Social media have dramatically changed the mode of information dissemination. Various models and algorithms have been developed to model information diffusion and address the influence maximization problem in complex social networks. However, it appears difficult for state-of-the-art models to interpret complex and reversible real interactive networks. In this paper, we propose a novel influence diffusion model, i.e., the Operator-Based Model (OBM), by leveraging the advantages offered from the heat diffusion based model and the agent-based model. The OBM improves the performance of simulated dissemination by considering the complex user context in the operator of the heat diffusion based model. The experiment obtains a high similarity of the OBM simulated trend to the real-world diffusion process by use of the dynamic time warping method. Furthermore, a novel influence maximization algorithm, i.e., the Global Topical Support Greedy algorithm (GTS-Greedy algorithm), is proposed corresponding to the OBM. The experimental results demonstrate its promising performance by comparing it against other classic algorithms.

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AI recommendation techniques provide users with personalized services, feeding them the information they may be interested in. The increasing personalization raises the hypotheses of the "filter bubble" and "echo chamber" effects. To investigate these hypotheses, in this paper, we inspect the impact of recommendation algorithms on forming two types of ideological isolation, i.e., the individual isolation and the topological isolation, in terms of the filter bubble and echo chamber effects, respectively. Simulation results show that AI recommendation strategies severely facilitate the evolution of the filter bubble effect, leading users to become ideologically isolated at an individual level. Whereas, at a topological level, recommendation algorithms show eligibility in connecting individuals with dissimilar users or recommending diverse topics to receive more diverse viewpoints. This research sheds light on the ability of AI recommendation strategies to temper ideological isolation at a topological level.