Seeking provenance of information using social media

Social media propagates breaking news and disinformation alike fast and on an unsurpassed scale. Because of its democratizing nature, social media users can easily produce, receive, and propagate a piece of information without necessarily providing traceable information. Thus, there are no means for a user to verify the provenance (aka sources or originators) of information. The disinformation can cause tragic consequences to society and individuals. This work aims to take advantage of characteristics of social media to provide a solution to the problem of lacking traceable information. Such knowledge can provide additional context to received information such that a user can assess how much value, trust, and validity should be placed in it. In this paper, we are studying a novel research problem that facilitates the seeking of the provenance of information for a few known recipients (less than 1% of the total recipients) by recovering the paths it has taken from its originators. The proposed methodology exploits easily computable node centralities of a large social media network. The experimental results with Facebook and Twitter datasets show that the proposed mechanism is effective in correctly identifying the additional recipients and seeking the provenance of information.

[1]  Huan Liu,et al.  Community Detection and Mining in Social Media , 2010, Community Detection and Mining in Social Media.

[2]  Devavrat Shah,et al.  Rumors in a Network: Who's the Culprit? , 2009, IEEE Transactions on Information Theory.

[3]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[4]  Sudipto Guha,et al.  Approximation algorithms for directed Steiner problems , 1999, SODA '98.

[5]  Joel E. Cohen,et al.  Infectious Diseases of Humans: Dynamics and Control , 1992 .

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

[7]  Huan Liu,et al.  A tool for assisting provenance search in social media , 2013, CIKM.

[8]  Stanford,et al.  Learning to Discover Social Circles in Ego Networks , 2012 .

[9]  John Scott What is social network analysis , 2010 .

[10]  Dimitrios Gunopulos,et al.  Finding effectors in social networks , 2010, KDD.

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

[12]  James Frew,et al.  Lineage retrieval for scientific data processing: a survey , 2005, CSUR.

[13]  Sanjeev Khanna,et al.  Why and Where: A Characterization of Data Provenance , 2001, ICDT.

[14]  Ronald L. Rivest,et al.  Introduction to Algorithms, third edition , 2009 .

[15]  Yogesh L. Simmhan,et al.  A survey of data provenance in e-science , 2005, SGMD.

[16]  Jon M. Kleinberg,et al.  The structure of information pathways in a social communication network , 2008, KDD.

[17]  Christos Faloutsos,et al.  Spotting Culprits in Epidemics: How Many and Which Ones? , 2012, 2012 IEEE 12th International Conference on Data Mining.

[18]  Jure Leskovec,et al.  Learning to Discover Social Circles in Ego Networks , 2012, NIPS.

[19]  Huan Liu,et al.  Finding provenance data in social media , 2011 .

[20]  Huan Liu,et al.  Recovering information recipients in social media via provenance , 2013, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).

[21]  Huan Liu,et al.  A tool for collecting provenance data in social media , 2013, KDD.

[22]  Carole A. Goble,et al.  Mining Taverna's semantic web of provenance , 2008, Concurr. Comput. Pract. Exp..