Delay-Driven Information Diffusion in Telegram: Modeling, Empirical Analysis, and the Limits of Competition

Information diffusion models developed for Twitter, Reddit, and Facebook assume network contagion and competition for shared attention. Telegram operates differently. It is built around channels rather than social graphs, and users receive posts directly from subscribed channels without algorithmic mediation. We analyze over 5000 forwarding cascades from the Pushshift Telegram dataset to examine whether existing diffusion models generalize to this broadcast environment. Our findings reveal fundamental structural differences. Telegram forwarding produces perfect star topologies with zero multi-hop propagation. Every forward connects directly to the original message, creating trees with maximum depth of exactly 1. This contrasts sharply with Twitter retweet chains that routinely reach depths of 5 or more hops. Forwarding delays follow heavy-tailed Weibull or lognormal distributions with median delays measured in days rather than hours. Approximately 15 to 20 percent of cascades exhibit administrative bulk reposting rather than organic user-driven growth. Most strikingly, early-stage competitive overtaking is absent. Six of 30 pairs exhibit crossings, but these occur late (median 79 days) via administrative bursts rather than organic competitive acceleration during peak growth. We develop a delay-driven star diffusion model that treats forwarding as independent draws from a delay distribution. The model achieves median prediction errors below 10 percent for organic cascades. These findings demonstrate that platform architecture fundamentally shapes diffusion dynamics. Comparison with prior studies on Twitter, Weibo, and Reddit reveals that Telegram’s broadcast structure produces categorically different patterns—including perfect star topology and asynchronous delays—requiring platform-specific modeling approaches rather than network-based frameworks developed for other platforms.