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 <[email protected]> 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 > > -- > > Kinneret Gordon > > Lead Research Community Officer > > Wikimedia Foundation <https://wikimediafoundation.org/> > > > *Learn more about Wikimedia Research <https://research.wikimedia.org/>* > _______________________________________________ Wiki-research-l mailing list -- [email protected] To unsubscribe send an email to [email protected]
