2026-09-21T00:06:13Z https://www.ideals.illinois.edu/oai-pmh

oai:www.ideals.illinois.edu:2142/129430 2025-10-20 col_2142_5131 col_2142_10761 com_2142_5130 com_2142_10755 com_2142_234

application/pdf en text Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-10-19 without embargo terms The student, Margie Ruffin, accepted the attached license on 2025-04-22 at 18:33. The student, Margie Ruffin, submitted this Dissertation for approval on 2025-04-22 at 18:44. This Dissertation was approved for publication on 2025-04-23 at 09:41. DSpace SAF Submission Ingestion Package generated from Vireo submission #21904 on 2025-10-19 at 18:18:40 Towards the downstream effects of insufficient content moderation on the internet Ruffin, Margie 2025-04-23 Wang, Gang Wang, Gang Levchenko, Kirill Cobb, Camille Xiong, Aiping Content Moderation AI-generated media (images videos text) Copyright Violations Academic Integrity Violations eng Internet platforms rely heavily on content moderation to help drive positive engagement. Historically, this task has been done by humans, but in the recent decade, ML/AI models have been developed to reduce the burden by filtering out abusive content. While a step in the right direction, some moderation practices, manual and automated (e.g., context labeling, deepfake detection, and self-moderation), can still inadvertently cause harm to individual Internet users. To prevent the perpetuation of these harms, we must understand how they manifest regarding individuals’ perceptions, sentiments, and behaviors. This dissertation investigates how the ineffective content moderation practices of internet platforms affect how individuals respond to online abuse. Specifically, this dissertation explores how explanations (content-labeling) impact their viewers’ perceptions and sentiments (Chapter 3), how unmonitored deepfake-enabled profiles can sway an individual’s perception of and engagement with fake news (Chapter 4), and how low-moderation of platforms results in self-moderation to combat copyright violation harms (Chapter 5). In summary, these works show the downstream effects of deceitful and unauthorized content that slips through the moderation cracks, and assessing them is essential for developing better interventions. 2025-05 Thesis https://hdl.handle.net/2142/129430 Copyright 2025 Margie Ruffin Siebel School Comp & Data Sci Computer Science University of Illinois Urbana-Champaign Ph.D. Dissertation