Utilizando Métricas de Ego-network para Validação de Atributos dos Perfis de Usuários de Redes Sociais On-line

• Hélder Seixas Lima PUC Minas
• Humberto Torres Marques-Neto PUC Minas

DOI: https://doi.org/10.5753/sbrc.2018.2428

Abstract

Online social network users identify themselves through their profiles, which are usually composed of attributes such as name, gender, age, city, among others. Since the profile attributes are self-declared, the possibility of malicious users creating accounts with false information arises. This work proposes a framework that determines a trustworthiness level for each attribute used in the profile of an online social network user. The proposed framework uses metrics in the ego-network context to verify common phenomena in social networks. The proposal was evaluated experimentally with two real samples and two synthetic samples of two social networks: Facebook and Google+. Synthetic samples simulate false users. The results showed that the trustworthiness levels determined by the framework are higher for most profile attributes of real samples when compared to those of synthetic samples.

References

Bahri, L., Carminati, B., and Ferrari, E. (2014). Community-based identity validation on online social networks. In Distributed Computing Systems (ICDCS), 2014 IEEE 34th International Conference on, pages 21–30. IEEE.

Bahri, L., Carminati, B., and Ferrari, E. (2016). Coip—continuous, operable, impartial, and privacy-aware identity validity estimation for osn proles. ACM Transactions on the Web (TWEB), 10(4):23.

Bhattacharyya, P., Garg, A., and Wu, S. F. (2011). Analysis of user keyword similarity in online social networks. Social network analysis and mining, 1(3):143–158.

Bianconi, G., Darst, R. K., Iacovacci, J., and Fortunato, S. (2014). Triadic closure as a basic generating mechanism of communities in complex networks. Physical Review E, 90(4):042806.

Brandt, C. and Leskovec, J. (2014). Status and friendship: Mechanisms of social network evolution. In Proceedings of the 23rd International Conference on World Wide Web, WWW ’14 Companion, pages 229–230, New York, NY, USA. ACM.

Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32.

Caetano, J., Lima, H., Santos, M., and Marques-Neto, H. (2017). Utilizando análise de sentimentos para denição da homolia política dos usuários do twitter durante a eleição presidencial americana de 2016. In Proceedings of the 6th Brazilian Workshop on Social Network Analysis and Mining.

Cai, X., Bain, M., Krzywicki, A., Wobcke, W., Kim, Y. S., Compton, P., and Mahidadia, A. (2010). Collaborative ltering for people to people recommendation in social networks. In Australasian Joint Conference on Articial Intelligence, pages 476–485. Springer.

Colleoni, E., Rozza, A., and Arvidsson, A. (2014). Echo chamber or public sphere? predicting political orientation and measuring political homophily in twitter using big data. Journal of Communication, 64(2):317–332.

Currarini, S., Jackson, M. O., and Pin, P. (2009). An economic model of friendship: Homophily, minorities, and segregation. Econometrica, 77(4):1003–1045.

Danezis, G. and Mittal, P. (2009). Sybilinfer: Detecting sybil nodes using social networks. In NDSS. San Diego, CA. In International Workshop on Peer-to-Peer Douceur, J. R. (2002). The sybil attack. Systems, pages 251–260. Springer.

Easley, D. and Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge University Press.

Halberstam, Y. and Knight, B. (2014). Homophily, group size, and the diffusion of political information in social networks: Evidence from twitter. Technical report, National Bureau of Economic Research.

Han, X., Wang, L., Crespi, N., Park, S., and Cuevas, íA. (2015). Alike people, alike interests? inferring interest similarity in online social networks. Decision Support Systems, 69:92–106.

Himelboim, I., Sweetser, K. D., Tinkham, S. F., Cameron, K., Danelo, M., and West, K. (2014). Valence-based homophily on twitter: network analysis of emotions new media & society, page and political talk in the 2012 presidential election. 1461444814555096.

Huang, H., Tang, J., Liu, L., Luo, J., and Fu, X. (2015). Triadic closure pattern analyIEEE Transactions on Knowledge and Data sis and prediction in social networks. Engineering, 27(12):3374–3389.

Kwak, H., Lee, C., Park, H., and Moon, S. (2010). What is twitter, a social network or a news media? In Proceedings of the 19th international conference on World wide web, pages 591–600. ACM.

Laleh, N., Carminati, B., and Ferrari, E. (2017). Risk assessment in social networks based on user anomalous behaviour. IEEE Transactions on Dependable and Secure Computing, PP(99):1–1.

Leskovec, J. and Mcauley, J. J. (2012). Learning to discover social circles in ego networks. In Advances in neural information processing systems, pages 539–547.

McPherson, M., Smith-Lovin, L., and Cook, J. M. (2001). Birds of a feather: Homophily in social networks. Annual review of sociology, pages 415–444.

Mislove, A., Viswanath, B., Gummadi, K. P., and Druschel, P. (2010). You are who you know: inferring user proles in online social networks. In Proceedings of the third ACM international conference on Web search and data mining, pages 251–260. ACM.

Mukta, M. S. H., Ali, M. E., and Mahmud, J. (2016). Identifying and validating personality traits-based homophilies for an egocentric network. Social Network Analysis and Mining, 6(1):74.

Mulamba, D., Ray, I., and Ray, I. (2016). SybilRadar: A Graph-Structure Based Framework for Sybil Detection in On-line Social Networks, pages 179–193. Springer International Publishing, Cham.

Newman, M. E. (2003a). Mixing patterns in networks. Physical Review E, 67(2):026126.

Newman, M. E. (2003b). The structure and function of complex networks. SIAM review, 45(2):167–256.

Rapoport, A. (1953). Spread of information through a population with socio-structural bias: I. assumption of transitivity. Bulletin of Mathematical Biology, 15(4):523–533.

Silva, L. A. d., Peres, S. M., and Boscarioli, C. (2016). Introdução à mineração de dados: com aplicações em R. Elsevier.

Sirivianos, M., Kim, K., Gan, J. W., and Yang, X. (2014). Leveraging social feedback to verify online identity claims. ACM Transactions on the Web (TWEB), 8(2):9.

Soliman, A., Bahri, L., Girdzijauskas, S., Carminati, B., and Ferrari, E. (2016). Cadiva: cooperative and adaptive decentralized identity validation model for social networks. Social Network Analysis and Mining, 6(1):1–22.

Tran, N., Li, J., Subramanian, L., and Chow, S. S. M. (2011). Optimal sybil-resilient node admission control. In 2011 Proceedings IEEE INFOCOM, pages 3218–3226.

Wei, W., Xu, F., Tan, C. C., and Li, Q. (2013). Sybildefender: A defense mechanism for sybil attacks in large social networks. IEEE Transactions on Parallel and Distributed Systems, 24(12):2492–2502.

Wen, J. and Yuan, Q. (2016). Social circles discovery based on structural and attribute similarities. In 2016 IEEE Trustcom/BigDataSE/ISPA, pages 1652–1659.

Yu, H., Gibbons, P. B., Kaminsky, M., and Xiao, F. (2010). Sybillimit: A near-optimal social network defense against sybil attacks. IEEE/ACM Transactions on Networking, 18(3):885–898.

Yu, H., Kaminsky, M., Gibbons, P. B., and Flaxman, A. D. (2008). Sybilguard: Defending IEEE/ACM Transactions on Networking, against sybil attacks via social networks. 16(3):576–589.