Identifying Influencers in Online Social Networks

2013 ◽  
Vol 9 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Yifeng Zhang ◽  
Xiaoqing Li ◽  
Te-Wei Wang
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Word Of Mouth ◽  
Agent Based Modeling ◽  
Viral Marketing ◽  
Tie Strength ◽  
Agent Based ◽  
Widespread Acceptance ◽  
Tremendous Amount ◽  
Marketing Messages

Online social networks (OSNs) are quickly becoming a key component of the Internet. With their widespread acceptance among the general public and the tremendous amount time that users spend on them, OSNs provide great potentials for marketing, especially viral marketing, in which marketing messages are spread among consumers via the word-of-mouth process. A critical task in viral marketing is influencer identification, i.e. finding a group of consumers as the initial receivers of a marketing message. Using agent-based modeling, this paper examines the effectiveness of tie strength as a criterion for influencer identification on OSNs. Results show that identifying influencers by the number of strong connections that a user has is superior to doing so by the total number of connections when the strength of strong connections is relatively high compared to that of weak connections or there is a relatively high percentage of strong connections between users. Implications of the results are discussed.

Information diffusion in structured online social networks

2015 ◽  
Vol 29 (13) ◽  
pp. 1550063 ◽  
Author(s):  
Pei Li ◽  
Yini Zhang ◽  
Fengcai Qiao ◽  
Hui Wang
Keyword(s):  
Social Networks ◽  
Theoretical Analysis ◽  
Online Social Networks ◽  
Information Diffusion ◽  
Word Of Mouth ◽  
Viral Marketing ◽  
User Influence ◽  
Diffusion Dynamics ◽  
Approach Infinity ◽  
Simulation Results

Nowadays, due to the word-of-mouth effect, online social networks have been considered to be efficient approaches to conduct viral marketing, which makes it of great importance to understand the diffusion dynamics in online social networks. However, most research on diffusion dynamics in epidemiology and existing social networks cannot be applied directly to characterize online social networks. In this paper, we propose models to characterize the information diffusion in structured online social networks with push-based forwarding mechanism. We introduce the term user influence to characterize the average number of times that messages are browsed which is incurred by a given type user generating a message, and study the diffusion threshold, above which the user influence of generating a message will approach infinity. We conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of use in understanding the diffusion dynamics in online social networks and also critical for advertisers in viral marketing who want to estimate the user influence before posting an advertisement.

Handling big data on agent-based modeling of Online Social Networks with MapReduce

2014 ◽  
Author(s):  
Maira A. de C. Gatti ◽  
Marcos R. Vieira ◽  
Joao Paulo F. de Melo ◽  
Paulo Rodrigo Cavalin ◽  
Claudio Santos Pinhanez
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Online Social Networks ◽  
Agent Based Modeling ◽  
Agent Based

Social Media Analytics

2013 ◽  
pp. 212-228
Author(s):  
Sunil Kr Pandey ◽  
Vineet Kansal
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Online Social Networks ◽  
Medical Science ◽  
Multimedia Data ◽  
Social Media Analytics ◽  
Multimedia Networks ◽  
Linkage Data ◽  
Insurance Sector ◽  
Tremendous Amount

Many popular online social networks such as Twitter, LinkedIn, and Facebook have become increasingly popular. In addition, a number of multimedia networks such as Flickr have also seen an increasing level of popularity in recent years. Many such social networks are extremely rich in content, and contain tremendous amount of content and linkage data which can be leveraged for analysis. The linkage data is essentially the graph structure of the social network and the communications between entities; whereas the content data contains the text, images and other multimedia data in the network. The growth of the usage and penetration of social media in the recent years has been enormous and unprecedented. This significant increase in its usage and increased number of users, there has been trend of a substantial increase in the volume of information generated by users of social media. Irrespective of primary domain in which organization is operating in to, whether it is insurance sector, social media (including facebook, twitter etc), medical science, banking etc. Virtually a large number of varying nature and services of organizations are making significant investments in social media. But it is also true that many are not systematically analyzing the valuable information that is resulting from their investments. This chapter aims at providing a data-centric view of online social networks; a topic which has been missing from much of the literature and to draw unanswered research issues which can be further explored to strengthen this area.

Word-of-Mouth Recommendations in an Automobile Market System

2019 ◽  
Author(s):  
Amineh Zadbood ◽  
Nicholas Russo ◽  
Steven Hoffenson
Keyword(s):  
Social Networks ◽  
Word Of Mouth ◽  
Consumer Preferences ◽  
Random Network ◽  
Network Models ◽  
System Level ◽  
Purchasing Decisions ◽  
Eigenvector Centrality ◽  
Agent Based ◽  
Consumer Interactions

Abstract Improving design in the context of market systems requires an understanding of how consumers learn about and evaluate competing products. Marketing models frequently assume that consumers choose the product with the highest utility, which provides businesses insights into how to design and price their products to maximize profits. While recent research has shown the impacts of consumer interactions within social networks on their purchasing decisions, they typically model market systems using a top-down approach. This paper applies an agent-based modeling approach with social network models to investigate the extent to which word-of-mouth (WOM) communications are influential in changing consumer preferences and producer market performance. Using a random network, we study the effects of the number of referrals for a product and the degrees of similarity between the senders and receivers of referrals on purchase decisions. In addition, the eigenvector centrality metric is used to analyze the spread of WOM referrals. The simulation results show that the most influential consumers in the network can create significant shifts in the market share, and a statistical analysis reveals a significant change in the system-level metrics of interest for the competing firms when WOM recommendations are included. The findings incentivize producers to invest in supporting their product development efforts with rigorous social networks analysis so as to increase their market success.

A framework of multilayer social networks for communication behavior with agent-based modeling

SIMULATION ◽  
2013 ◽  
Vol 89 (7) ◽  
pp. 810-828 ◽  
Author(s):  
Yuanzheng Ge ◽  
Liang Liu ◽  
Xiaogang Qiu ◽  
Hongbin Song ◽  
Yong Wang ◽  
...  
Keyword(s):  
Social Networks ◽  
Agent Based Modeling ◽  
Communication Behavior ◽  
Agent Based ◽  
Multilayer Social Networks
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