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arXiv · 2512.13915

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

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

Concerns about AI-generated political content are growing, yet there is limited empirical evidence on how deepfakes actually appear and circulate across social platforms during major events in democratic countries. In this study, we present one of the first in-depth analyses of how these realistic synthetic media shape the political landscape online, focusing specifically on the 2025 Canadian federal election. By analyzing 187,778 posts from X, Bluesky, and Reddit with a high-accuracy detection framework trained on a diverse set of modern generative models, we find that 5.86% of election-related images were deepfakes. Right-leaning accounts shared them more frequently, with 8.66% of their posted images flagged compared to 4.42% for left-leaning users, often with defamatory or conspiratorial intent. Yet, most detected deepfakes were benign or non-political, and harmful ones drew little attention, accounting for only 0.12% 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 potential misuse.

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Victor Livernoche, Andreea Musulan, Zachary Yang, Jean-François Godbout, Reihaneh Rabbany. 2025-12-15. Deepfakes in the 2025 Canadian Election: Prevalence, Partisanship, and Platform Dynamics. https://doi.org/10.1145/3774904.3792948

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