Hi all,
The March Research Showcase will be live-streamed this Wednesday, March 19, at 9:30 AM PT / 16:30 UTC. Find your local time here https://zonestamp.toolforge.org/1742401800. March is Women's History Month https://en.wikipedia.org/wiki/Women%27s_History_Month in many parts of the world, making it a good time to discuss the latest research on *Gender Gapsֹֹ*- our theme for this month.
We invite you to watch via the YouTube stream: https://www.youtube.com/live/zRTdu-Ku1FU. As always, you can join the conversation in the YouTube chat as soon as the showcase goes live.
For this showcase we’re excited to feature three presentations, including a full-length talk and two presentations of research supported by the Wikimedia Research Fund:
Online Images Amplify Gender Bias By *Douglas Guilbeault (Stanford University)* Each year, people spend less time reading and more time viewing images, which are proliferating online. Images from platforms like Google and Wikipedia are downloaded by millions every day, and millions more are interacting via social media like Instagram and TikTok that primarily consist of exchanging visual content. In parallel, news agencies and digital advertisers are increasingly capturing attention online through the use of visual content, which people process more quickly, implicitly, and memorably than text. In this paper, we show that the rise of images online significantly exacerbates gender bias, both in its statistical prevalence and its psychological impact. We examine the gender associations of 3,495 social categories (such as nurse or banker) in over one million images from Google, Wikipedia, and IMDb, as well as in billions of words from these platforms. We find that gender bias is stronger and more prevalent in images than text for both female- and male-typed categories. We further show that the documented underrepresentation of women online is worse in images compared to not only text, but also public opinion and US census data. Finally, we conducted a nationally representative, pre-registered experiment which shows that googling for images rather than textual descriptions of occupations amplifies gender bias in participants’ beliefs. Addressing the societal impact of this large-scale shift toward visual communication will be essential for developing a fair and inclusive future for the internet. Measuring the Gender GapBy *Tianwa Chen (The University of Queensland)*In this presentation, I would like to present our three research works aimed at measuring the gender gap on Wikipedia through data-driven strategies. Our first study explores the estimation of gender completeness within Wikipedia, offering a new methodology for assessing content gaps. The second study analyses the evolution of gender diversity, employing visualizations to track the gender distribution in Wikipedia articles categorized under ‘Person’ over time. The third and ongoing study delves into the gender balancing efforts among Wikipedia editors. We are currently conducting interviews within the editor community and planning to develop a dashboard through a co-design approach. These studies collectively advance our understanding of gender representation and provide actionable insights to foster gender equality in the Wikipedia community.Addressing Wikipedia’s Gender Gaps Through Social Media AdsBy *Reham AL Tamime (University of Strathclyde)*Wikipedia’s well-documented gender gap remains a persistent challenge, with women underrepresented among contributors. While past efforts—such as Edit-a-thons, workshops, and social media campaigns—have aimed to bridge this gap, more targeted approaches remain under-explored. In this talk, I will present our project, which explores the use of social media advertising to reach and recruit women as Wikipedia editors. I will share preliminary findings from our targeted advertisements on LinkedIn, where we designed a survey to assess the effectiveness of the reach of the advertisement. Building on these insights, I will discuss how we have expanded our approach to include multiple social media platforms, refined targeting strategies, and developed various messages to increase reach and eventually participation in Wikipedia. Best,Kinneret
Hi all,
Just a quick reminder that we will be starting this month's showcase in about an hour. Join us at https://www.youtube.com/live/zRTdu-Ku1FU.
Best, Kinneret
On Mon, Mar 17, 2025 at 11:56 AM Kinneret Gordon kgordon@wikimedia.org wrote:
Hi all,
The March Research Showcase will be live-streamed this Wednesday, March 19, at 9:30 AM PT / 16:30 UTC. Find your local time here https://zonestamp.toolforge.org/1742401800. March is Women's History Month https://en.wikipedia.org/wiki/Women%27s_History_Month in many parts of the world, making it a good time to discuss the latest research on *Gender Gapsֹֹ*- our theme for this month.
We invite you to watch via the YouTube stream: https://www.youtube.com/live/zRTdu-Ku1FU. As always, you can join the conversation in the YouTube chat as soon as the showcase goes live.
For this showcase we’re excited to feature three presentations, including a full-length talk and two presentations of research supported by the Wikimedia Research Fund:
Online Images Amplify Gender Bias By *Douglas Guilbeault (Stanford University)* Each year, people spend less time reading and more time viewing images, which are proliferating online. Images from platforms like Google and Wikipedia are downloaded by millions every day, and millions more are interacting via social media like Instagram and TikTok that primarily consist of exchanging visual content. In parallel, news agencies and digital advertisers are increasingly capturing attention online through the use of visual content, which people process more quickly, implicitly, and memorably than text. In this paper, we show that the rise of images online significantly exacerbates gender bias, both in its statistical prevalence and its psychological impact. We examine the gender associations of 3,495 social categories (such as nurse or banker) in over one million images from Google, Wikipedia, and IMDb, as well as in billions of words from these platforms. We find that gender bias is stronger and more prevalent in images than text for both female- and male-typed categories. We further show that the documented underrepresentation of women online is worse in images compared to not only text, but also public opinion and US census data. Finally, we conducted a nationally representative, pre-registered experiment which shows that googling for images rather than textual descriptions of occupations amplifies gender bias in participants’ beliefs. Addressing the societal impact of this large-scale shift toward visual communication will be essential for developing a fair and inclusive future for the internet. Measuring the Gender GapBy *Tianwa Chen (The University of Queensland)*In this presentation, I would like to present our three research works aimed at measuring the gender gap on Wikipedia through data-driven strategies. Our first study explores the estimation of gender completeness within Wikipedia, offering a new methodology for assessing content gaps. The second study analyses the evolution of gender diversity, employing visualizations to track the gender distribution in Wikipedia articles categorized under ‘Person’ over time. The third and ongoing study delves into the gender balancing efforts among Wikipedia editors. We are currently conducting interviews within the editor community and planning to develop a dashboard through a co-design approach. These studies collectively advance our understanding of gender representation and provide actionable insights to foster gender equality in the Wikipedia community.Addressing Wikipedia’s Gender Gaps Through Social Media AdsBy *Reham AL Tamime (University of Strathclyde)*Wikipedia’s well-documented gender gap remains a persistent challenge, with women underrepresented among contributors. While past efforts—such as Edit-a-thons, workshops, and social media campaigns—have aimed to bridge this gap, more targeted approaches remain under-explored. In this talk, I will present our project, which explores the use of social media advertising to reach and recruit women as Wikipedia editors. I will share preliminary findings from our targeted advertisements on LinkedIn, where we designed a survey to assess the effectiveness of the reach of the advertisement. Building on these insights, I will discuss how we have expanded our approach to include multiple social media platforms, refined targeting strategies, and developed various messages to increase reach and eventually participation in Wikipedia. Best,Kinneret
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Kinneret Gordon
Lead Research Community Officer
Wikimedia Foundation https://wikimediafoundation.org/
*Learn more about Wikimedia Research https://research.wikimedia.org/*