Discover the Misinformation Broadcasting in On-Line Social Networks

In recent years, more and more people join social networks to share information with others. At the same time, the information sharing/spreading becomes far more frequent and convenient due to the wide usage of mobile devices. As a result, the messages created are very arbitrary, which may contain a lot of misinformation. Proper actions must be taken to avoid the spreading of misinformation or rumors before it causes serious damages. Therefore, any misinformation should be discovered in time when it does not spread to a large group of people. All previous works studied either how the information is spread in the social network or how to inhibit the further pervasion of an observed misinformation. However, no works considered how to discover the broadcasting of misinformation in time. A possible solution is to set observers across the network to discover the suspects of misinformation. In this paper, we design a novel mechanism to select a set of observers in a social network with the minimum cost, where these observers guarantee any misinformation can be discovered with a high probability before it reaches a bounded number of users. Extensive experiment on real data sets verifies the effectiveness of our solution.

[1]  Alexander Grey,et al.  The Mathematical Theory of Infectious Diseases and Its Applications , 1977 .

[2]  Norman T. J. Bailey,et al.  The Mathematical Theory of Infectious Diseases , 1975 .

[3]  Dragomir R. Radev,et al.  Rumor has it: Identifying Misinformation in Microblogs , 2011, EMNLP.

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

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

[6]  Jacob Goldenberg,et al.  Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth , 2001 .

[7]  Yifei Yuan,et al.  Scalable Influence Maximization in Social Networks under the Linear Threshold Model , 2010, 2010 IEEE International Conference on Data Mining.

[8]  R. Durrett Lecture notes on particle systems and percolation , 1988 .

[9]  Frank Diederich,et al.  Mathematical Epidemiology Of Infectious Diseases Model Building Analysis And Interpretation , 2016 .

[10]  Juan-Zi Li,et al.  Expert Finding in a Social Network , 2007, DASFAA.

[11]  Brian D. Noble,et al.  Modeling epidemic spreading in mobile environments , 2005, WiSe '05.

[12]  O. Diekmann Mathematical Epidemiology of Infectious Diseases , 1996 .

[13]  Matthew Richardson,et al.  Mining the network value of customers , 2001, KDD '01.

[14]  Sameep Mehta,et al.  A study of rumor control strategies on social networks , 2010, CIKM.

[15]  Jacob Ratkiewicz,et al.  Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams , 2010, ArXiv.

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

[17]  N. Bailey,et al.  The mathematical theory of infectious diseases and its applications. 2nd edition. , 1975 .

[18]  N. Ling The Mathematical Theory of Infectious Diseases and its applications , 1978 .

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

[20]  Jeffrey O. Kephart,et al.  Measuring and modeling computer virus prevalence , 1993, Proceedings 1993 IEEE Computer Society Symposium on Research in Security and Privacy.

[21]  Jacob Ratkiewicz,et al.  Truthy: mapping the spread of astroturf in microblog streams , 2010, WWW.

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

[23]  Brendan J. Frey,et al.  Mixture Modeling by Affinity Propagation , 2005, NIPS.

[24]  Jimeng Sun,et al.  Social influence analysis in large-scale networks , 2009, KDD.

[25]  Mark S. Granovetter Threshold Models of Collective Behavior , 1978, American Journal of Sociology.

[26]  Juan-Zi Li,et al.  A Mixture Model for Expert Finding , 2008, PAKDD.

[27]  Alfred V. Aho,et al.  The Design and Analysis of Computer Algorithms , 1974 .