Post Sharing-Based Credibility Network for Social Network

Social networks are intensively and extensively used to exchange news and contents in real time. The lack of a global authority for assessing posts truthfulness however allows malicious to exhibit unfair behaviours; identifying methodologies to detect hoaxes and defamatory content automatically is therefore more and more required. Social networks as Facebook and Twitter provided specific solutions and general approaches were also developed; in this paper we present a general model that takes into account both post as well as users’ credibility, using a duplex network of acquaintances and credibility among users. First experiments show that it is possible to distinguish individuals who post non-truthful content through a combined analysis of both the news content and the reposts they get from their contacts.

[1]  Vincenza Carchiolo,et al.  Users' attachment in trust networks: reputation vs. effort , 2013, Int. J. Bio Inspired Comput..

[2]  Juan Martínez-Romo,et al.  Detecting malicious tweets in trending topics using a statistical analysis of language , 2013, Expert Syst. Appl..

[3]  Qiaozhu Mei,et al.  Enquiring Minds: Early Detection of Rumors in Social Media from Enquiry Posts , 2015, WWW.

[4]  Meeyoung Cha,et al.  Modeling Bursty Temporal Pattern of Rumors , 2014, ICWSM.

[5]  Vincenza Carchiolo,et al.  Trusting Evaluation by Social Reputation , 2008, IDC.

[6]  Gianluca Stringhini,et al.  COMPA: Detecting Compromised Accounts on Social Networks , 2013, NDSS.

[7]  Kyomin Jung,et al.  Prominent Features of Rumor Propagation in Online Social Media , 2013, 2013 IEEE 13th International Conference on Data Mining.

[8]  Vincenza Carchiolo,et al.  Trust assessment: a personalized, distributed, and secure approach , 2012, Concurr. Comput. Pract. Exp..

[9]  Jesús Gómez-Gardeñes,et al.  A mathematical model for networks with structures in the mesoscale , 2010, Int. J. Comput. Math..

[10]  Vincenza Carchiolo,et al.  Gain the Best Reputation in Trust Networks , 2011, IDC.

[11]  Wei Gao,et al.  Detect Rumors Using Time Series of Social Context Information on Microblogging Websites , 2015, CIKM.

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

[13]  Yongdong Zhang,et al.  News Credibility Evaluation on Microblog with a Hierarchical Propagation Model , 2014, 2014 IEEE International Conference on Data Mining.

[14]  Padraig Cunningham,et al.  Identifying Discriminating Network Motifs in YouTube Spam , 2012, ArXiv.