A new viral marketing strategy with the competition in the large-scale online social networks

2016 ◽  
Author(s):  
Canh V. Pham ◽  
Dung K. Ha ◽  
Dung Q. Ngo ◽  
Quang C. Vu ◽  
Huan X. Hoang
Keyword(s):  
Social Networks ◽  
Marketing Strategy ◽  
Online Social Networks ◽  
Large Scale ◽  
Viral Marketing

scholarly journals Local structure can identify and quantify influential global spreaders in large scale social networks

2018 ◽  
Vol 115 (29) ◽  
pp. 7468-7472 ◽  
Author(s):  
Yanqing Hu ◽  
Shenggong Ji ◽  
Yuliang Jin ◽  
Ling Feng ◽  
H. Eugene Stanley ◽  
...  
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Large Scale ◽  
Characteristic Number ◽  
Percolation Cluster ◽  
Network Size ◽  
Viral Marketing ◽  
Nucleation Behavior ◽  
Practical Applications ◽  
Entire Network

Measuring and optimizing the influence of nodes in big-data online social networks are important for many practical applications, such as the viral marketing and the adoption of new products. As the viral spreading on a social network is a global process, it is commonly believed that measuring the influence of nodes inevitably requires the knowledge of the entire network. Using percolation theory, we show that the spreading process displays a nucleation behavior: Once a piece of information spreads from the seeds to more than a small characteristic number of nodes, it reaches a point of no return and will quickly reach the percolation cluster, regardless of the entire network structure; otherwise the spreading will be contained locally. Thus, we find that, without the knowledge of the entire network, any node’s global influence can be accurately measured using this characteristic number, which is independent of the network size. This motivates an efficient algorithm with constant time complexity on the long-standing problem of best seed spreaders selection, with performance remarkably close to the true optimum.

scholarly journals Hashkat: large-scale simulations of online social networks

2017 ◽  
Vol 7 (1) ◽  
Author(s):  
Kevin Ryczko ◽  
Adam Domurad ◽  
Nicholas Buhagiar ◽  
Isaac Tamblyn
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Large Scale ◽  
Large Scale Simulations

A Review of Machine Learning and Data Mining Approaches for Business Applications in Social Networks

2013 ◽  
Vol 9 (1) ◽  
pp. 36-53
Author(s):  
Evis Trandafili ◽  
Marenglen Biba
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Social Networks ◽  
Large Scale ◽  
Viral Marketing ◽  
Social Network Mining ◽  
Business Applications ◽  
Mining Community ◽  
Recent Developments ◽  
Analysis Of Social Networks

Social networks have an outstanding marketing value and developing data mining methods for viral marketing is a hot topic in the research community. However, most social networks remain impossible to be fully analyzed and understood due to prohibiting sizes and the incapability of traditional machine learning and data mining approaches to deal with the new dimension in the learning process related to the large-scale environment where the data are produced. On one hand, the birth and evolution of such networks has posed outstanding challenges for the learning and mining community, and on the other has opened the possibility for very powerful business applications. However, little understanding exists regarding these business applications and the potential of social network mining to boost marketing. This paper presents a review of the most important state-of-the-art approaches in the machine learning and data mining community regarding analysis of social networks and their business applications. The authors review the problems related to social networks and describe the recent developments in the area discussing important achievements in the analysis of social networks and outlining future work. The focus of the review in not only on the technical aspects of the learning and mining approaches applied to social networks but also on the business potentials of such methods.

Prediction of Social Influence for Provenance of Misinformation in Online Social Network Using Big Data Approach

2020 ◽  
Author(s):  
Kumaran P ◽  
Rajeswari Sridhar
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Law Enforcement ◽  
Online Social Networks ◽  
Large Scale ◽  
Online Social Network ◽  
Root Cause ◽  
Minimum Number ◽  
Targeted Marketing ◽  
Implicit And Explicit

Abstract Online social networks (OSNs) is a platform that plays an essential role in identifying misinformation like false rumors, insults, pranks, hoaxes, spear phishing and computational propaganda in a better way. Detection of misinformation finds its applications in areas such as law enforcement to pinpoint culprits who spread rumors to harm the society, targeted marketing in e-commerce to identify the user who originates dissatisfaction messages about products or services that harm an organizations reputation. The process of identifying and detecting misinformation is very crucial in complex social networks. As misinformation in social network is identified by designing and placing the monitors, computing the minimum number of monitors for detecting misinformation is a very trivial work in the complex social network. The proposed approach determines the top suspected sources of misinformation using a tweet polarity-based ranking system in tandem with sarcasm detection (both implicit and explicit sarcasm) with optimization approaches on large-scale incomplete network. The algorithm subsequently uses this determined feature to place the minimum set of monitors in the network for detecting misinformation. The proposed work focuses on the timely detection of misinformation by limiting the distance between the suspected sources and the monitors. The proposed work also determines the root cause of misinformation (provenance) by using a combination of network-based and content-based approaches. The proposed work is compared with the state-of-art work and has observed that the proposed algorithm produces better results than existing methods.

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.

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