MADS‐UC: Finding Key Users in Online Social Networks Through Users Activation Activeness and Combination Weighting MCDM

ABSTRACT In online social networks, identifying influential users is crucial for maintaining network stability and accelerating information dissemination. However, most of the existing research evaluate the influence of users according to the topological structure of local or global networks, which ignores the historical information and social interactions of users. In this work, we introduce a novel algorithm called MADS‐UC to pinpoint the influential super‐spreaders of information. Specifically, three kinds of activation influence between each pair of users are defined to better portray the mutual interactions with each other, and the proposed measure makes full consideration of the topological and historical information of networks, and the social interactions of users. Then, a hybrid multi‐attribute decision‐making algorithm is put forward to evaluate the influence of users, while the obtained three kinds of activation influence are introduced as basic indicators, and the weight for each indicator is determined from both subjective and objective dimensions. Finally, to achieve a good balance between algorithm accuracy and time complexity, the influential users are identified by considering the interactions between a user and other users within its influence range. We collect seven real‐world datasets from the Sina Weibo platform in 2024 about mobile product Honor, and a series of experiments is conducted to validate the effectiveness of the MADS‐UC algorithm. Experimental results show that MADS‐UC surpasses six widely used algorithms in robustness, sensitivity analysis, and distinguishing capability, which is useful in accelerating the dissemination of information in the product marketing process.