When information dissemination campaigns on Online Social Networking platforms are too aggressive, this can easily cause information overexposure. Overexposure can break through the psychological and physiological limits that the audience can tolerate, making it difficult for the audience to obtain reasonable cognitive concepts. For example, the overexposure of information referring to an object in a promotional campaign can lead to inflated expectations in individuals. Building on this, we introduce two indicators for individuals’ expectations and actual utility of the object and design a multi-stage triggered mechanism for seed individuals to explore the relieving overexposure problem in information diffusion. We build a multi-stage information diffusion model and characterize the evolution of individual expectations. We verify the hardness result of the relieving overexposure problem by budget multi-stage allocation, and the non-submodularity and non-monotonicity of the objective function. Addressing the non-monotonic and non-submodular set function, we provide a direct influence-oriented algorithm with a greedy approach. Extensive experiments are performed on four real networks to explore how model parameters and network properties affect the effects of multi-stage triggered strategies for seed individuals. Using the experiments, we found that the seed individual multi-stage incremental triggered strategy of dissemination campaign of information referring to an object shows better performance, and the lower the actual utility of the specific object, the more accurate the promotion strategy needs to be developed.

The continuous development of network media technology has driven the unceasing change in the online social environment, from PC social to mobile social, which are currently experiencing a new change: Metaverse social. The iteration of the social environment gives impetus to the uninterrupted upgrading of social patterns, which leads to the ceaseless innovation of the product promotion model. In this article, motivated by the characteristics of the Metaverse social, we devise virtual activity-oriented product promotion tactics. We propose an activity-oriented promotion utility maximization problem and tackle it systematically. We demonstrate the complexity and inapproximability of the problem. A continuity approximate concave relaxation method is devised to optimize the set function with a supermodularity ratio. We develop an approximate projected subgradient procedure to obtain the solution with an approximate factor guarantee. Experiments on two types (traditional social network and Metaverse social network) of real-world datasets verify the feasibility and scalability of our algorithm and model, and the research results have guiding significance for the online promotion of products.

With the in-depth development of intelligent media technology, online and offline fusion, reality and virtual entanglement, information content generalization, the boundary between positive and negative information is blurred, all kinds of misinformation in the social network fission spread, and cyberspace governance has become a global consensus. In this article, we comprehensively consider the spread of misinformation in location-based interpersonal social network and online social network, and systematically tackle the novel problem of minimizing the influence of misinformation under individual protection strategies. We first analyze the complexity and modularity of the problem. Then, we leverage the Lovász extension to devise a nonsubmodular set function continuity approximate convex relaxation method, and develop an approximate projected subgradient procedure to obtain a solution with a factor approximate guarantee. Finally, experiments on three assembled real-world datasets demonstrate the effectiveness and feasibility of our designed method and developed the algorithm.

An echo chamber effect describes the situation in which opinions are amplified by communication and repetition inside a relatively closed social system. In this article, we will detect the echo chamber effect in real-world data set and measure this effect during the information diffusion process. Any user will be influenced by its neighbors or echo chamber effect. Also, we assume that these activation events from each activated neighbor and from echo chamber effect are independent. In this article, we detect and model the echo chamber effect for the first time. Then, the influence maximization with echo chamber (IMEC) problem aims to select$k$users to propagate information such that the expected number of activated users is maximized. We formulate this problem using a graph model and analyze the NP-hardness. Second, the objective of IMEC as a set function is proved to be neither submodular nor supermodular. Then, an improved greedy algorithm is proposed, which is combined metaheuristic strategies. Finally, experimental results show that our algorithm is effective in detecting echo chamber effect and efficiency in selecting seed nodes.

In recent years, online social media has flourished, and a large amount of information has spread through social platforms, changing the way in which people access information. The authenticity of information content is weakened, and all kinds of misinformation rely on social media to spread rapidly. Network space governance and providing a trusted network environment are of critical significance. In this article, we study a novel problem called activity minimization of misinformation influence (AMMI) problem that blocks a node set from the network such that the total amount of misinformation interaction between nodes (TAMIN) is minimized. That is to say, the AMMI problem is to select K nodes from a given social network G to block so that the TAMIN is the smallest. We prove that the objective function is neither submodular nor supermodular and propose a heuristic greedy algorithm (HGA) to select top K nodes for removal. Furthermore, in order to evaluate our proposed method, extensive experiments have been carried out on three real-world networks. The experimental results demonstrate that our proposed method outperforms comparison approaches.