Relative Superiority of Key Centrality Measures for Identifying Influencers on Social Media

2014 ◽  
Vol 10 (4) ◽  
pp. 1-23 ◽  
Author(s):  
Yifeng Zhang ◽  
Xiaoqing Li
Keyword(s):  
Social Media ◽  
Information Diffusion ◽  
Viral Marketing ◽  
Centrality Measures ◽  
Network Structures ◽  
Agent Based ◽  
Marketing Models ◽  
Practical Implications

Marketers have been increasingly turning to social media for marketing campaigns, including viral marketing. A key step in viral marketing is to identify influencers in order to maximize the reach of a marketing message. Existing research shows that centrality measures, such as degree and betweenness, are effective methods for influencer identification. However, viral marketing models used in different studies vary greatly, making it difficult to compare findings across the studies. In this paper, the authors built an agent-based framework of viral marketing that supports different experiment settings, such as different network structures and information diffusion modes, and used it to study relative superiority of various centrality measures. The results show that relative superiority of the measures are affected by some factors, but not as much by others. Practical implications of the results are discussed.

scholarly journals The Dynamic Evolution Mechanism of Heterogeneous OWOM—An Improved Viral Marketing Model

Information ◽  
2020 ◽  
Vol 11 (3) ◽  
pp. 140
Author(s):  
Mengjie Liao ◽  
Lin Qi ◽  
Jian Zhang
Keyword(s):  
Social Media ◽  
Negative Impact ◽  
Viral Marketing ◽  
Dynamic Visualization ◽  
Agent Based ◽  
Marketing Model ◽  
Online Word Of Mouth ◽  
The Social ◽  
Huge Impact ◽  
Multi Agent

The negative impact of brand negative online word-of-mouth (OWOM) on social images in social media is far greater than the promotion of positive OWOM. Thus, how to optimize brand image by improving the positive OWOM effect and slowing the negative OWOM communication has turned into an urgent problem for brand enterprises. On this basis, we analyze the evolution process of the OWOM communication group of the social media brand network based on the SOR (stimulus-organism-response) theory of psychology. Through constructing the heterogeneous brand OWOM communication dynamic model and conducting the multi-agent-based simulation experiment, the dynamic visualization of brand OWOM communication effect combined the thinking model of viral marketing is realized. Experiments show that the ability of brand communicators to persuade has a direct impact on the persistence and breadth of brand communication. When the acceptance of the consumer market is high, the negative OWOM of the brand has a relatively huge impact on consumers.

scholarly journals Model-based learning of information diffusion in social media networks

2019 ◽  
Vol 4 (1) ◽  
Author(s):  
Zhecheng Qiang ◽  
Eduardo L. Pasiliao ◽  
Qipeng P. Zheng
Keyword(s):  
Social Media ◽  
Information Diffusion ◽  
Large Scale ◽  
Viral Marketing ◽  
Propagation Model ◽  
Mixed Integer ◽  
Prediction Errors ◽  
Learning Models ◽  
Approximate Methods ◽  
Social Media Networks

AbstractSocial networks have become widely used platforms for their users to share information. Learning the information diffusion process is essential for successful applications of viral marketing and cyber security in social media networks. This paper proposes two learning models that are aimed at learning person-to-person influence in information diffusion from historical cascades based on the threshold propagation model. The first model is based on the linear threshold propagation model. In addition, by considering multi-step information propagation in one time period, this paper proposes a learning model for multi-step diffusion influence between pairs of users based on the idea of random walk. Mixed integer programs (MIP) have been used to learn these models by minimizing the prediction errors, where decision variables are estimations of the diffusion influence between pairs of users. For large-scale networks, this paper develops approximate methods for those learning models by using artificial neural networks to learn the pairwise influence. Extensive computational experiments using both synthetic data and real data have been conducted to demonstrate the effectiveness of the proposed models and methods.

Jumping over the network threshold of information diffusion: testing the threshold hypothesis of social influence

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Cheng-Jun Wang ◽  
Jonathan J.H. Zhu
Keyword(s):  
Social Influence ◽  
Information Diffusion ◽  
Viral Marketing ◽  
Threshold Models ◽  
Large Network ◽  
Content Type ◽  
Innovative Strategies ◽  
And Diffusion ◽  
Practical Implications ◽  
Limited Diffusion

PurposeSocial influence plays a crucial role in determining the size of information diffusion. Drawing on threshold models, we reformulate the nonlinear threshold hypothesis of social influence.Design/methodology/approachWe test the threshold hypothesis of social influence with a large dataset of information diffusion on social media.FindingsThere exists a bell-shaped relationship between social influence and diffusion size. However, the large network threshold, limited diffusion depth and intense bursts become the bottlenecks that constrain the diffusion size.Practical implicationsThe practice of viral marketing needs innovative strategies to increase information novelty and reduce the excessive network threshold.Originality/valueIn all, this research extends threshold models of social influence and underlines the nonlinear nature of social influence in information diffusion.

Techniques of Viral Marketing

2019 ◽  
Vol 118 (6) ◽  
pp. 97-99
Author(s):  
Arockia Jeyasheela A ◽  
Dr.S. Chandramohan
Keyword(s):  
Social Media ◽  
Real Time ◽  
Word Of Mouth ◽  
Viral Marketing ◽  
Consumer Perception ◽  
Key Word ◽  
The Right ◽  
A Company

This study is discussed about the viral marketing. It is a one of the key success of marketing. This paper gave the techniques of viral marketing. It can be delivered word of mouth. It can be created by both the representatives of a company and consumer (individuals or communities). The right viral message with go to right consumer to the right time. Viral marketing is easy to attract the consumer. It is most important advertising to consumer. It involves consumer perception, organization contribution, blogs, SMO (Social Media Optimize), SEO (Social Engine Optimize). Principles of viral marketing are social profile gathering, Proximity Market, Real time Key word density.

scholarly journals Diffusion and persistence of false rumors in social media networks: implications of searchability on rumor self-correction on Twitter

2021 ◽  
Author(s):  
Kathrin Eismann
Keyword(s):  
Social Media ◽  
Agent Based ◽  
Social Media Networks ◽  
Agent Based Simulations

AbstractSocial media networks (SMN) such as Facebook and Twitter are infamous for facilitating the spread of potentially false rumors. Although it has been argued that SMN enable their users to identify and challenge false rumors through collective efforts to make sense of unverified information—a process typically referred to as self-correction—evidence suggests that users frequently fail to distinguish among rumors before they have been resolved. How users evaluate the veracity of a rumor can depend on the appraisals of others who participate in a conversation. Affordances such as the searchability of SMN, which enables users to learn about a rumor through dedicated search and query features rather than relying on interactions with their relational connections, might therefore affect the veracity judgments at which they arrive. This paper uses agent-based simulations to illustrate that searchability can hinder actors seeking to evaluate the trustworthiness of a rumor’s source and hence impede self-correction. The findings indicate that exchanges between related users can increase the likelihood that trustworthy agents transmit rumor messages, which can promote the propagation of useful information and corrective posts.

scholarly journals A Survey of Information Cascade Analysis

2021 ◽  
Vol 54 (2) ◽  
pp. 1-36
Author(s):  
Fan Zhou ◽  
Xovee Xu ◽  
Goce Trajcevski ◽  
Kunpeng Zhang
Keyword(s):  
Information Diffusion ◽  
Research Work ◽  
Viral Marketing ◽  
Graph Representation ◽  
Digital Information ◽  
Information Cascades ◽  
Challenges And Opportunities ◽  
Scientific Papers ◽  
Popularity Prediction ◽  
Types Of Information

The deluge of digital information in our daily life—from user-generated content, such as microblogs and scientific papers, to online business, such as viral marketing and advertising—offers unprecedented opportunities to explore and exploit the trajectories and structures of the evolution of information cascades. Abundant research efforts, both academic and industrial, have aimed to reach a better understanding of the mechanisms driving the spread of information and quantifying the outcome of information diffusion. This article presents a comprehensive review and categorization of information popularity prediction methods, from feature engineering and stochastic processes , through graph representation , to deep learning-based approaches . Specifically, we first formally define different types of information cascades and summarize the perspectives of existing studies. We then present a taxonomy that categorizes existing works into the aforementioned three main groups as well as the main subclasses in each group, and we systematically review cutting-edge research work. Finally, we summarize the pros and cons of existing research efforts and outline the open challenges and opportunities in this field.

scholarly journals Examining Perceived and Projected Destination Image: A Social Media Content Analysis

2021 ◽  
Vol 13 (6) ◽  
pp. 3354
Author(s):  
Wei Sun ◽  
Shoulian Tang ◽  
Fang Liu
Keyword(s):  
Social Media ◽  
Content Analysis ◽  
Destination Image ◽  
Media Content ◽  
Media Content Analysis ◽  
Poor Understanding ◽  
Practical Implications

Destination image has been extensively studied in tourism and marketing, but the questions surrounding the discrepancy between the projected (perceptions from the National Tourism Organizations) and perceived destination image (perceptions from tourists) as well as how the discrepancy may influence sustainable experience remain unclear. Poor understanding of the discrepancy may cause tourist confusion and misuse of resources. The aim of this study is to empirically investigate if the perceived (by tourists) and projected (by NTOs) destination image are significantly different in both cognitive and affective aspects. Through a comprehensive social media content analysis of the NTO-generated and tourist-generated-contents (TGC), the current study identifies numerous gaps between the projected and perceived destination image, which offers some important theoretical and practical implications on destination management and marketing.

Fear of Missing Out as motivation to process information: How differences in Instagram use affect attitude formation online

2021 ◽  
pp. 146144482110118
Author(s):  
Dominik Neumann ◽  
Patricia T Huddleston ◽  
Bridget K Behe
Keyword(s):  
Social Media ◽  
Cognitive Processing ◽  
Online Survey ◽  
Regression Analyses ◽  
Fear Of Missing Out ◽  
Advertising Strategy ◽  
Social Media Platforms ◽  
Advertising Content ◽  
Practical Implications ◽  
Marketing Messages

Marketing on social media has become ubiquitous. Consequently, social media platforms are increasing the level of advertising content that users may later encounter when navigating online shopping websites. It is unclear how this amplification of exposure to marketing messages through social media affects consumers’ attitudes to products online. Furthermore, the roles of social media participation and proneness to experience Fear of Missing Out on product attitude remain largely unexplored. In this research ( N = 1002), we employed an online survey of US Instagram users. These data were submitted to three-way moderation regression analyses with attitude toward the product as the dependent variable. Consumers who are more active on social media and had high (vs low) Fear of Missing Out expressed more favorable attitudes toward online products after being exposed to Instagram content (vs not exposed). The theoretical and practical implications for cognitive processing research and advertising strategy and study limitations are discussed.

The Value of Brand Fans during a Crisis: Exploring the Roles of Response Strategy, Source, and Brand Identification

2021 ◽  
pp. 232948842199969
Author(s):  
Hayoung Sally Lim ◽  
Natalie Brown-Devlin
Keyword(s):  
Social Media ◽  
Experimental Design ◽  
Crisis Communication ◽  
Word Of Mouth ◽  
Crisis Response ◽  
Response Strategy ◽  
Brand Identification ◽  
Communication Source ◽  
Practical Implications ◽  
Selection Of

Using a two (crisis response strategy: diminish vs. rebuild) × three (source: brand organization vs. brand executive vs. brand fan) experimental design, this study examines how brand fans (i.e., consumers who identify with a brand) can be prompted to protect a brand’s reputation during crises and how the selection of a crisis spokesperson can influence consumers’ evaluations of the crisis communication. Being buffers for their preferred brands, brand fans are more likely to accept their brand’s crisis response and engage in positive electronic word-of-mouth on social media. Brand fans are more likely to evaluate other brand fan’s social media accounts as a credible crisis communication source, whereas those who are not brand fans are more likely to evaluate brand and/or brand executives as credible. Findings provide theoretical applications in paracrisis literature pertaining to social media but also practical implications for brand managers to strategically utilize brand fans in crisis communication.

scholarly journals Agent Based Model of Anti-Vaccination Movements: Simulations and Comparison with Empirical Data

Vaccines ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 809
Author(s):  
Pawel Sobkowicz ◽  
Antoni Sobkowicz
Keyword(s):  
Social Media ◽  
Critical Parameters ◽  
Rational Approach ◽  
The Internet ◽  
High Temporal Resolution ◽  
Nonlinear Feedback ◽  
Agent Based Model ◽  
Linear Processes ◽  
Agent Based ◽  
The Social

Background: A realistic description of the social processes leading to the increasing reluctance to various forms of vaccination is a very challenging task. This is due to the complexity of the psychological and social mechanisms determining the positioning of individuals and groups against vaccination and associated activities. Understanding the role played by social media and the Internet in the current spread of the anti-vaccination (AV) movement is of crucial importance. Methods: We present novel, long-term Big Data analyses of Internet activity connected with the AV movement for such different societies as the US and Poland. The datasets we analyzed cover multiyear periods preceding the COVID-19 pandemic, documenting the behavior of vaccine related Internet activity with high temporal resolution. To understand the empirical observations, in particular the mechanism driving the peaks of AV activity, we propose an Agent Based Model (ABM) of the AV movement. The model includes the interplay between multiple driving factors: contacts with medical practitioners and public vaccination campaigns, interpersonal communication, and the influence of the infosphere (social networks, WEB pages, user comments, etc.). The model takes into account the difference between the rational approach of the pro-vaccination information providers and the largely emotional appeal of anti-vaccination propaganda. Results: The datasets studied show the presence of short-lived, high intensity activity peaks, much higher than the low activity background. The peaks are seemingly random in size and time separation. Such behavior strongly suggests a nonlinear nature for the social interactions driving the AV movement instead of the slow, gradual growth typical of linear processes. The ABM simulations reproduce the observed temporal behavior of the AV interest very closely. For a range of parameters, the simulations result in a relatively small fraction of people refusing vaccination, but a slight change in critical parameters (such as willingness to post anti-vaccination information) may lead to a catastrophic breakdown of vaccination support in the model society, due to nonlinear feedback effects. The model allows the effectiveness of strategies combating the anti-vaccination movement to be studied. An increase in intensity of standard pro-vaccination communications by government agencies and medical personnel is found to have little effect. On the other hand, focused campaigns using the Internet and social media and copying the highly emotional and narrative-focused format used by the anti-vaccination activists can diminish the AV influence. Similar effects result from censoring and taking down anti-vaccination communications by social media platforms. The benefit of such tactics might, however, be offset by their social cost, for example, the increased polarization and potential to exploit it for political goals, or increased ‘persecution’ and ‘martyrdom’ tropes.

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