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arXiv · 2610.16907

Friends as Sensors: High-Sensitivity Monitoring of Misinformation and Platform Dynamics via the Friendship Paradox

Angermaier Mathias·Lasser Jana·Lewandowsky Stephan·Garcia David
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Abstract

The friends of a randomly chosen social media user are, on average, better connected than the user, a network property known as the friendship paradox. We use it to monitor misinformation on Twitter/X around the 2024 US election, comparing roughly 27 million tweets from random users with 31 million from the accounts they retweet, which we call sensors. Sensors share news of about the same quality as random users, because news quality follows the political orientation of linked outlets and not the user's position in the network. News links rated by NewsGuard reach sensors a median of 70 minutes earlier, across the quality spectrum. Over the election window, right-leaning content rises about 2.5-fold in visibility, measured as impressions per tweet, among sensors but not among random users. Friendship-paradox sampling therefore cannot separate low-quality from high-quality news, but it detects news early and tracks what a platform makes visible.

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Angermaier Mathias, Lasser Jana, Lewandowsky Stephan, Garcia David. 2026-10-12. Friends as Sensors: High-Sensitivity Monitoring of Misinformation and Platform Dynamics via the Friendship Paradox. https://arxiv.org/abs/2610.16907

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