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Recognizing and Stopping Rumors Patterns in Social Networks


  • Department of Computer Science, Faculty of Science, Menoufia University, Shebeen El Koom, Egypt


Objectives: In this study, a proposed Colored Petri Net Model (CPNM) is used for recognizing and stopping rumors in Social Networks (SN). Methods/Analysis: Detecting and blocking rumors represent an open security issue in social networks. In response to this issue, the proposed CPNM is experimentally simulated on dataset consists of 863-newsworthy tweets collected from the trending topic #CharlieHebdo in Twitter. The performance of CPNM is analyzed and evaluated using Precision, Recall, and Accuracy metrics. In addition, the CPNM is verified against the Reachability as a major behavior property in Petri Nets. Findings: The practical results disclosed a superiority of the proposed CPNM in detecting accurately rumors patterns compared with other approaches in the literature. In addition, verifying the Reachability using Reachability Graph proved that detecting and blocking rumors tweets are reachable states according to the firing life-cycle of tokens. Novelty/Improvement: Detecting rumors in social networks in more accuracy and low False Positive Rate (FPR) as well as blocking its propagation over the Social Network.


Colored Petri Nets (CPNs), Credibility Evaluation, Reachability, Rumors, Social Networks (SN)

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  • Kumar KP, Geethakumari G. Detecting Misinformation in Online Social Networks using Cognitive Psychology. Human-Centric Computing and Information Science Springer. 2014; 4(1):1–22. Crossref
  • Karlova NA, Fisher KE, Plz RT. A Social Diffusion model of Misinformation and disinformation for understanding human information behavior. Inform Res. 2013; 18(1):1–17.
  • Gupta A, Kumaraguru P. Credibility Ranking of Tweets During High Impact Events. Proceedings of the 1st Workshop on Privacy and Security in Online Social Media, ACM Lyon France: 2012. p. 2–6. Crossref
  • Liu B. Sentiment analysis and opinion mining. Synthesis lectures on human language technologies. 2012; 5(1):1– 167.
  • Torky M, Babars R, Ibrahim R, Hassanein AE, Schaefer G, Zhu SY. Credibility Investigation of Newsworthy Tweets Using a Visualising Petri Net Model. Proceedings of IEEE International Conference on Systems Man and Cybernetics, 2016. p. 003894–8. Crossref
  • Nivedah R, Sairam N. A Machine Learning based Classification for Social Media Messages. Indian Journal of Science and Technology. 2015; 8(16):1–4. Crossref
  • Cstillo C, Mendoza M, Poblete B. Information Credibility on Twitter. Proceedings of WWW 2011 international Conference on Information Credibility, 2011. p. 675–84. Crossref
  • Morris MR, Counts S, Roseway A, Hoff A, Schwarz J. Tweeting is Believing Understanding Microblog Credibility Perceptions. Proceedings of the CSCW 2012 Conference, ACM, Seattle Washington USA: 2012. p.441–50.
  • Abbasi MA, Liu H. Measuring user credibility in social media. Proceedings of International Conference on Social Computing Behavioral-Cultural Modeling and Prediction, Springer Berlin Heidelberg; 2013 Apr. p.441–8.Crossref
  • Nguyen DT, Nguyen NP, Thai MT. Sources of Misinformation in Online Social Networks Who to Suspect. Proceedings of Military Communications Conference IEEE, Florida USA: 2012 Oct. p. 1–6.Crossref
  • Qazvinian V, Rosengren E, Radev DR, Mei Q. Rumor has it Identifying misinformation in microblogs. Proceedings ofthe Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics; 2011. p. 589–1599.
  • Lappas T, Terzi E, Gunopulos D, Mannila H. Finding effectors in social networks. Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM, Washington: 2010 Jul. p. 1059–68.Crossref
  • Nguyen NP, Yan G, Thai MT. Analysis of misinformation containment in online social networks. Computer Networks Elsevier. 2013; 57(10):2133–46.Crossref
  • Budak C, Agrawal D, Abbadi A. Limiting the spread of misinformation in social networks. Proceedings of the 20th international conference on World wide web ACM, Hyderabad India: 2011. p. 665–74. Crossref
  • Jensen K. Coloured Petri nets basic concepts analysis methods and practical use. 2nd ed. Springer Science Business Media; 1997. Crossref
  • Simulator functions CPN Tools. Crossref 17. R project. Crossref


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