Search engines are the primary gateways of information. However, the veracity of search results is often not considered before the search results are promoted to users. As the most popular video search engine, YouTube recommends misinformation on the treatment, spread, and origins of COVID-19, undermining public health efforts. Despite the global effects of COVID-19 misinformation, the majority of research is confined to the Global North, leaving the Global South behind. My research aims to qualitatively and quantitatively compare the exposure to COVID-19 misinformation on YouTube between a country in the Global North and Global South. I focused on the United States and South Africa, both of which have been heavily affected by the pandemic. Using 48 curated COVID-19 misinformation search queries, I systematically audited YouTube search results for 10 consecutive days with 12 programmed bots emulating “real” users in South Africa and the United States. This sock-puppet audit method ethically prevented harmful exposure to misinformation to real users and provided a scalable, controlled way to collect data. Then, I fact-checked the collected video results and trained a machine-learning model to scale the annotation process, allowing for the measurement of misinformation prevalence in different geolocations. My preliminary findings showed that YouTube search results differ by up to 20% between South Africa and the United States. Based on these early findings, I expect to see potential differences in the veracity and rankings of COVID-19 misinformation search results between users in South Africa and the United States. This research is the first to report a comparative investigation of COVID-19 misinformation on YouTube between a country in the Global North and Global South. It also establishes a novel method of conducting geolocation audits in different countries, paving the way for further audit research in the Global South and ensuring accountability for social media platforms.

The amount of online information is rapidly increasing, including an abundance of potentially misleading content. However, fact-checkers rely heavily on manual search methods to identify this content, leading to a significant investment of time and resources. While social media monitoring tools exist for platforms like Twitter and Facebook, such tools still need to be improved for video search platforms like YouTube. We collaborated with Africa's largest indigenous fact-checking organization – PesaCheck, during the 2022 Kenyan general election. The spread of rumors aimed at voter suppression gave an advantage to the tally of a specific presidential candidate, which created an urgent need for automated fact-checking systems. To address this issue, a team of undergraduates from the information school and computer science department developed a tool to automatically generate search queries related to significant events and topics for fact-checkers to monitor while providing them the flexibility to modify or create their queries. I was in charge of developing a method to identify video ids, extract the keywords with Natural Language Understanding (NLU), and the function that allowed users to choose keyword tags to characterize videos and then link them to the top search queries of the day. During the crucial period of the Kenyan election, ten full-time fact-checkers were using our tool to check and report falsities on the internet. During the four months of deploying this fact-checking tool, 42 misinformation discovery annotations were added by various fact-checkers, and over 500 misinformation queries were generated by the tool. As a result, the new Kenyan president emerged from one of the most competitive elections in the nation's history. Our research project will also be published as a case study paper to discover value-sensitive fact-checking systems through participatory design.