Detection of virtual multi-identities
Andrzej Opaliński
AGH University of Science and Technology, Kraków, Poland.
DOI:
https://doi.org/10.7494/cmms.2014.3.0486
Abstract:
Last decade is a time of rapid evolution of the Internet. Various human’s life areas are migrating into the cyberspace, where people exist and act through virtual identities – personalization of themselves. Groups of virtual identities with their relations and context form cybersocieties. Due to the unique cyberspace’s structure, virtual identities are characterized by relatively high anonymity level. This cause the phenomena of the virtual multi-identities, where a single physical person incarnates a few virtual identities. This entails both positive and negative effects, and presented article concerns a negative one, which is deceptive opinion spam, generated by multi-identities of a single person. This problem constantly increases as relaying on the opinions from the WEB becomes very common. This article presents the concept which combines elements from various domains, linked in order to solve this problem and detect virtual multi-identities hiding in social networks such as web forums, blogs or recommendation portals. The paper describes a general concept and the architecture of the implemented system. At the end, the evaluation of the solution is carried out, based on the examples from the recommendation portal and web forum.
Cite as:
Opaliński, A. (2014). Detection of virtual multi-identities. Computer Methods in Materials Science, 14(3), 152 – 159. https://doi.org/10.7494/cmms.2014.3.0486
Article (PDF):
Keywords:
Virtual-identities, Cybersociety, Opinion spam
Publication dates:
Received: 23.10.2014, accepted: 12.12.2014, published:
Publication type:
Original scientific paper
References:
Chen, H. C., Goldberg, M., Magdon-Ismail, M. 2004. Identifying multi-ID users in open forums, Intelligence and Security Informatics, 176-186.
Chen, C., Wu, K., Srinivasan, V., Zhang, X. 2013. Battling the internet water army: Detection of hidden paid posters, Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 116-120.
Christopherson, K.M., 2007, The positive and negative implications of anonymity in Internet social interactions: On the Internet, nobody knows you’re a dog, Computers in Human Behavior, 23(6), 3038-3056. de Vel, O., Anderson, A., Corney, M., Mohay, G., 2001, Mining e-mail content for author identification forensics, ACM Sigmod Record, 30(4), 55-64. International Telecommunication Union, 2012, Measuring the Information Society 2012, Place des Nations, CH-1211 Geneva Switzerland.
Jindal, N., Liu, B., 2007, Analyzing and Detecting Review Spam, Data Mining, ICDM 2007, October 28-31, 547- 552.
Jindal, N., Liu, B., 2008, Opinion spam and analysis, Proceedings of the International Conference on Web Search and Web Data Mining, 219-230.
Juola, P., 2007, Authorship attribution, Foundations and Trends in Information Retrieval, 1(3), 233-334.
Kim, S.M., Pantel, P., Chklovski, T., Pennacchiotti, M., 2006, Automatically assessing review helpfulness, Proc. of the 2006 Conference on Empirical Methods in Natural Language Processing, 423-430.
Le, J., Edmonds, A., Hester, V., Biewald, L., 2010, Ensuring quality in crowdsourced search relevance evaluation: The effects of training question distribution, SIGIR 2010 Workshop on Crowdsourcing for Search Evaluation, 21- 26.
Li, J., Wang, G.A., Chen, H., 2010, Identity matching using personal and social identity features, Information Systems Frontiers, 13(1), 101-113.
Maciolek, P., Dobrowolski, G., 2013, CLUO: Web-Scale Text Mining System for Open Source Intelligence Purposes, Computer Science, 14(1), 45. DOI:10.7494. Miniwatts Marketing Group: World internet usage and population statistics, 2012, available online at: http://www.internetworldstats.com.
Mukherjee, A., Liu, B., Glance, N., 2012, Spotting Fake reviewer groups in consumer reviews, Proc. of the 21st Int. Conf. on WWW, 191-200.
Musial, K., Kazienko, P., 2013, Social networks on the internet, World Wide Web, 16 (1), 31-72.
Ott, M., Choi, Y., Cardie, C., Hancock, J. T., 2011, Finding deceptive opinion spam by any stretch of the imagination, Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, 1, 309-319. –
Pillay, S.R., Solorio, T., 2010, Authorship attribution of web forum posts, In: eCrime Researchers Summit (eCrime), 1-7.
Stamatatos, E., 2007, Author identification using imbalanced and limited training texts, 18th International Workshop on DEXA 2007, 237-241.
Stamatatos, E., 2009a, A survey of modern authorship attribution methods, Journal of the American Society for Information Science and Technology, 60(3), 538-556.
Stamatatos, E., 2009b, Intrinsic plagiarism detection using character n-gram profiles, 3rd PAN Workshop on Un- covering Plagiarism, Authorship and Social Software Misuse, 38.
Thomas, D., Douglas, T., Loader, B., eds., 2000, Cybercrime: Law enforcement, security and surveillance in the information age, Psychology Press.
Wang, A. G., Atabakhsh, H., Petersen, T., Chen, H., 2005, Discovering identity problems: A case study, Intelligence and Security Informatics, 368-373.
Wang, D., Irani, D., Pu, C., 2011, A social-spam detection framework, Proceedings of the 8th Annual Collaboration, Electronic Messaging, Anti-Abuse and Spam Conference, 46-54.
Wang, G., Mohanlal, M., Wilson, C., Wang, X., Metzger, M.,
Zheng, H., Zhao, B., 2012, Social Turing Tests: Crowdsourcing Sybil Detection, arXiv pre- print:1205.3856.
Weimer, M., Gurevych, I., M¨uhlh¨auser, M., 2007, Automatically assessing the post quality in online discussions on software, Proceedings of the 45th Annual Meeting of the ACL, 125-128.
Xie, S., Wang, G., Lin, S., Yu, P.S., 2012, Review spam detection via temporal pattern discovery, Proceedings of the 18th ACM SIGKDD, 823-831.
Xu, J., Chau, M., Wang, G.A., Li, J., 2007, Complex problem solving: identity matching based on social contextual information, Journal of the Association for Information Systems, 8(10), 525-545.
Yang, Y.C., Padmanabhan, B., 2010, Toward user patterns for online security: Observation time and user identification, Decision Support Systems, 48(4), 548-558.
Zheng, R., Qin, Y., Huang, Z., Chen, H., 2003, Authorship analysis in cybercrime investigation, Intelligence and Security Informatics, Springer Berlin Heidelberg 59-73.
Zheng, R., Li, J., Chen, H., Huang, Z., 2005, A framework for authorship identification of online messages: Writing- style features and classification techniques, Journal of the American Society for Information Science and Technology, 57(3), 378-393. – 159