Word of Mouth Propagation in Online Social Networks

Online social networks (OSNs) are becoming an important propagation platform for Word of mouth (WOM). Therefore, it is of great significance to study the propagation of WOM in OSNs. A WOM propagation model named N-P-N is proposed in this paper, and some simulation experiments are carried out to investigate the mechanism of WOM propagation. From the sensitivity analysis of degree of initial information source node, it can be seen that the degree of initial information source node determines the scope and speed of the propagation of WOM in OSNs in some extent. Then the sensitivity analysis of number of initial information source nodes shows that the initial source nodes are crucial for controlling the propagation of negative information in OSNs. Moreover, from the user behavior respect, it is found that different user behavior in OSNs causes different propagation results, the more users who are willing to diffuse WOM, the more scope WOM can propagate and the faster the information diffuses. Findings in this paper are helpful for enterprises to form an effective WOM.

[1]  Damon Centola,et al.  The Spread of Behavior in an Online Social Network Experiment , 2010, Science.

[2]  P. Lazarsfeld,et al.  Personal Influence: The Part Played by People in the Flow of Mass Communications , 1956 .

[3]  Stefan M. Wild,et al.  Maximizing influence in a competitive social network: a follower's perspective , 2007, ICEC.

[4]  Pradeep Dubey,et al.  Competing for Customers in a Social Network: The Quasi-linear Case , 2006, WINE.

[5]  P. Lazarsfeld,et al.  6. Katz, E. Personal Influence: The Part Played by People in the Flow of Mass Communications , 1956 .

[6]  Divyakant Agrawal,et al.  Limiting the spread of misinformation in social networks , 2011, WWW.

[7]  Dunia López-Pintado,et al.  Diffusion in complex social networks , 2008, Games Econ. Behav..

[8]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[9]  J. Hertz,et al.  Mean field theory for nonequilibrium network reconstruction. , 2010, Physical review letters.

[10]  Brandon Van Der Heide,et al.  A social network as information: The effect of system generated reports of connectedness on credibility on Twitter , 2012, Comput. Hum. Behav..

[11]  P. Lazarsfeld,et al.  Personal Influence: The Part Played by People in the Flow of Mass Communications , 1956 .

[12]  H. Hethcote.  Qualitative analyses of communicable disease models , 1976 .

[13]  Marcel Salathé,et al.  Dynamics and Control of Diseases in Networks with Community Structure , 2010, PLoS Comput. Biol..

[14]  P. Kaye.  Infectious diseases of humans: Dynamics and control , 1993 .

[15]  Lucas Antiqueira,et al.  Analyzing and modeling real-world phenomena with complex networks: a survey of applications , 2007, 0711.3199.

[16]  Jinhong Xie,et al.  Online Social Interactions: A Natural Experiment on Word of Mouth versus Observational Learning , 2010 .

[17]  Shishir Bharathi,et al.  Competitive Influence Maximization in Social Networks , 2007, WINE.

[18]  Stefan Schmid,et al.  On the windfall of friendship: inoculation strategies on social networks , 2008, EC '08.

[19]  Allan Borodin,et al.  Threshold Models for Competitive Influence in Social Networks , 2010, WINE.

[20]  Sarah J. S. Wilner,et al.  Networked Narratives: Understanding Word-of-Mouth Marketing in Online Communities , 2009 .

[21]  Joseph A. Schumpeter,et al.  The theory of economics development , 1934 .

[22]  Luis A. Nunes Amaral,et al.  Complex Systems - A New Paradigm for the Integrative Study of Management, Physical, and Technological Systems , 2007, Manag. Sci..