The spread of misinformation has increased rapidly in the last few years on many social media platforms. Our understanding of its effects, strategies, and influence is growing along with the information in real time. During political elections, we have seen that misinformation can become contagious and pose harmful threats to many aspects of our society and our political environment. How exactly does misinformation disseminate online, and are these social media posts used as a political strategy? To delve deeper into this study, I examine the relationship between false information online and legal cases that challenge election results. Using a mix of qualitative and quantitative methods, I analyze articles, data, and social media posts concerning the 2020 and 2022 elections in Maricopa County. With this data, I identify recurring narratives and influential political figures, using Python visualizations and codebooks for the empirical evidence found online. I anticipate that my findings reveal a pattern of legal cases being used to spread false political narratives that mislead the public about the voting process in Maricopa County. Since Maricopa County is the fourth most populous county in the United States, this study provides insight into how online users receive information about political elections and voting processes. I also anticipate that utilizing courts and election lawsuits can be an effective strategy to uphold and spread misinformation. Further research into other counties may demonstrate similar patterns and narratives with misinformation and U.S. elections.

Software services often depend on storing or processing users' personal data. To promote responsible handling of this information, modern privacy legislation such as the General Data Protection Regulation and California Consumer Privacy Act impose strict regulation around demonstrable privacy enforcement for personal data. In addition to privacy legislation, increased social emphasis on accountability for privacy policies and individual user preferences has added requirements to many systems. This landscape creates interest in technical mechanisms for privacy compliance. Traditional privacy methods such as encryption or anonymization are important, but not sufficient, to address the more nuanced aspects of privacy regulations and policies such as data use requirements based on purpose, fine-grained personal data control, or obligations. An influx of research in both industry and academia seeks to confront this challenge of policy-based privacy enforcement. However, the interdisciplinary nature of privacy, wide variety of approaches, and common gap between theory and software development makes it difficult to navigate the space. To help, this research project presents a systematization of policy-based privacy enforcement with a focus on practical software mechanisms, implementations and frequently adopted privacy-by-policy design patterns. It considers deriving software requirements from natural language requirements, expressing privacy conditions in privacy languages, managing data access with privacy conditions, restricting data flow for privacy, and leveraging logs and audits. Within these domains, the research project explores common approaches, mechanisms, and methodologies and further describes key insights, gaps, and future directions for policy-based privacy enforcement. 

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.

There are an estimated 500,000 people in the U.S. who are deaf and use American Sign Language (ASL). Compared to the general population, deaf people are at greater risk of having chronic health problems and experience significant health disparities and inequities (Sanfacon, Leffers, Miller, Stabbe, DeWindt, Wagner, & Kushalnagar, 2020; Kushalnagar, Reesman, Holcomb, & Ryan, 2019; Kushalnagar & Miller, 2019). The longstanding history of inequitable access to language and education, and a lack of printed information and materials, leave people who are deaf and who use ASL unaware of opportunities to participate in cutting-edge research/clinical trials (Kushalnagar & Miller, 2019; Lesch, Brucher, Chapple, R., & Chapple, K., 2019; Smith & Chin, 2012). An unintended consequence, therefore, is that Principle Investigators (PIs) neglect to include ASL signers who are deaf in their subject sample pools, and this marginalized population continues to be at disparity for both health outcomes and clinical research participation. One barrier is the unavailability of informed consent materials that are accessible in ASL. The current research study conducted by our team at the Center for Deaf Health Equity at Gallaudet University attempts to address the language barrier to the consent process through a careful reconsideration of its traditional English format and the development of an American Sign Language (ASL) informed consent app. As part of the project, I successfully leveraged existing machine learning methods to develop a way to navigate and signature an informed consent process using ASL. I call this new method of navigation and signature “ASL Interactability.” In my findings, I found that deaf people who are primarily college educated felt that the process for obtaining ASL consent through an accessible app is just as fluid and easy to understand as traditional English consent. These findings not only show the potential of ASL Interactability in the informed consent process, but in any other digital application that requires the user to interact (e.g. to move between pages, to provide signature, etc).

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.

Throughout the US higher education system, student technology fee programs have played a crucial role in creating accessible and critical technology services for students. However, existing studies in the field of educational Information Technology (IT) services have focused primarily on studying the preferences of students and provost-leveled administrators through surveys. This leaves the statistical analysis of how fee grants are allocated by the fee committee a gap in research. This study aims to visualize and quantify how the Student Technology Fee at the University of Washington (UWSTF) has allocated $30 million of funding from 2016 to 2022 to understand its impact on the UW. A database of over 550 proposals from the request-for-proposal website was analyzed. Interactive visualization methods were applied to the data using Tableau analytics software. An outcome model of the proposal assessment system was also constructed by setting the funding result of the proposal as the outcome variable and all other factors, such as proposal title, category, and proposal written prompts as the input variables. The tools and methods of this analysis include regressions and textual modeling and will be made open-source to allow easy repetition of this study at other institutions. The visualization and outcome modeling created by this study would uncover how STF funding flows through the UW and the correlation between the various proposal characteristics and the decision outcome of the proposal. We can then begin to understand the implicit ways by which proposals are judged and the resulting impact of these decisions on UW. This allows us to both impact the UW by providing UWSTF with the information needed to make informed decisions and also to carry these findings and analysis methodology into the field of educational Information Technology to improve student technology fees at other educational institutions.