Social Media Communication Model Research Bases on Sina-weibo

The popularity of microblog brings new characters to information diffusion in social networks. Facing new challenges of understanding information propagation in microblog, the framework of information producing and receiving was proposed. A general model named competing-window is also presented based on human behavior. The detailed composition of the model and its basal mathematical description are also given. In addition, a parameter called information lost as a supplement to measure dynamics of information diffusion. Meanwhile, the further application of our model to information processing and propagating was pointed out. All those work is based on the studies on human dynamics. Finally, to verify applicability, the model was applied to empirical data crawled from Sina-weibo. The interesting patterns extracted from empirical data indicate that microblog in deed is fundamentally characterized by human dynamics.

[1]  Danah Boyd,et al.  Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter , 2010, 2010 43rd Hawaii International Conference on System Sciences.

[2]  Hazem M. Hajj,et al.  A Framework for Emotion Mining from Text in Online Social Networks , 2010, 2010 IEEE International Conference on Data Mining Workshops.

[3]  L. Amaral,et al.  On Universality in Human Correspondence Activity , 2009, Science.

[4]  Jure Leskovec,et al.  Modeling Information Diffusion in Implicit Networks , 2010, 2010 IEEE International Conference on Data Mining.

[5]  Alexei Vazquez.  Impact of memory on human dynamics , 2007 .

[6]  A.R.M. Teutle,et al.  Twitter: Network properties analysis , 2010, 2010 20th International Conference on Electronics Communications and Computers (CONIELECOMP).

[7]  Bruno Gonçalves,et al.  Human dynamics revealed through Web analytics , 2008, Physical review. E, Statistical, nonlinear, and soft matter physics.

[8]  Albert-László Barabási,et al.  Modeling bursts and heavy tails in human dynamics , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[9]  Albert-László Barabási,et al.  The origin of bursts and heavy tails in human dynamics , 2005, Nature.

[10]  Tao Zhou,et al.  Modeling human dynamics with adaptive interest , 2007, 0711.0741.

[11]  Thilo Gross,et al.  Adaptive coevolutionary networks: a review , 2007, Journal of The Royal Society Interface.

[12]  G Caldarelli,et al.  Invasion percolation and critical transient in the Barabási model of human dynamics. , 2007, Physical review letters.

[13]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[14]  Adilson E. Motter,et al.  A Poissonian explanation for heavy tails in e-mail communication , 2008, Proceedings of the National Academy of Sciences.

[15]  Aristides Gionis,et al.  Learning and Predicting the Evolution of Social Networks , 2010, IEEE Intelligent Systems.

[16]  A. O. Walker.  British Fruit Growing , 1905, Nature.

[17]  KARL PEARSON,et al.  The Problem of the Random Walk , 1905, Nature.

[18]  Scott Counts,et al.  Predicting the Speed, Scale, and Range of Information Diffusion in Twitter , 2010, ICWSM.

[19]  Alex Hai Wang,et al.  Don't follow me: Spam detection in Twitter , 2010, 2010 International Conference on Security and Cryptography (SECRYPT).

[20]  Michael Gertz,et al.  Mining email social networks , 2006, MSR '06.

[21]  F. Haight.  Handbook of the Poisson Distribution , 1967 .

[22]  M. E. J. Newman,et al.  Power laws, Pareto distributions and Zipf's law , 2005 .

[23]  Long Wang,et al.  Empirical analysis of online social networks in the age of Web 2.0 , 2008 .

[24]  Peter Nijkamp,et al.  Accessibility of Cities in the Digital Economy , 2004, cond-mat/0412004.

[25]  Lu Liu,et al.  Information diffusion through online social networks , 2010, 2010 IEEE International Conference on Emergency Management and Management Sciences.