Reap what you sow? Competitive diffusion in social networks with heterogeneous opinions and relationships

Pei Li, Mian Wang (), Tingqin He () and Jianyong Yu ()
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Pei Li: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, P. R. China
Mian Wang: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, P. R. China
Tingqin He: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, P. R. China
Jianyong Yu: School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan 411201, P. R. China

International Journal of Modern Physics C (IJMPC), 2023, vol. 34, issue 12, 1-18

Abstract: Nowadays, an increasing number of people use social networks to receive up-to-date information and express their personal opinions, and popular social networks have become important platforms to conduct viral-marketing for many companies. However, due to the existences of negative opinions and hostile relationships, some spreading behaviors will receive much more undesired responses. To study this process of competitive diffusion, we consider heterogeneous opinions (positive and negative ones) and heterogeneous relationships (friendly and hostile ones), and assume the reaction of a user after receiving a message is determined by the received message type, his/her own opinion and the type of relationship between him/her and the neighbor who sends this message. We then modify the duplicate forwarding model to characterize the diffusion dynamics in competitive diffusion, and define the term positive (negative) user influence which is the mean number of positive (negative) messages received by users after a user generates a message. These user influences and the corresponding diffusion threshold can be analyzed theoretically, which are verified by simulations. We then study the impacts of different factors on user influences on some real networks, and observe that messages of some type are easier to be forwarded and received in a given network if the message spreading intensity approaches the diffusion threshold and users of this type have a larger average homophily factor. These findings can help to explain why a large number of boycotts may be attracted if a user or company publishes a post or advertisement in a social network, and we believe this analysis framework will be of use for advertisers to conduct viral-marketing.

Keywords: Competitive diffusion; heterogeneous opinions; heterogeneous relationships; user influences; social networks (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1142/S0129183123501577

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