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Online platforms are riddled with harassment, which significantly impacts the well-being of users. Unfortunately, the content moderation solutions provided by platforms often disappoint end-users as they fail to equip individuals with sufficient controls for their personal situations. In this work, the author, who personally experienced a sustained harassment campaign on Twitter, decided to regain control by constructing an automated, personalized, and collaborative anti-harassment system to protect herself, which has proven itself to be effective. The experience of developing---and re-developing in the face of repeated platform API changes and restrictions---this personalized content moderation system highlights many design issues that make managing severe online harassment such a challenge and invites critical study of the power dynamics between large online platforms and individual users. Through this analysis, this report aims to inform better designs to help platforms more effectively protect victims. 

YouTube is the world's most widely used video platform, with over 70% of content viewed through algorithmic recommendations. While prior audits have examined polarization in YouTube's long-form video recommendations, the platform's fast-growing Shorts feature remains understudied. In this paper, we present the first large-scale audit comparing political content exposure and engagement dynamics across short-form and long-form videos on YouTube. We design a matched audit based on the insight that many news media organizations publish both short and long versions of the same content and collect 50,000 pairs of long-form and short-form video recommendations from both political and nonpolitcal seed videos. We analyze recommendations along several dimensions: the frequency of political recommendations, the diversity of retrieved videos, the engagement those videos receive, and finally, the partisan alignment between recommended videos and seed videos. Our results highlight fundamental differences between each algorithm, which we hope we can inform future research in analyzing the impact of YouTube recommendations.