Wikimedia Research/Showcase
(Redirected from Analytics/Research and Data/Showcase)
The Monthly Wikimedia Research Showcase is a public showcase of recent research by the Wikimedia Foundation's Research Team and guest presenters from the academic community. The showcase is hosted virtually every 3rd Wednesday of the month at 9:30 a.m. Pacific Time/18:30 p.m. CET and is live-streamed on YouTube. The schedule may change, see the calendar below for a list of confirmed showcases.
How to attend[edit]
We live stream our research showcase every month on YouTube. The link will be in each showcase's details below and is also announced in advance via wiki-research-l, analytics-l, and @WikiResearch on Twitter. You can join the conversation and participate in Q&A after each presentation using the YouTube chat. We expect all presenters and attendees to abide by our Friendly Space Policy.
Upcoming Events[edit]
April 2024[edit]
- Time
- Wednesday, April 17, 16:30 UTC: Find your local time here
- Theme
- Supporting Multimedia on Wikipedia
April 17, 2024 Video: YouTube
- Towards image accessibility solutions grounded in communicative principles
- By Elisa Kreiss
- Images have become an omnipresent communicative tool -- and this is no exception on Wikipedia. However, the undeniable benefits they carry for sighted communicators turns into a serious accessibility challenge for people who are blind or have low vision (BLV). BLV users often have to rely on textual descriptions of those images to equally participate in an ever-increasing image-dominated online lifestyle. In this talk, I will present how framing accessibility as a communication problem highlights important ways forward in redefining image accessibility on Wikipedia. I will present the Wikipedia-based dataset Concadia and use it to discuss the successes and shortcomings of image captions and alt texts for accessibility, and how the usefulness of accessibility descriptions is fundamentally contextual. I will conclude by highlighting the potential and risks of AI-based solutions and discussing implications for different Wikipedia editing communities.
- By Elisa Kreiss
- Automatic Multi-Path Web Story Creation from a Structural Article
- By Daniel Nkemelu
- Web articles such as Wikipedia serve as one of the major sources of knowledge dissemination and online learning. However, their in-depth information--often in a dense text format--may not be suitable for mobile browsing, even in a responsive user interface. We propose an automatic approach that converts a structured article of any length into a set of interactive Web Stories that are ideal for mobile experiences. We focused on Wikipedia articles and developed Wiki2Story, a pipeline based on language and layout models, to demonstrate the concept. Wiki2Story dynamically slices an article and plans one to multiple Story paths according to the document hierarchy. For each slice, it generates a multi-page summary Story composed of text and image pairs in visually appealing layouts. We derived design principles from an analysis of manually created Story practices. We executed our pipeline on 500 Wikipedia documents and conducted user studies to review selected outputs. Results showed that Wiki2Story effectively captured and presented salient content from the original articles and sparked interest in viewers.
- By Daniel Nkemelu
Archive[edit]
For information about past research showcases (2013-present), you can search below or see listing of all months here.
2024[edit]
March 2024[edit]
- Time
- Wednesday, March 20, 16:30 UTC: Find your local time here
- Theme
- Addressing Gender Gaps
Wednesday, March 20, 2023 Video: YouTube
- Leveraging Recommender Systems to Reduce Content Gaps on Wikipedia
- By Mo Houtti
- Many Wikipedians use algorithmic recommender systems to help them find interesting articles to edit. The algorithms underlying those systems are driven by a straightforward assumption: we can look at what someone edited in the past to figure out what they’ll most likely want to edit next. But the story of what Wikipedians want to edit is almost definitely more complex than that. For example, our own prior research shows that Wikipedians prefer prioritizing articles that would minimize content gaps. So, we asked, what would happen if we incorporated that value into Wikipedians’ personalized recommendations? Through a controlled experiment on SuggestBot, we found that recommending more content gap articles didn’t significantly impact editing, despite those articles being less “optimally interesting” according to the recommendation algorithm. In this presentation, I will describe our experiment, our results, and their implications - including how recommender systems can be one useful strategy for tackling content gaps on Wikipedia.
- By Mo Houtti
- Bridging the offline and online- Offline meetings of Wikipedians
[[1]]
- By Nicole Schwitter
- Wikipedia is primarily known as an online encyclopaedia, but it also features a noteworthy offline component: Wikipedia and particularly its German-language edition – which is one of the largest and most active language versions – is characterised by regular local offline meetups which give editors the chance to get to know each other. This talk will present the recently published dewiki meetup dataset which covers (almost) all offline gatherings organised on the German-language version of Wikipedia. The dataset covers almost 20 years of offline activity of the German-language Wikipedia, containing 4418 meetups that have been organised with information on attendees, apologies, date and place of meeting, and minutes recorded. The talk will explain how the dataset can be used for research, highlight the importance of considering offline meetings among Wikipedians, and place these insights within the context of addressing gender gaps within Wikipedia.
- By Nicole Schwitter
February 2024[edit]
- Time
- Wednesday, February 21, 16:30 UTC: Find your local time here
- Theme
- Platform Governance and Policies
Wednesday, February 21, 2023 Video: YouTube
- Sociotechnical Designs for Democratic and Pluralistic Governance of Social Media and AI
- By Amy X. Zhang, University of Washington
- Decisions about policies when using widely-deployed technologies, including social media and more recently, generative AI, are often made in a centralized and top-down fashion. Yet these systems are used by millions of people, with a diverse set of preferences and norms. Who gets to decide what are the rules, and what should the procedures be for deciding them---and must we all abide by the same ones? In this talk, I draw on theories and lessons from offline governance to reimagine how sociotechnical systems could be designed to provide greater agency and voice to everyday users and communities. This includes the design and development of: 1) personal moderation and curation controls that are usable and understandable to laypeople, 2) tools for authoring and carrying out governance to suit a community's needs and values, and 3) decision-making workflows for large-scale democratic alignment that are legitimate and consistent.
- By Amy X. Zhang, University of Washington
January 2024[edit]
- Time
- Wednesday, January 17, 17:30 UTC: Find your local time here
- Theme
- Connecting Actions with Policy
January 17, 2023 Video: YouTube
- Presenting the report "Unreliable Guidelines"
- By Amber Berson and Monika Sengul-Jones
- The goal behind the report Unreliable Guidelines: Reliable Sources and Marginalized Communities in French, English and Spanish Wikipedias was to understand the effects of the set of reliable source guidelines and rules on the participation of and the content about marginalized communities on three Wikipedias. Two years following the release of their report, researchers Berson and Sengul-Jones reflect on the impact of their research as well as the actionable next steps.
- By Amber Berson and Monika Sengul-Jones
- Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions
- By Lucie-Aimée Kaffee and Arnav Arora
- The moderation of content on online platforms is usually non-transparent. On Wikipedia, however, this discussion is carried out publicly and the editors are encouraged to use the content moderation policies as explanations for making moderation decisions. However, currently only a few comments explicitly mention those policies. To aid in this process of understanding how content is moderated, we construct a novel multilingual dataset of Wikipedia editor discussions along with their reasoning in three languages. We demonstrate that stance and corresponding reason (policy) can be predicted jointly with a high degree of accuracy, adding transparency to the decision-making process.
- Paper: Kaffee, Lucie-Aimée, Arnav Arora, and Isabelle Augenstein. Why Should This Article Be Deleted? Transparent Stance Detection in Multilingual Wikipedia Editor Discussions. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023.
- The moderation of content on online platforms is usually non-transparent. On Wikipedia, however, this discussion is carried out publicly and the editors are encouraged to use the content moderation policies as explanations for making moderation decisions. However, currently only a few comments explicitly mention those policies. To aid in this process of understanding how content is moderated, we construct a novel multilingual dataset of Wikipedia editor discussions along with their reasoning in three languages. We demonstrate that stance and corresponding reason (policy) can be predicted jointly with a high degree of accuracy, adding transparency to the decision-making process.
- By Lucie-Aimée Kaffee and Arnav Arora