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What are the key-features that enable an information diffusion model toexplain the inherent dynamic, and often competitive, nature of real-worldpropagation phenomena? In this paper we aim to answer this question byproposing a novel class of diffusion models, inspired by the classic LinearThreshold model, and built around the following aspects: trust/distrust in theuser relationships, which is leveraged to model different effects of socialinfluence on the decisions taken by an individual; changes in adopting one oralternative information items; hesitation towards adopting an information itemover time; latency in the propagation; time horizon for the unfolding of thediffusion process; and multiple cascades of information that might occurcompetitively. To the best of our knowledge, the above aspects have never beenunified into the same LT-based diffusion model. We also define differentstrategies for the selection of the initial influencers to simulatenon-competitive and competitive diffusion scenarios, particularly related tothe problem of limitation of misinformation spread. Results on publiclyavailable networks have shown the meaningfulness and uniqueness of our models.

Trust-based dynamic linear threshold models for non-competitive and competitive influence propagation