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The ACM Web Conference 2026Short Papers

Deepfakes in the 2025 Canadian Election: Prevalence, Partisanship, and Platform Dynamics

Victor Livernoche, Andreea Musulan, Zachary Yang, Jean-François Godbout, Reihaneh Rabbany

PDF 由论文原始站点提供,PaperCompass 不保存论文文件。DOI 10.1145/3774904.3792948 ↗

摘要

Concerns about AI-generated political content are growing, yet there is limited empirical evidence on how deepfakes appear and circulate across social platforms during major events in democratic countries. We analyze the 2025 Canadian federal election across X, Bluesky, and Reddit using a high-accuracy detector trained on diverse modern generative models, covering 187,778 posts. We find that 5.9% of election-related images were deepfakes. Right-leaning accounts shared them more often (9.2% of images flagged) than left-leaning users (3.9%), with flagged content more frequently defamatory or conspiratorial. Yet, most detected deepfakes were benign or non-political, and harmful ones drew little attention, accounting for only 0.1% of all views on X. Overall, deepfakes were present in the election conversation, but their reach was modest, and realistic fabricated images, although less common, drew higher engagement, highlighting growing concerns about their misuses.