{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T23:10:29Z","timestamp":1776121829413,"version":"3.50.1"},"reference-count":51,"publisher":"Emerald","issue":"2","license":[{"start":{"date-parts":[[2018,6,18]],"date-time":"2018-06-18T00:00:00Z","timestamp":1529280000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJWIS"],"published-print":{"date-parts":[[2018,6,18]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>This paper aims to analyze the role of influential nodes on other users on Facebook social media sites by social and behavioral characteristics of users. Hence, a new centrality for user is defined, applying susceptible-infected recovered (SIR) model to identify influence of users. Results show that the combination of behavioral and social characteristics would be determined the most influential users that influence majority of nodes on social networks.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>In this paper, the authors define a new centrality for users, considering node status and behaviors. Thus, this node has a high level of influence. Node social status includes node degree, clustering coefficient and average neighbors\u2019 node, and social status of node refers to user activities on Facebook social media website such as sending posts and receiving likes from other users. According to social status and user activity, the new centrality is defined. Finally, through the SIR model, the authors explore infection power of nodes and their influences of other node in the network.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>Results show that the proposed centrality is more effective than other centrality approaches, infecting more nodes in social network. Another significant point in this research is that users who have high social status and activities on Facebook are more influential than users who have only high social status on the Facebook social media.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The influence of user on others in social media includes two key factors. The first factor is user social status such as node degree and clustering coefficient in social media graph and the second factor is related to user social activities in social media sites. Most centralities focused on node social status without considering node behavior. This paper analyzes the role of influential nodes on other users on Facebook social media site by social and behavioral characteristics of users.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijwis-07-2017-0053","type":"journal-article","created":{"date-parts":[[2018,5,2]],"date-time":"2018-05-02T19:11:06Z","timestamp":1525288266000},"page":"158-176","source":"Crossref","is-referenced-by-count":29,"title":["New centrality measure for nodes based on user social status and behavior on Facebook"],"prefix":"10.1108","volume":"14","author":[{"given":"Saman","family":"Forouzandeh","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amir","family":"Sheikhahmadi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Atae","family":"Rezaei Aghdam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuxiang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","reference":[{"key":"key2020092910371909400_ref001","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1007\/978-1-4419-8462-3_7","article-title":"A survey of models and algorithms for social influence analysis","volume-title":"Social Network Data Analytics","year":"2011"},{"key":"key2020092910371909400_ref002","first-page":"181","article-title":"Marketing based on user behavior in Facebook social network through recommender system design","volume-title":"IEEE International Congress on Technology, Communication and Knowledge (ICTCK)","year":"2015"},{"key":"key2020092910371909400_ref003","first-page":"752","article-title":"A learning-based model for predicting information diffusion in social networks: case of Twitter","volume-title":"IEEE International Conference on Control, Decision and Information Technologies (CoDIT)","year":"2016"},{"key":"key2020092910371909400_ref004","volume-title":"Studying Facebook via Data Extraction: The Netvizz Application","year":"2013"},{"key":"key2020092910371909400_ref005","first-page":"159","article-title":"Betweenness centrality updation and community detection in streaming graphs using incremental algorithm","year":"2017"},{"key":"key2020092910371909400_ref006","article-title":"Inferring user interests in the twitter social network","volume-title":"Publication Rights Licensed to ACM","year":"2014"},{"key":"key2020092910371909400_ref007","first-page":"573","article-title":"Representation learning for information diffusion through social networks","volume-title":"Proceedings of the Ninth ACM International Conference on Web Search and Data Mining - 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