Discovering the influential users oriented to viral marketing based on online social networks

2013 ◽  
Vol 392 (16) ◽  
pp. 3459-3469 ◽  
Author(s):  
Zhiguo Zhu
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Viral Marketing ◽  
Influential Users

scholarly journals Cross-site Prediction on Social Influence for Cold-start Users in Online Social Networks

2021 ◽  
Vol 15 (2) ◽  
pp. 1-23
Author(s):  
Qingyuan Gong ◽  
Yang Chen ◽  
Xinlei He ◽  
Yu Xiao ◽  
Pan Hui ◽  
...  
Keyword(s):  
Social Networks ◽  
Social Influence ◽  
Online Social Networks ◽  
Cold Start ◽  
Learning Technologies ◽  
Viral Marketing ◽  
Supervised Machine Learning ◽  
Activity Data ◽  
Public Data ◽  
Influential Users

Online social networks (OSNs) have become a commodity in our daily life. As an important concept in sociology and viral marketing, the study of social influence has received a lot of attentions in academia. Most of the existing proposals work well on dominant OSNs, such as Twitter, since these sites are mature and many users have generated a large amount of data for the calculation of social influence. Unfortunately, cold-start users on emerging OSNs generate much less activity data, which makes it challenging to identify potential influential users among them. In this work, we propose a practical solution to predict whether a cold-start user will become an influential user on an emerging OSN, by opportunistically leveraging the user’s information on dominant OSNs. A supervised machine learning-based approach is adopted, transferring the knowledge of both the descriptive information and dynamic activities on dominant OSNs. Descriptive features are extracted from the public data on a user’s homepage. In particular, to extract useful information from the fine-grained dynamic activities that cannot be represented by the statistical indices, we use deep learning technologies to deal with the sequential activity data. Using the real data of millions of users collected from Twitter (a dominant OSN) and Medium (an emerging OSN), we evaluate the performance of our proposed framework to predict prospective influential users. Our system achieves a high prediction performance based on different social influence definitions.

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.

scholarly journals Social networks that matter: Twitter under the microscope

First Monday ◽  
2008 ◽  
Author(s):  
Bernardo Huberman ◽  
Daniel M Romero ◽  
Fang Wu
Keyword(s):  
Social Networks ◽  
Social Interactions ◽  
Online Social Networks ◽  
Viral Marketing ◽  
Social Bond

Scholars, advertisers and political activists see massive online social networks as a representation of social interactions that can be used to study the propagation of ideas, social bond dynamics and viral marketing, among others. But the linked structures of social networks do not reveal actual interactions among people. Scarcity of attention and the daily rythms of life and work makes people default to interacting with those few that matter and that reciprocate their attention. A study of social interactions within Twitter reveals that the driver of usage is a sparse and hidden network of connections underlying the “declared” set of friends and followers.

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.

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