Background
Each day users are surfing around different social media platforms. Tweeting, likes, retweeting: All those actions contribute to information diffusion, and thus creating the chain effect. No matter if it’s a meme, a topic or a trend of life, information spreads quickly through cascades.
Abstract
This study provides a review of the factors influencing information propagation and their impact on the relationship between user similarity and retweeting behavior. At the core of social network dynamics, information diffusion is characterized by two distinct patterns: “broadcast” and “viral” propagation. While empirical evidence confirms that interest similarity generally correlates positively with retweeting probability, this relationship is not static and is significantly moderated by the depth of the diffusion process. Research indicates that as information spreads deeper into a network, the marginal influence of interest similarity tends to diminish. Furthermore, achieving deep diffusion requires more than just high-profile initial exposure; it relies on a confluence of factors, including audience-content matching, repeated exposure, reciprocal relationships, and favorable network paths. By clustering users into distinct profiles, this research demonstrates that different types of users play varied roles in the propagation chain, suggesting that effective communication strategies must move beyond simple follower counts to integrate network mechanisms, exposure patterns, and specific strategic objectives.