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.

Creative coding refers to a method of computer programming that prioritizes artistic expression. Remixing is the iterative behavior of altering an existing artifact. Frameworks such as p5.js support sketching with creative code. Given the focus on expressivity over functionality, code reuse in creative coding practice is distinct from other programming contexts. Additionally, remixing facilitates iteration on existing code, but we have yet understand how creative coders use remixing in practice. To understand creative coder remixing strategies, we studied the community of OpenProcessing, a site dedicated to sharing code-generated artworks. To begin, we conducted a network analysis to determine which datasets of original sketches (also referred to as the antecedent sketch) and their remixes to use in our study. Our visualization consisted of a social network graph in which the nodes represent individuals, and edges showing their relationships. We found that 30% of the 1.2 million sketches in our dataset were involved in remixing. For data analysis, we utilized a code-diff tool to showcase ways the antecedent sketch's code differs from the remix and categorize various types of remixing strategies. Over time, these categories became increasingly focused on changes made visible on our code-diff tool. We present on the diversity of ways that authors remix to curate projects, annotate process, explore variations, and transform existing sketches. Through remixing, artists have already begun to tailor, customize, and explore different ways to use their creative tools, in ways system developers may not have foreseen. We find that remixing also encourages exploratory programming and experiential learning. As creative code is increasingly used to support computational education, we can consider the implications of remixing for understanding and facilitating informal learning. At last, we reflect on the prevalence of these remix types and how future systems could support a multiplicity of remixing strategies for creative work.