Abstract
Aiming at the shortcomings of some existing interval-valued intuitionistic fuzzy entropy, this paper proposes an interval-valued intuitionistic fuzzy entropy, which contains not only the interval distance between membership and nonmembership but also the average hesitancy information. On this basis, the impact evaluation of microblog users is studied. Through the multilevel decomposition of user influence, the coverage of microblog, user interaction, and user activity are constructed as the first level indicators, and nine secondary indicators are selected as the comprehensive evaluation index system of microblog influence. Considering the highly dynamic and unstructured characteristics of social network data, the idea of interval-valued intuitionistic fuzzy is introduced to transform the evaluation of social network users’ influence into interval-valued intuitionistic fuzzy multiattribute group decision-making problem. Finally, the model is applied to dynamic evaluation of Sina Weibo users’ influence to verify the effectiveness of the model.
Highlights
A network is composed of several nodes and links connecting these nodes, which represents many objects and their relationship
At present, combing the relevant literature, we find that there are various methods to measure the influence of social network users
An interval-valued intuitionistic fuzzy (IVIF) set is an object having the form: A~ = f∣x ∈ Xg where the function μ~A~ : X → Intð1⁄20, 1Þ defines the degree of membership and ν~A~ : X → Intð1⁄20, 1Þ defines the degree of nonmembership of the element xi ∈ X, respectively, and for every xi ∈ X, it holds that 0 ≤ sup μ~A~ ðxÞ + sup ν~A~ ðxÞ ≤ 1
Summary
A network is composed of several nodes and links connecting these nodes, which represents many objects and their relationship. How to effectively evaluate the influence of users in social networks and find out “opinion leaders” play an extremely important role in information dissemination, public opinion guidance, advertising recommendation, user recommendation, and so on. Reasonable measurement of social network users’ influence is of great significance to improve the marketing effect, guide the healthy development of public opinion, and maintain the network order. This has aroused the interest of many researchers [21,22,23,24,25,26]. It has many applications in reflecting the weight of information [28,29,30]
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