Survey on Methods of Opinion Leader Mining in Social Networks

Opinion leaders are those who have strong influence on the public in the process of communication, directly or indirectly influence public opinion tendency and formation. Opinion leader mining in social networks is a significant task, which has been widely used in the fields of commercial marketing,...

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Autor principal:	GUO Yi, XU Liang+, XIONG Xuejun
Formato:	article
Lenguaje:	ZH
Publicado:	Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press 2021
Materias:	social network opinion leader rating rules influence communication model graph neural network Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/a0227492127648eb8aa51e4b8a3d7826
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spelling	oai:doaj.org-article:a0227492127648eb8aa51e4b8a3d78262021-11-10T08:05:17ZSurvey on Methods of Opinion Leader Mining in Social Networks10.3778/j.issn.1673-9418.21040131673-9418https://doaj.org/article/a0227492127648eb8aa51e4b8a3d78262021-11-01T00:00:00Zhttp://fcst.ceaj.org/CN/abstract/abstract2948.shtmlhttps://doaj.org/toc/1673-9418Opinion leaders are those who have strong influence on the public in the process of communication, directly or indirectly influence public opinion tendency and formation. Opinion leader mining in social networks is a significant task, which has been widely used in the fields of commercial marketing, policy propaganda, public opinion monitoring, and social public issues. Firstly, the origin, definition and classification of opinion leaders are described. Then a more comprehensive summary of current opinion leader mining methods is presented, which is grouped into four categories: user rating rule-based methods, social network graph-based methods, influence propagation model-based methods, and multidimensional fusion methods. The basic ideas and key points of the above methods are described respectively, and the advantages and disadvantages of each method are analyzed. In addition, the recommended evaluation metrics are given by analyzing the existing evaluation metrics. Finally, three future research directions are discussed: improving mining efficiency and effectiveness through clustering mining methods using graph neural network, designing dynamic models to meet more time-sensitive application scenarios, and dividing opinion leader hierarchies to meet different levels of demand.GUO Yi, XU Liang+, XIONG XuejunJournal of Computer Engineering and Applications Beijing Co., Ltd., Science Pressarticlesocial networkopinion leaderrating rulesinfluence communication modelgraph neural networkElectronic computers. Computer scienceQA75.5-76.95ZHJisuanji kexue yu tansuo, Vol 15, Iss 11, Pp 2077-2092 (2021)
institution	DOAJ
collection	DOAJ
language	ZH
topic	social network opinion leader rating rules influence communication model graph neural network Electronic computers. Computer science QA75.5-76.95
spellingShingle	social network opinion leader rating rules influence communication model graph neural network Electronic computers. Computer science QA75.5-76.95 GUO Yi, XU Liang+, XIONG Xuejun Survey on Methods of Opinion Leader Mining in Social Networks
description	Opinion leaders are those who have strong influence on the public in the process of communication, directly or indirectly influence public opinion tendency and formation. Opinion leader mining in social networks is a significant task, which has been widely used in the fields of commercial marketing, policy propaganda, public opinion monitoring, and social public issues. Firstly, the origin, definition and classification of opinion leaders are described. Then a more comprehensive summary of current opinion leader mining methods is presented, which is grouped into four categories: user rating rule-based methods, social network graph-based methods, influence propagation model-based methods, and multidimensional fusion methods. The basic ideas and key points of the above methods are described respectively, and the advantages and disadvantages of each method are analyzed. In addition, the recommended evaluation metrics are given by analyzing the existing evaluation metrics. Finally, three future research directions are discussed: improving mining efficiency and effectiveness through clustering mining methods using graph neural network, designing dynamic models to meet more time-sensitive application scenarios, and dividing opinion leader hierarchies to meet different levels of demand.
format	article
author	GUO Yi, XU Liang+, XIONG Xuejun
author_facet	GUO Yi, XU Liang+, XIONG Xuejun
author_sort	GUO Yi, XU Liang+, XIONG Xuejun
title	Survey on Methods of Opinion Leader Mining in Social Networks
title_short	Survey on Methods of Opinion Leader Mining in Social Networks
title_full	Survey on Methods of Opinion Leader Mining in Social Networks
title_fullStr	Survey on Methods of Opinion Leader Mining in Social Networks
title_full_unstemmed	Survey on Methods of Opinion Leader Mining in Social Networks
title_sort	survey on methods of opinion leader mining in social networks
publisher	Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
publishDate	2021
url	https://doaj.org/article/a0227492127648eb8aa51e4b8a3d7826
work_keys_str_mv	AT guoyixuliangxiongxuejun surveyonmethodsofopinionleadermininginsocialnetworks
_version_	1718440403313623040

Survey on Methods of Opinion Leader Mining in Social Networks

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