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2D-SocialNetworks:AWay to Virally Distribute Popular Information Avoiding Spam

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 570))

Abstract

In Online Social Networks, in order to virally distribute some topics and, at the same time, protecting users from undesired messages, we propose to diffuse viral campaigns only on a second dimension of the social network. In the proposed approach, software agents assist the user by selecting the most appropriate campaigns for their owners. A users-to-campaigns matching algorithm, called Viral Filtered Diffusion, allows the agents to dynamically manage the evolution of the viral activity. Preliminary experiments clearly show the advantages in assigning to the users only campaigns compatible with their orientations.

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References

  1. Ardon, S., et al.: Spatio-temporal and events based analysis of topic popularity in twitter. In: CIKM, pp. 219–228. ACM (2013)

    Google Scholar 

  2. Barbieri, N., Bonchi, F., Manco, G.: Cascade-based community detection. In: Proceedings of the Sixth ACM Int. Conf. on Web Search and Data Mining, pp. 33–42. ACM (2013)

    Google Scholar 

  3. Belák, V., Lam, S., Hayes, C.: Towards maximising cross-community information diffusion. In: Advances in Social Networks Analysis and Mining, 2012, pp. 171–178. IEEE (2012)

    Google Scholar 

  4. Benevenuto, F., Magno, G., Rodrigues, T., Almeida, V.: Detecting spammers on twitter. In: Collaboration, Electronic Messaging, Anti-Abuse and Spam Conf., vol. 6, p. 12 (2010)

    Google Scholar 

  5. Buccafurri, F., Palopoli, L., Rosaci, D., Sarné, G.M.L.: Modeling cooperation in multi-agent communities. Cognitive Systems Research 5(3), 171–190 (2004)

    Article  Google Scholar 

  6. Chen, W., Wang, Y., Yang, S.: Efficient influence maximization in social networks. In: Proc. of the 15th Int. Conf. on Knowledge Discovery and Data Mining, pp. 199–208. ACM (2009)

    Google Scholar 

  7. De Meo, P., Ferrara, E., Abel, F., Aroyo, L., Houben, G.J.: Analyzing user behavior across social sharing environments. ACM Trans. on Intelligent Systems and Technology (TIST) 5(1), 14 (2013)

    Google Scholar 

  8. De Meo, P., Nocera, A., Rosaci, D., Ursino, D.: Recommendation of reliable users, social networks and high-quality resources in a social internetworking system. Ai Communications 24(1), 31–50 (2011)

    MATH  MathSciNet  Google Scholar 

  9. Gao, H., Hu, J., Wilson, C., Li, Z., Chen, Y., Zhao, B.Y.: Detecting and characterizing social spam campaigns. In: Proc. 10th Conf. on Internet Measurement, pp. 35–47. ACM (2010)

    Google Scholar 

  10. Ghosh, S., Viswanath, B., Kooti, F., Sharma, N.K., Korlam, G., Benevenuto, F., Ganguly, N., Gummadi, K.P.: Understanding and combating link farming in the twitter social network. In: Proc. of the 21st Int. Conf. on World Wide Web, pp. 61–70. ACM (2012)

    Google Scholar 

  11. Goyal, A., et al.: On minimizing budget and time in influence propagation over social networks. Social Network Analysis and Mining 3(2), 179–192 (2013)

    Article  Google Scholar 

  12. Heymann, P., Koutrika, G., Garcia-Molina, H.R.: Fighting spam on social web sites: A survey of approaches and future challenges. IEEE Internet Computing 11(6), 36–45 (2007)

    Article  Google Scholar 

  13. Kempe, D., Kleinberg, J., Tardos, É.: Maximizing spread of influence in a social network. In: Proc. 9th Int. Conf. on Knowledge Discovery and Data Mining, pp. 137–146. ACM (2003)

    Google Scholar 

  14. Kempe, D., Kleinberg, J.M., Tardos, É.: Influential nodes in a diffusion model for social networks. In: Caires, L., Italiano, G.F., Monteiro, L., Palamidessi, C., Yung, M. (eds.) ICALP 2005. LNCS, vol. 3580, pp. 1127–1138. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  15. Messina, F., Pappalardo, G., Rosaci, D., Santoro, C., Sarné, G.M.L.: HySoN: A distributed agent-based protocol for group formation in online social networks. In: Klusch, M., Thimm, M., Paprzycki, M. (eds.) MATES 2013. LNCS, vol. 8076, pp. 320–333. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  16. Messina, F., Pappalardo, G., Rosaci, D., Santoro, C., Sarné, G.M.L.: A Trust-Based Approach for a Competitive Cloud/Grid Computing Scenario. In: Fortino, G., Badica, C., Malgeri, M., Unland, R. (eds.) IDC 2012. SCI, vol. 446, pp. 129–138. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  17. Messina, F., Pappalardo, G., Rosaci, D., Santoro, C., Sarné, G.M.L.: A Distributed Agent-Based Approach for Supporting Group Formation in P2P e-Learning. In: Baldoni, M., Baroglio, C., Boella, G., Micalizio, R. (eds.) AI*IA 2013. LNCS, vol. 8249, pp. 312–323. Springer, Heidelberg (2013)

    Google Scholar 

  18. Messina, F., Pappalardo, G., Santoro, C.: Complexsim: An smp-aware complex network simulation framework. In: 2012 Sixth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), pp. 861–866. IEEE (2012), doi:10.1109/CISIS.2012.102

    Google Scholar 

  19. Messina, F., Pappalardo, G., Santoro, C.: Exploiting gpus to simulate complex systems. In: 2013 Seventh International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), pp. 535–540. IEEE (2013), doi:10.1109/CISIS.2013.97

    Google Scholar 

  20. Messina, F., Pappalardo, G., Santoro, C.: Complexsim: a flexible simulation platform for complex systems. International Journal of Simulation and Process Modelling 8(4), 202–211 (2013), doi:10.1504/IJSPM.2013.059417

    Article  Google Scholar 

  21. Nascimento, V., et al.: Exploring emergent social networks to improve agent mediated e-commerce. In: 10th Int. Conf. on e-Business Engineering, pp. 50–55. IEEE (2013)

    Google Scholar 

  22. Oscar, P., Roychowdbury, V.P.: Leveraging social networks to fight spam. IEEE Computer 38(4), 61–68 (2005)

    Article  Google Scholar 

  23. Rajyalakshmi, S., Bagchi, A., Das, S., Tripathy, R.M.: Topic diffusion and emergence of virality in social networks. CoRR, abs/1202.2215 (2012)

    Google Scholar 

  24. Rosaci, D., Sarné, G.M.L.: Matching Users with Groups in Social Networks. In: Zavoral, F., Jung, J.J., Badica, C. (eds.) IDC 2013. SCI, vol. 511, pp. 45–54. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  25. Rosaci, D., Sarnè, G.M.L.: Multi-agent technology and ontologies to support personalization in B2C e-Commerce. Electronic Commerce Research and Applications 13(1), 13–23 (2014)

    Article  Google Scholar 

  26. Rosaci, D., Sarnè, G.M.L., Garruzzo, S.: Integrating trust measures in multiagent systems. International Journal of Intelligent Systems 27(1), 1–15 (2012)

    Article  Google Scholar 

  27. Ruhela, A., et al.: Towards the use of online social networks for efficient internet content distribution. In: ANTS, pp. 1–6. IEEE (December 2011)

    Google Scholar 

  28. Shin, S., et al.: The user-group based recommendation for the diverse multimedia contents in the social network environments. In: 9th Int. Conf. on DASC, pp. 202–206. IEEE (2011)

    Google Scholar 

  29. Stringhini, G., Kruegel, C., Vigna, G.: Detecting spammers on social networks. In: Proc. of the 26th Annual Computer Security Applications Conf., pp. 1–9. ACM (2010)

    Google Scholar 

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Correspondence to Pasquale De Meo .

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De Meo, P., Messina, F., Rosaci, D., Sarné, G.M.L. (2015). 2D-SocialNetworks:AWay to Virally Distribute Popular Information Avoiding Spam. In: Camacho, D., Braubach, L., Venticinque, S., Badica, C. (eds) Intelligent Distributed Computing VIII. Studies in Computational Intelligence, vol 570. Springer, Cham. https://doi.org/10.1007/978-3-319-10422-5_38

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  • DOI: https://doi.org/10.1007/978-3-319-10422-5_38

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-10421-8

  • Online ISBN: 978-3-319-10422-5

  • eBook Packages: EngineeringEngineering (R0)

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