DRIMUX: Dynamic Rumor Influence Minimization with User Experience in Social Networks

With the soaring development of large scale online social networks, online information sharing is becoming ubiquitous everyday. Various information is propagating through online social networks including both the positive and negative. In this paper, we focus on the negative information problems such as the online rumors. Rumor blocking is a serious problem in large-scale social networks. Malicious rumors could cause chaos in society and hence need to be blocked as soon as possible after being detected. In this paper, we propose a model of dynamic rumor influence minimization with user experience (DRIMUX). Our goal is to minimize the influence of the rumor (i.e., the number of users that have accepted and sent the rumor) by blocking a certain subset of nodes. A dynamic Ising propagation model considering both the global popularity and individual attraction of the rumor is presented based on a realistic scenario. In addition, different from existing problems of influence minimization, we take into account the constraint of user experience utility. Specifically, each node is assigned a tolerance time threshold. If the blocking time of each user exceeds that threshold, the utility of the network will decrease. Under this constraint, we then formulate the problem as a network inference problem with survival theory, and propose solutions based on maximum likelihood principle. Experiments are implemented based on large-scale real world networks and validate the effectiveness of our method.

[1]  Jong-Ryul Lee,et al.  A fast approximation for influence maximization in large social networks , 2014, WWW.

[2]  M. Newman,et al.  The structure of scientific collaboration networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[3]  T. D. Lee,et al.  Statistical Theory of Equations of State and Phase Transitions. II. Lattice Gas and Ising Model , 1952 .

[4]  Wei Chen,et al.  Efficient influence maximization in social networks , 2009, KDD.

[5]  Carlos Angel Iglesias,et al.  A Novel Agent-Based Rumor Spreading Model in Twitter , 2015, WWW.

[6]  F. Brauer,et al.  Mathematical Models in Population Biology and Epidemiology , 2001 .

[7]  Peter Rossmanith,et al.  Simulated Annealing , 2008, Taschenbuch der Algorithmen.

[8]  Kathryn A. Dowsland,et al.  Simulated Annealing , 1989, Encyclopedia of GIS.

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

[10]  Andrea Montanari,et al.  The spread of innovations in social networks , 2010, Proceedings of the National Academy of Sciences.

[11]  H. O. Lancaster,et al.  Chi-Square Distribution , 2005 .

[12]  Nathan Mantel,et al.  Chi-square tests with one degree of freedom , 1963 .

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

[14]  Shou-De Lin,et al.  Information propagation game: a tool to acquire humanplaying data for multiplayer influence maximization on social networks , 2012, KDD.

[15]  Weili Wu,et al.  Least Cost Rumor Blocking in Social Networks , 2013, 2013 IEEE 33rd International Conference on Distributed Computing Systems.

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

[17]  Masahiro Kimura,et al.  Blocking links to minimize contamination spread in a social network , 2009, TKDD.

[18]  Christos Faloutsos,et al.  Rise and fall patterns of information diffusion: model and implications , 2012, KDD.

[19]  Jure Leskovec,et al.  Patterns of temporal variation in online media , 2011, WSDM '11.

[20]  Allan Kuchinsky,et al.  Integrating user-perceived quality into Web server design , 2000, Comput. Networks.

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

[22]  F. E. Satterthwaite.  An approximate distribution of estimates of variance components. , 1946, Biometrics.

[23]  Satterthwaite Fe.  An approximate distribution of estimates of variance components. , 1946 .

[24]  S. Brush.  History of the Lenz-Ising Model , 1967 .

[25]  Allen Newell,et al.  The psychology of human-computer interaction , 1983 .

[26]  Michela Del Vicario,et al.  Viral Misinformation: The Role of Homophily and Polarization , 2014, WWW.

[27]  O. Aalen,et al.  Survival and Event History Analysis: A Process Point of View , 2008 .

[28]  S. Smirnov,et al.  Universality in the 2D Ising model and conformal invariance of fermionic observables , 2009, 0910.2045.

[29]  Wei Chen,et al.  Scalable influence maximization for independent cascade model in large-scale social networks , 2012, Data Mining and Knowledge Discovery.

[30]  Rajeev Motwani,et al.  The PageRank Citation Ranking : Bringing Order to the Web , 1999, WWW 1999.

[31]  Wei Chen,et al.  Scalable influence maximization for prevalent viral marketing in large-scale social networks , 2010, KDD.

[32]  Xiang Ao,et al.  Energy model for rumor propagation on social networks , 2014 .

[33]  Didier Sornette,et al.  Robust dynamic classes revealed by measuring the response function of a social system , 2008, Proceedings of the National Academy of Sciences.

[34]  Katsuhide Warashina,et al.  A rumor transmission model with various contact interactions. , 2008, Journal of theoretical biology.

[35]  Xiaoying Gan,et al.  Capacity of Wireless Networks With Social Characteristics , 2016, IEEE Transactions on Wireless Communications.

[36]  Sudula Dharani,et al.  Drimux Dynamic Rumor Influence Minimization with User Experience in Social Networks , 2018 .

[37]  Richard Sproat,et al.  Mining correlated bursty topic patterns from coordinated text streams , 2007, KDD '07.

[38]  Jure Leskovec,et al.  Meme-tracking and the dynamics of the news cycle , 2009, KDD.

[39]  B. Shulgin,et al.  Pulse vaccination strategy in the SIR epidemic model , 1998, Bulletin of mathematical biology.

[40]  Jon M. Kleinberg,et al.  Tracing information flow on a global scale using Internet chain-letter data , 2008, Proceedings of the National Academy of Sciences.

[41]  Krishna P. Gummadi,et al.  Measuring User Influence in Twitter: The Million Follower Fallacy , 2010, ICWSM.

[42]  Xin Rong,et al.  Diffusion of innovations revisited: from social network to innovation network , 2013, CIKM.

[43]  Andrew McCallum,et al.  Topic and Role Discovery in Social Networks , 2005, IJCAI.

[44]  Arunabha Sen,et al.  Influence propagation in adversarial setting: how to defeat competition with least amount of investment , 2012, CIKM.

[45]  Yasuhiro Takeuchi,et al.  Global stability of an SIR epidemic model with time delays , 1995, Journal of mathematical biology.

[46]  Wei Chen,et al.  Maximizing acceptance probability for active friending in online social networks , 2013, KDD.