated/canned messages is an idea that gets
> floated around too but could be counterproductive for the vandalism case as
> you point out.
>
> > There is a long-standing tool to search them at
>
> https://sigma.toolforge.org/summary.py?name=Stuartyeates=re-review=500=enwiki=Wikipedia
> In case you're looking for code to reuse.
> Thanks! Glad to see this tool exists!
>
> For completeness, it was also pointed out to me that Wattenberg, Viégas,
> and Hollenbach's 2007 paper "Visualizing Activity on Wikipedia with
> Chromograms" makes heavy use of edit summaries and provides some insight
> into their usage:
> https://link.springer.com/content/pdf/10.1007/978-3-540-74800-7_23.pdf
>
> Best,
> Isaac
>
> On Tue, Aug 3, 2021 at 3:48 PM Stuart A. Yeates  wrote:
>
> > There is a long-standing tool to search them at
> >
> >
> >
> https://sigma.toolforge.org/summary.py?name=Stuartyeates=re-review=500=enwiki=Wikipedia
> >
> > In case you're looking for code to reuse.
> >
> > cheers
> > stuart
> > --
> > ...let us be heard from red core to black sky
> >
> > On Wed, 4 Aug 2021 at 05:38, WereSpielChequers
> >  wrote:
> > >
> > > Dear Isaac,
> > >
> > > I'm not aware of any research on this. But there are a couple of common
> > > assumptions that you could check as part of any research.
> > >
> > >
> > >1. One of the reasons why any suggestion that we make edit summaries
> > >compulsory is that as long as they are optional, blank edit
> summaries
> > are a
> > >great way to identify vandals.
> > >2. There is also a certain amount of "sneaky vandalism" denoted by
> > edits
> > >that get reverted or reverted and the perpetrators get warned for
> > vandalism
> > >or blocked as a "vandalism only account"
> > >3. Though we admins have the technology to blank people's edit
> > summaries
> > >it is very rarely used
> > >
> > >
> > >
> > >
> > >  Regards
> > > Jonathan
> > >
> > > On Tue, 3 Aug 2021 at 16:20, Isaac Johnson 
> wrote:
> > >
> > > > Does anyone know of any research or statistics around edit summary
> > > >  usage on
> Wikipedia?
> > All
> > > > I
> > > > could find in a quick scan was some statistics from 2010 (
> > > > https://meta.wikimedia.org/wiki/Usage_of_edit_summary_on_Wikipedia).
> > I'm
> > > > curious if anyone has more updated statistics, or, even better: a
> more
> > > > thorough analysis of how edit summaries are used by editors -- i.e.
> how
> > > > complete they are, to what degree they represent the "what" vs. the
> > "why",
> > > > how often they are misleading, etc.
> > > >
> > > > Best,
> > > > Isaac
> > > >
> > > > --
> > > > Isaac Johnson (he/him/his) -- Research Scientist -- Wikimedia
> > Foundation
> > > > ___
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> > > >
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> >
>
>
> --
> Isaac Johnson (he/him/his) -- Research Scientist -- Wikimedia Foundation
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