 Hello everyone, my name is Rihama Tamimi and today I'm going to present our work in collaboration with Ingmar Weber on addressing Wikipedia's gender gaps through LinkedIn ads. In 2020, the Wikimedia Foundation have launched the movement strategy which stresses on the importance of creating a positive consumption and contribution experience on the Wikimedia platforms, regardless of gender. Women are underrepresented among Wikimedia's leadership and this has been well documented in the literature, as women view fewer pages per reading session than men do. Women are also underrepresented among Wikimedia's contribution, as male Wikimedia editors exceed female editors. In non-English language Wikimedia, women are also minority among Wikimedia editors. We have reviewed existing initiatives that aim to increase women representation in Wikipedia. To name a few, they are Project Rewrite, Art and Feminism, Whose Knowledge, Wikidon, Wikigab, Women Do News, and Women in Red. Our research goal is aligned with the previous initiatives in which it's also aimed to increase female representation in Wikipedia. But our goal is also via using a novel strategy which is LinkedIn ads. In particular, we aim to understand options to identify and contact potential Wikimedia editors and in turn to see if the targeting can lead to successful recruitment and retention of female editors. We also want to use targeted advertising on LinkedIn to find and contact women who are interested in becoming Wikimedia editors. To show you an overflow of the research study, the main goal is to identify women who are qualified to edit articles on a niche topic via LinkedIn ad targeting and to invite women to contribute to Wikimedia based on their expertise. At the first step to achieve this goal, we conducted a feasibility test which aims to run a survey on LinkedIn ad to better understand how the targeting works. And to better estimate the reach on LinkedIn via a niche topic and we selected machine learning to experiment with a niche topic. To show you an overview of the LinkedIn ad targeting, LinkedIn allows advertisers to select different targeting criteria, such as locations, different kinds of demographics and different types of skills. And in turn, LinkedIn provides advertisers the estimated audience size. For our feasibility test, we have selected locations to be the United States, gender to be female and skills to be machine learning and Wikimedia. The target audience size based on these targeting criteria was more than 1,200 LinkedIn members. The targeted audience size on LinkedIn will be able to see this advertisement. This advertisement asks LinkedIn members if or whether they would like to join a project and answer our survey. And if they wish to do so, they would simply have to click join. They will be directed to a survey link. This survey asks questions on different constructs, including demographics, such as gender and location, expertise, which aims to ask respondents to send further expertise in machine learning ranging from non-religion to expert, reading Wikibiti articles to ask respondents about the frequency and reasons to read Wikibiti articles and fourth, editing Wikibiti articles, which aims to ask respondents about their willingness, rare experience and reasons to edit Wikibiti articles. Running the survey for around one week, the survey has received impressions and clicks, but participants didn't progress to completing the survey. The LinkedIn ad has received a total of 4,936 impressions and 25 survey website visits. As a future work, we aim to test a larger set of targeting criteria. And also, we would like to experiment with other advertising platforms to recruit underrepresented groups into the pool of Wikibiti editors. The Wikibiti workshop is a great opportunity to discuss alternative methods or strategies to contribute to narrowing the gender gaps in Wikipedia, and to invite collaborators and stakeholders to join the project. I would like to thank you and please don't hesitate to contact us if you have any other queries. Thank you.