Multi-meme spreading dynamics based on users’ memory-based interest preferences

The diffusion and competition of memes play a fundamental role in shaping collective attention, opinion formation, and information ecosystems in online social networks. Existing studies on competitive information diffusion predominantly focus on the role of network structural properties, while largely overlooking the underlying mechanisms induced by users’ non-Markovian behavioral characteristics. To address this gap, we propose a multi-meme diffusion model that incorporates users’ memory-based interest preferences. By introducing a dual finite-attention mechanism consisting of a screen window and a memory window, the model captures the decision-making process through which users are triggered by memory and subsequently driven by interest preferences to forward memes. The results demonstrate that the proposed model successfully reproduces several empirical regularities, including the power-law distribution of meme popularity, as well as exponentially truncated power-law distributions for meme lifetimes and inter-forwarding time intervals. Our findings further reveal that appropriate levels of interest preference and memory capacity not only sustain meme diversity within the system but also enhance meme survival time and diffusion reach. Moreover, the interplay between real network structures and memory-based preference mechanisms facilitates the propagation of multiple competing memes. This work provides a computable framework for investigating multi-meme competition under finite attention, offering insights for public opinion governance and information ecosystem regulation.