New centrality measure for nodes based on user social status and behavior on Facebook

Abstract

Purpose 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. Design/methodology/approach 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’ 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. Findings 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. Originality/value 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.

The influence of public health information sharing on social media during the early COVID-19 outbreak

“…Network cluster is defined as the connection density of individuals connected with each other in a network (Huang et al , 2017). A high density in a network implies a large number of connections, and nodes in such a network have an increased possibility of being interconnected (Forouzandeh et al , 2018). When people are familiar with each other, they are more likely to provide help and reward each other (Joshi et al , 2017).…”

“…When people are familiar with each other, they are more likely to provide help and reward each other (Joshi et al , 2017). As has been described in network closure theory, when two individuals connect to the same third party, the third party serves as an extra path for information, and increases trust between the original two individuals; information shared thus becomes more efficient and effective (Burt, 2005; Forouzandeh et al , 2018). For simplicity, when A lists B as a friend, B lists C and C lists A as a friend, it may form a closed triangle.…”

“…According to previous studies, network cluster is one of the most critical factors in personal networks, because it influences the process and efficiency of information sharing (Yoganarasimhan, 2012). Regarding social media, the platform allows users to view friends in common with new connections and to easily increase their familiarity with those new connections (Forouzandeh et al , 2018; Kim and Lee, 2011). At this time, the relationship between network cluster and information sharing on social media is warranted.…”

Using User Behavior to Measure Privacy on Online Social Networks

“…Users' private information is disclosed not only through their voluntary disclosure but also through their social activities [38]. A user's behavior information may be used by other services, but users do not consider security issues such as central node detection [39] and recommendation services [40]. Belanger & Xu argue that researchers should "focus on actual behavior rather than personal intentions" when researching information privacy, considering that users' behavior may not always be reasonable in regard to privacy [41].…”

A new approach for node centrality evaluation based on residual network performance measurement

“…Identifying key nodes helps to discovery laws of information propagation. It is also of great practical value to measure the influence of nodes in social networks, which helps to design algorithms of influence propagation 6 and increase the accuracy of recommender systems for mining users' status and predicting users' behavior. 7 Furthermore, ranking the influence of nodes in structured populations helps to find influential invaders.…”