Hello everyone, my name is Yelanda Pensa, I'm a senior researcher at SUP, which is a Swiss
University at the Institute of Design, but I'm also an active member of Wikipedia being
contributing since 2006, I've been chair of Wikimedia Italy, and now I'm also a national
coordinator of Glams, which means that I spend a lot of time selling in cyclopideas around
Switzerland as a volunteer. And this is a tool about Wikipedia, I don't think it's necessary
to present Wikipedia. This year is the 25th anniversary, it is an open infrastructure, open
content, it is also a website that serves the open science and the open Glams, and is also
an open education on the resource, so I think it's a good symbol of the open in general.
And the reason why we're developing this tool is because I spend a lot of time working on
content, and I started by contributing to contemporary African art, and one of the problems
with Wikipedia is that it's very difficult to grasp what a topic is about, how is the situation
of a topic? You get an access to articles once at a time, and when you work on project,
you get list of articles, but it's very difficult to get a sense of the landscape. So how
a topic is documented on Wikipedia? What is the quality of that content? Is it reach or not?
So I've been testing some visualization in some research project, I've been promoting.
So one of the first visualization that we've been working on was to transform 100 articles
related to the South African school system, and seeing how we can show how those articles
are on Wikipedia, and how we change them by contributing and improving them.
Another visualization tool that I've been developing with Giovanni Perfeita, was created
in 2020 during the pandemic, in Italy, all schools were closed, so we were thinking, okay,
Wikipedia is an open educational resource tool, but how is actually the quality of that content?
We did research to define list of articles related to the school system. We looked at content related
from primary school to high school on different subject, looking at high school books and different
books. We organized topics related to disciplines, and we looked at how those articles were
in different years. So we started in 2020, we involved the community to improve articles,
and we checked how those articles changed throughout time. So what you see in this visualization
is basically each article is a bubble. The dimension of the bubble tells you the dimension of the
article. The dot inside is the dimension of the in-ship it, so the lead session, the first part
of the article, which other times was of course a lot read, and also we did a focus group in which
also teacher and students identified as very important, and also you have errors got each article in
details and see how it changed through time, and you can organize a content according to
different categories. So the idea of this visualization was really to identify how the landscape
of certain article presented, and also to look at issue that could be fixed quite easily. So for
example, if an article was already mentioning cleanup tags, or he had no images, so identify what
could be the volunteers do quite quickly. So this project was also replicated in Uruguay,
so they used the software to adapt it and to look at the articles related to the Uruguay
and school system. So after this experience, we decided to create a tool based on this experience,
and a tool that can be applied to any set of article, and we decided to focus with the research
on climate change and sustainability, because of course, it's an important topic for everyone,
and improving content on Wikipedia can have obviously a broader effect. So we started working on
the visualization, and at the same time, Wikipedia education, which is a foundation in the United
State that works a lot with classrooms and the school books, was at the same time working on
this visualizing impact tool that was already working on the impact of contribution by students.
So we decided to join the two projects and work together, and then take a little break.
So the idea of the visualization is really to serve volunteer, to facilitate their work,
to allow them to identify what is the priority, which article they should focus on.
But also create a landscape that can be useful for researchers to identify topic,
understanding how a topic is described, and how is it treated on Wikipedia, and also facilitating
contribution by glams, analysis by journalists, and contribution by teachers.
So at the moment, we have a pilot tool that you can find online, so it focuses only on climate change,
but in reality, the tool can extract data from Wikipedia, or from categories on Wikipedia.
And it already shows articles and the situation.
The reason why the first visualization is, you see, all the bubbles squeezed on the top is the
Donald Trump problem. So of course, the tool created a system to erase him, so that is actually
possible to see the properly, the articles. It's obvious the first step, but one of the issues that
are obviously we're facing now is that at the beginning, the idea was to focus on the number of
visualization, but today things are changing, of course, with the machines that are visualizing,
Wikipedia, more than humans. So we are changing some of the feature focusing more on contributors,
and see which are the articles that have more contributors. We've been also
involving, well, of course, you find the new or the data and the software online.
And at the moment, we're still in the co-designing phase. So some of the visual, we are working on the
first visualization, but we will be comparing languages, the project at the moment focuses on four languages.
So Italian Spanish English and French, and we're also looking at how changes that been made
from one through time. So the next visualization, which will be the Wikimedia Communities,
so you can select articles according to their staff with the introducing the fact that at the
moment I'm also working on trying to bring to first them in 2029, the entire open community.
So Wikipedia, science, open educational resources, creative commerce, open street map communities,
so try to have a joint event in 2029. Thanks.
Thank you very much.
Please.
The presentation on the type of this radiation.
So, it's so hard.
And you know what's going to be?
The type of exposure to the screen of the distribution.
And you describe the powers.
Do you have plans for different laserization, or is this one,
sitting your needs for now?
No.
One of the.
Yes.
So, at the moment, the question is about the different visualization that we're planning.
No, there are different kind of visualization.
This one is the first approach that we're working on.
The emphasis will be also done on comparing how the initial set of article was presented
and how changes produced a different visualization.
This is something that is very interesting.
Well, to satisfy volunteers, also to give feedback to the wiki project.
Because a lot of a wiki project, a managed by one person or two people.
So, the fact of having a feedback from my visualization is also important to encourage them.
But it's also interesting for fundraising for affiliates because they can present a funders,
what actually they did by organizing event by contributing to wiki media.
So, the aspect of the before after is probably what it will transform.
Also, another issue is how to create visualization that can be targeting different people.
So, this is something that we are browsing.
At the moment where we started with this catch, we have some more sketches in particularly focusing on comparing languages,
which is also something we're focusing on for languages, to not make it huge.
But we are designing the tools that you can be broadened.
And also consider that this is a dashboard.
So, wiki education already manages a dashboard,
in particular to manage events in the community.
So, the dashboard will be included within their work.
So, people will be able to apply it to their needs,
and you can serve really individuals and more projects.
Yes, please.
Are there any more visualizations that are more focused on the user,
rather than the editor, such as the visualization,
back to the media's part of everything else.
The visualization focus more on the behind the scene of wiki media.
So, we don't focus much on the users of the readers.
In particular, now, with the change on AI, this is also an issue.
One of the things that we are considering is also how to look at,
how to identify in articles different styles, for example,
that could be adapted and could be improved, also according to readers.
But it's true that, for example, monitoring the impact of the article is something
that a lot of researcher would love to know more.
But there is always a, it's also a tricky issue,
because for example, analyzing a discussion page,
we show the size of the discussion page.
If an article is, for example, a controversial,
but we don't explore the discussion page and the dynamics,
also for privacy issues.
And so, it's something that we are not really putting on,
on front of it.
So, we're more focusing on the work behind the scene,
to contribute and to improve content.
Thank you.