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

2025-12-15

AI Summary

Prior research lacks empirical evidence on the actual prevalence, partisan distribution, and dissemination impact of deepfakes in democratic elections. Method: This project conducts the first systematic analysis of 188,000 political posts across X, Bluesky, and Reddit during the 2025 Canadian federal election, employing a high-accuracy detection framework trained on multi-model synthetic data, integrated with cross-platform data collection, automated partisan labeling, and propagation influence attribution. Contribution/Results: We find that 5.86% of election-related images are deepfakes; right-wing accounts generate deepfakes at significantly higher rates (8.66%) than left-wing accounts (4.42%); although high-fidelity deepfakes constitute only 0.12% of total impressions on X, they elicit substantially greater user engagement—challenging the conventional assumption that harm scales linearly with reach. This study provides the first empirically grounded characterization of deepfake scale, ideological skew, and influence mechanisms in electoral contexts.

Technology Category

• Application Domains: Misinformation & Fake News
• Humans and AI: Voting
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Social Networks and Social Media: Media and governance, opinion dynamics, filter bubbles, polarization
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Web Mining and Content Analysis: Large pretrained models with web data

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.

Problem

• Analyzing deepfake prevalence in the 2025 Canadian election online discourse
• Examining partisan differences in sharing AI-generated political images
• Assessing the reach and engagement of harmful versus benign deepfakes

Innovation

• High-accuracy detection framework for diverse generative models
• Analyzed 187,778 posts across X, Bluesky, and Reddit
• Measured deepfake prevalence and engagement in election context