Re: [Wikimedia-l] [Wikimedia Research Showcase] February 20 at 11:30 AM PST, 19:30 UTC

[Janna Layton]

The Research Showcase will be starting in about 30 minutes! Info below:

On Thu, Feb 14, 2019 at 11:20 AM Janna Layton  wrote:

> 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 
>


-- 
Janna Layton (she, her)
Administrative Assistant - Audiences & Technology
Wikimedia Foundation 
___
Wikimedia-l mailing list, guidelines at: 
https://meta.wikimedia.org/wiki/Mailing_lists/Guidelines and 
https://meta.wikimedia.org/wiki/Wikimedia-l
New messages to: Wikimedia-l@lists.wikimedia.org
Unsubscribe: https://lists.wikimedia.org/mailman/listinfo/wikimedia-l, 

Re: [Wikimedia-l] [Wikimedia Research Showcase] February 20 at 11:30 AM PST, 19:30 UTC

[Janna Layton]

This is just a reminder that the event below will be happening tomorrow,
February 20.

On Thu, Feb 14, 2019 at 11:20 AM Janna Layton  wrote:

> 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 
>


-- 
Janna Layton (she, her)
Administrative Assistant - Audiences & Technology
Wikimedia Foundation 
___
Wikimedia-l mailing list, guidelines at: 
https://meta.wikimedia.org/wiki/Mailing_lists/Guidelines and 
https://meta.wikimedia.org/wiki/Wikimedia-l
New messages to: Wikimedia-l@lists.wikimedia.org
Unsubscribe: https://lists.wikimedia.org/mailman/listinfo/wikimedia-l, 

[Wikimedia-l] [Wikimedia Research Showcase] February 20 at 11:30 AM PST, 19:30 UTC

[Janna Layton]

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 
___
Wikimedia-l mailing list, guidelines at: 
https://meta.wikimedia.org/wiki/Mailing_lists/Guidelines and 
https://meta.wikimedia.org/wiki/Wikimedia-l
New messages to: Wikimedia-l@lists.wikimedia.org
Unsubscribe: https://lists.wikimedia.org/mailman/listinfo/wikimedia-l,