An Opinion-Leader Mining Method in Social Networks With a Phased-Clustering Perspective

Bo Zhang,Yanhong Bai,Qian Zhang,Jie Lian,Meizi Li

doi:10.1109/access.2020.2972997

Bo Zhang, Yanhong Bai + Show 3 more

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https://doi.org/10.1109/access.2020.2972997

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Abstract

Opinion-leader mining in social networks is a critical problem in research of the information dissemination process and in public opinion guidance and supervision. Not every social network user has a high probability to be an opinion leader. However, most mining methods identify opinion leaders among users in the whole network, which adds unnecessary calculations. To solve this problem, we propose a rank after clustering (RaC) algorithm to mine opinion leaders in social networks with a phased-clustering perspective, which has the following aspects: (1) Aiming to reduce the scale of calculation, the clustering stage clusters users in social networks using a K-means algorithm according to topological information to find the set of opinion leader candidates; (2) The ranking stage determines the user ranks of opinion leader candidates by both their activeness and influence, and we accumulate the followers' influence weighted by degree of attention when assessing user influence. In experiments, a new indicator, the C-value, and simulations based on the linear threshold model are used to evaluate the performance of the RaC algorithm. The results show that RaC is effective and accurate.

Highlights

Opinion leaders in social networks generally have significant effects on people in terms of thoughts, feelings, and actions [1]
Based on the K-means algorithm, we propose the method that can select the set of opinion leader candidates according to the properties extracted from the topology in the clustering phase
The number of users in the set of opinion leader candidates that we obtain may occasionally be fewer than the top-k users that we require

Summary

Introduction

Opinion leaders in social networks generally have significant effects on people in terms of thoughts, feelings, and actions [1]. The mining of opinion leaders is an important subject in the field of user analysis in social networks. It has been widely applied in the analysis and prediction of information diffusion, guidance and supervision of public opinion, and commercial development in social networks [2]–[4]. There are two methods in opinion-leader mining. The first is to detect opinion leaders based on social network features, i.e., topological information and user behavior data. It is often used to assess opinion leaders of an entire network. Some studies identify opinion leaders by the number of friends (connections), the quantity of information published, The associate editor coordinating the review of this manuscript and approving it for publication was Shouguang Wang

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Conclusion