Viral social media campaigns can generate millions of dollars in donations, yet predicting their spread remains challenging. This study applies epidemiological models to analyze the 2014 ALS Ice Bucket Challenge (IBC) using published data, which models the campaign using Susceptible-Infected-Recovered (SIR) differential equations, branching process theory, and network epidemic frameworks. Using the reported values for the basic reproduction number R₀=1.43 and serial interval τ=2.1 days, I calculated the transmission rate and recovery rate. I also used branching process and SIR eigenvalue analysis to verify R₀. Using graph theory metrics (degree centrality, clustering coefficient, and network heterogeneity), I analyzed how a scale-free topology can amplify spread.

Extending this framework, this study considers why the similar Rice Bucket Challenge (RBC) did not achieve comparable global impact. I develop an exponential barrier model p(b)=p₀e^(-b) to quantify participation costs. This model predicts a reproduction number R₀ ≈ 0.70 < 1, which is consistent with observed limited diffusion. The comparison highlights five factors associated with viral success: 1) R₀ > 1; 2) short serial intervals; 3) favorable network topology; 4) low participation barriers; and 5) strategic seeding among highly connected individuals. These findings suggest that epidemiological models can provide a useful framework for forecasting the virality of social media campaigns.

Peer reviewed papers mentioned in the presentation: https://pmc.ncbi.nlm.nih.gov/articles/PMC4267700/ https://www.researchgate.net/publication/327821082_Viral_Ice_Buckets_A_Memetic_Perspective_on_the_ALS_Ice_Bucket_Challenge’s_Diffusion