A simulation-based approach to analyze the information diffusion in Microblogging Online Social Network

In this paper we propose a stochastic multi-agent based approach to analyze the information diffusion in Microblogging Online Social Networks (OSNs). OSNs, like Twitter and Facebook, became extremely popular and are being used to target marketing campaigns. Key known issues on this targeting is to be able to predict human behavior like posting a message with regard to some topics, and to analyze the emergent behavior of such actions. We explore Barack Obama's Twitter network as an egocentric network to present our simulation-based approach and predictive behavior modeling. Through experimental analysis, we evaluated the impact of inactivating both Obama and the most engaged users, aiming at understanding the influence of those users that are the most likely to disseminate information over the network.

[1]  Marco Janssen,et al.  Simulating Market Dynamics: Interactions between Consumer Psychology and Social Networks , 2003, Artificial Life.

[2]  Masahiro Kimura,et al.  Prediction of Information Diffusion Probabilities for Independent Cascade Model , 2008, KES.

[3]  Sharon L. Milgram,et al.  The Small World Problem , 1967 .

[4]  Kathleen C. Schwartzman,et al.  DIFFUSION IN ORGANIZATIONS AND SOCIAL MOVEMENTS: From Hybrid Corn to Poison Pills , 2007 .

[5]  Xi Chen,et al.  Rumor Propagation in Online Social Networks Like Twitter -- A Simulation Study , 2011, 2011 Third International Conference on Multimedia Information Networking and Security.

[6]  George S. Fishman,et al.  Discrete-Event Simulation : Modeling, Programming, and Analysis , 2001 .

[7]  Aristides Gionis,et al.  Social Network Analysis and Mining for Business Applications , 2011, TIST.

[8]  F. Al-Shamali,et al.  Author Biographies. , 2015, Journal of social work in disability & rehabilitation.

[9]  Michael J. North,et al.  Tutorial on agent-based modelling and simulation , 2005, Proceedings of the Winter Simulation Conference, 2005..

[10]  Seungyeop Han,et al.  Analysis of topological characteristics of huge online social networking services , 2007, WWW '07.

[11]  Ramanathan V. Guha,et al.  Information diffusion through blogspace , 2004, WWW '04.

[12]  Jure Leskovec,et al.  Planetary-scale views on a large instant-messaging network , 2008, WWW.

[13]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[14]  Laks V. S. Lakshmanan,et al.  Learning influence probabilities in social networks , 2010, WSDM '10.

[15]  Jon Doyle,et al.  Leveraging multiple mechanisms for information propagation , 2011, AAMAS'11.

[16]  Hosung Park,et al.  What is Twitter, a social network or a news media? , 2010, WWW '10.

[17]  Pushmeet Kohli,et al.  Personality and patterns of Facebook usage , 2012, WebSci '12.

[18]  Jure Leskovec,et al.  The dynamics of viral marketing , 2005, EC '06.

[19]  George S. Fishman,et al.  Discrete-event simulation , 2001 .

[20]  Charles M. Macal,et al.  Tutorial on agent-based modelling and simulation , 2005, Proceedings of the Winter Simulation Conference, 2005..

[21]  R. Holley,et al.  Ergodic Theorems for Weakly Interacting Infinite Systems and the Voter Model , 1975 .

[22]  Krishna P. Gummadi,et al.  A measurement-driven analysis of information propagation in the flickr social network , 2009, WWW '09.

[23]  Hawoong Jeong,et al.  Statistical properties of sampled networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[24]  NICHOLAS R. JENNINGS,et al.  An agent-based approach for building complex software systems , 2001, CACM.

[25]  Marco Janssen,et al.  Diffusion dynamics in small-world networks with heterogeneous consumers , 2007, Comput. Math. Organ. Theory.