scholarly journals Viral Marketing for Product Cross-Sell through Social Networks

2012 ◽  
pp. 581-596 ◽  
Author(s):  
Ramasuri Narayanam ◽  
Amit A. Nanavati
Keyword(s):  
Social Networks ◽  
Viral Marketing

VIRAL MARKETING IN SOCIAL NETWORKS

2019 ◽  
Vol 5 (5) ◽  
Author(s):  
Elena Gerasikova ◽  
Milena Ischenko ◽  
Olga Saenkova ◽  
Nataliya Yasenkova
Keyword(s):  
Social Networks ◽  
Viral Marketing

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.

Modeling Rumors in Twitter

2016 ◽  
Vol 3 (4) ◽  
pp. 46-67 ◽  
Author(s):  
Rhythm Walia ◽  
M.P.S. Bhatia
Keyword(s):  
Social Networks ◽  
Social Media ◽  
Comparative Analysis ◽  
State Of The Art ◽  
Viral Marketing ◽  
Daily Lives ◽  
The Past ◽  
Depth Analysis ◽  
Analysis Of Results ◽  
Effective Channel

With the advent of web 2.0 and anonymous free Internet services available to almost everyone, social media has gained immense popularity in disseminating information. It has become an effective channel for advertising and viral marketing. People rely on social networks for news, communication and it has become an integral part of our daily lives. But due to the limited accountability of users, it is often misused for the spread of rumors. Such rumor diffusion hampers the credibility of social media and may spread social panic. Analyzing rumors in social media has gained immense attention from the researchers in the past decade. In this paper the authors provide a survey of work in rumor analysis, which will serve as a stepping-stone for new researchers. They organized the study of rumors into four categories and discussed state of the art papers in each with an in-depth analysis of results of different models used and a comparative analysis between approaches used by different authors.

A Proposal Model for Measuring the Impact of Viral Marketing Through Social Networks on Purchasing Decision

2018 ◽  
Vol 9 (3) ◽  
pp. 13-33 ◽  
Author(s):  
Hussein Moselhy Sayed Ahmed
Keyword(s):  
Social Networks ◽  
Knowledge Sharing ◽  
Social Networking Sites ◽  
Viral Marketing ◽  
Purchasing Decision ◽  
Market Shares ◽  
Online Word Of Mouth ◽  
Proposed Model ◽  
Social Network Influence ◽  
The Impact

Viral marketing has become a conduit for today's organizations and an important pillar for managing the organization and a source that enhances its competitiveness and creates new opportunities for organizations through which they are trying to achieve competitive advantages to obtain new market shares. So, this study provides insight into how social network influence on purchasing decision through viral marketing and knowledge sharing on social networking sites (SNSs). By using the sample from 650 Egyptian college students - who spend more time on SNSs, this study investigates the relationship among the use of SNSs, users' social relationships, online word-of-mouth, and knowledge sharing. Therefore, this paper is working on the study of the impact of viral marketing through social networks on consumer buying decisions, and working on the development of a proposed model to measure this effect.

scholarly journals Latency-Bounded Target Set Selection in Signed Networks

Algorithms ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 32 ◽  
Author(s):  
Miriam Di Ianni ◽  
Giovanna Varricchio
Keyword(s):  
Social Networks ◽  
Diffusion Process ◽  
Large Fraction ◽  
New Product ◽  
Negative Influence ◽  
Viral Marketing ◽  
Dynamic Processes ◽  
Diffusion Of Information ◽  
Target Set ◽  
Target Set Selection

It is well-documented that social networks play a considerable role in information spreading. The dynamic processes governing the diffusion of information have been studied in many fields, including epidemiology, sociology, economics, and computer science. A widely studied problem in the area of viral marketing is the target set selection: in order to market a new product, hoping it will be adopted by a large fraction of individuals in the network, which set of individuals should we “target” (for instance, by offering them free samples of the product)? In this paper, we introduce a diffusion model in which some of the neighbors of a node have a negative influence on that node, namely, they induce the node to reject the feature that is supposed to be spread. We study the target set selection problem within this model, first proving a strong inapproximability result holding also when the diffusion process is required to reach all the nodes in a couple of rounds. Then, we consider a set of restrictions under which the problem is approximable to some extent.

scholarly journals Influence Maximization in Social Network Considering Memory Effect and Social Reinforcement Effect

2019 ◽  
Vol 11 (4) ◽  
pp. 95
Author(s):  
Wang ◽  
Zhu ◽  
Liu ◽  
Wang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Memory Effect ◽  
Influence Maximization ◽  
Viral Marketing ◽  
Maximization Problem ◽  
Social Reinforcement ◽  
Reinforcement Effect ◽  
The Social ◽  
Influence Maximization Problem

Social networks have attracted a lot of attention as novel information or advertisement diffusion media for viral marketing. Influence maximization describes the problem of finding a small subset of seed nodes in a social network that could maximize the spread of influence. A lot of algorithms have been proposed to solve this problem. Recently, in order to achieve more realistic viral marketing scenarios, some constrained versions of influence maximization, which consider time constraints, budget constraints and so on, have been proposed. However, none of them considers the memory effect and the social reinforcement effect, which are ubiquitous properties of social networks. In this paper, we define a new constrained version of the influence maximization problem that captures the social reinforcement and memory effects. We first propose a novel propagation model to capture the dynamics of the memory and social reinforcement effects. Then, we modify two baseline algorithms and design a new algorithm to solve the problem under the model. Experiments show that our algorithm achieves the best performance with relatively low time complexity. We also demonstrate that the new version captures some important properties of viral marketing in social networks, such as such as social reinforcements, and could explain some phenomena that cannot be explained by existing influence maximization problem definitions.

Exploiting Viral Marketing for Location Promotion in Location-Based Social Networks

2016 ◽  
Vol 11 (2) ◽  
pp. 1-28 ◽  
Author(s):  
Wen-Yuan Zhu ◽  
Wen-Chih Peng ◽  
Ling-Jyh Chen ◽  
Kai Zheng ◽  
Xiaofang Zhou
Keyword(s):  
Social Networks ◽  
Viral Marketing ◽  
Location Based Social Networks

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 KONSEP DAN STRATEGI KOMUNIKASI PEMASARAN: PERUBAHAN PERILAKU KONSUMEN MENUJU ERA DISRUPSI

2020 ◽  
Vol 8 (1) ◽  
pp. 22
Author(s):  
Dea Farahdiba
Keyword(s):  
Social Networks ◽  
Consumer Behavior ◽  
Research Methodology ◽  
Human Life ◽  
Viral Marketing ◽  
The Other ◽  
Marketing Communication ◽  
Literature Study ◽  
Communication Tools ◽  
The Impact

Marketing has evolved from the concept of marketing 1.0 to 4.0 so that to learn what is needed and wanted by consumers, marketers must be able to have policies using appropriate communication so that various information about products can be transferred to consumers. For this reason, it is necessary to better understand communication, especially marketing communication in terms of shaping consumer behavior. The purpose of this article is to provide insight in the field of marketing communication. This research methodology is a literature study from several journals that discusses marketing communication. The existence of communication can make it easier for someone to interact between one individual with another individual. Without communication there is no human life process. That is, every human being needs communication to exchange ideas in order to realize what he wants. Three major marketing shifts, marked by product-driven marketing in the later 1.0 era towards customer-centered marketing in the 2.0 era. Human-centered marketing existed in the 3.0 era. On the other hand, marketing communication tools can be in the form of advertisements, sales, signage, shops, displays, packaging, free product samples, coupons, giveaway and more. As for changing consumer behavior that is increasingly developing, the impact of marketing 4.0 also results in viral marketing through social networks such as Facebook enabling continuous two-way interactivity from anywhere and at any time.

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