Epidemiological Surprise: Adapting Outbreak Detection Methods to Spot Viral Marketing Flukes in Social Networks

SNA surveys (Singh et al., Golędzinowski & Błocki) and simulation frameworks (Rende et al.) highlight parallels between disease and information diffusion, but rarely leverage epidemiological anomaly detection directly. This project would adapt techniques like syndromic surveillance or spatial-temporal scan statistics—traditionally used to spot unusual disease outbreaks—to monitor real-time marketing or information campaigns. The novelty is in proactively identifying "viral flukes"—campaigns whose reach far exceeds (or falls short of) modeled expectations, possibly due to hidden network effects, content resonance, or bot activity. This synthesis not only brings more rigor to viral marketing analytics, but could also inform rapid-response strategies for both promotion and countering misinformation. It stands out by operationalizing epidemiological surprise in a commercial or social media context, making diffusion monitoring more sensitive and actionable.

References:

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