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Topics, References and Assignment

First Session (July 10th):

 

  1. Community detection
    Overview of community detection methods and comparison [1,2]
  2. Identifying influentials
    Methods and techniques for identifying influentials in social networks [3-5]
  3. User behavior
    Understanding user behavior in social networks and classifying users [6,7]
  4. Facebook: Profiles and Applications
    User profiling and detection of malicious applications [8,9]
  5. Classical e-commerce
    Knowing and profiling the customers in e-commerce, and analyzing their behavior [10,11]
  6. Social networks as means for organizing illegal actions
    Infrastructures of social networks as means of organizing illegal actions (riots, revolution) [12-14]

 

Second Session (July 17th):

  1. Identifying spammers
    Methods and algorithms to identify and target spammers [15-18]

  2. Spammers behavior
    Investigation of spammers’ behavior, micro world and their tactics [19-21]

  3. Identifying personality traits through social networks
    Identification and prediction of personality characteristics from public data on social media [22-24]
  4. Tweets and privacy
    Detecting and preventing privacy leaks in twitter [25,26]
  5. De-Anonymizing Social Networks
    Methods to trace and link users in social networks [27,28]
  6. Smart Grid Privacy
    Methods to violate and protect users’ privacy in Smart Grids [29,30]

 

References:

1. Fortunato, S., Community detection in graphs. Physics Reports, 2010. 486(3): p. 75-174.
2. Leskovec, J., K.J. Lang, and M. Mahoney, Empirical comparison of algorithms for network community detection, in Proceedings of the 19th international conference on World wide web. 2010, ACM: Raleigh, North Carolina, USA. p. 631-640.
3. Bakshy, E., et al., Everyone's an influencer: quantifying influence on twitter, in Proceedings of the fourth ACM international conference on Web search and data mining. 2011, ACM: Hong Kong, China. p. 65-74.
4. Cha, M., et al. Measuring User Influence in Twitter: The Million Follower Fallacy. in Fourth International AAAI Conference on Weblogs and Social Media. 2010. George Washington University.
5. Weng, J., et al., TwitterRank: finding topic-sensitive influential twitterers, in Proceedings of the third ACM international conference on Web search and data mining. 2010, ACM: New York, New York, USA. p. 261-270.
6. M. Pennacchiottim, A.-M. Popescu. Democrats, Republicans and Starbucks Aficionados: User Classification in Twitter. doi:10.1145/2020408.2020477.
7. H. Krasnova, S. Spiekermann, K. Koroleva, T. Hildebrand. Online Social Networks: Why We Disclose. doi:10.1057/jit.2010.6
8. A. Mislove, V. Bimal, G. P. Krishna, and P. Druschel. Are Who You Know: Inferring User Profiles in Online Social Networks. doi:10.1145/1718487.1718519.
9. M. Egelea, A. Moserb, C. Kruegela, E. Kirdac. PoX: Protecting users from malicious Facebook applications. <http://dx.doi.org/10.1016/j.comcom.2012.04.016>
10. J. Y. Tsai, S. Egelman, L. Cranor, A. Acquisti. The Effect of Online Privacy Information on Purchasing Behavior: An Experimental Study. doi 10.1287/isre.1090.0260
11. J. Turow, L. Feldman, K. Meltzer. Open to Exploitation: America's Shoppers Online and Offline. <http://repository.upenn.edu/cgi/viewcontent.cgi?article=1035&context=asc_papers>
12. González-Bailón, S., et al., The Dynamics of Protest Recruitment through an Online Network. Sci. Rep., 2011. 1.
13. Khondker, H.H., Role of the new media in the Arab Spring. Globalizations, 2011. 8(5): p. 675-679.
14. Tonkin, E., H.D. Pfeiffer, and G. Tourte, Twitter, information sharing and the London riots? Bulletin of the American Society for Information Science and Technology, 2012. 38(2): p. 49-57.
15. Lim, E.-P., et al., Detecting product review spammers using rating behaviors, in Proceedings of the 19th ACM international conference on Information and knowledge management. 2010, ACM: Toronto, ON, Canada. p. 939-948.
16. Stringhini, G., C. Kruegel, and G. Vigna, Detecting spammers on social networks, in Proceedings of the 26th Annual Computer Security Applications Conference. 2010, ACM: Austin, Texas. p. 1-9.
17. Benevenuto, F., et al. Detecting Spammers on Twitter. 2010.
18. Benevenuto, F., et al., Identifying video spammers in online social networks, in Proceedings of the 4th international workshop on Adversarial information retrieval on the web. 2008, ACM: Beijing, China. p. 45-52.
19. Yang, C., et al., Analyzing spammers' social networks for fun and profit: a case study of cyber criminal ecosystem on twitter, in Proceedings of the 21st international conference on World Wide Web. 2012, ACM: Lyon, France. p. 71-80.
20. Leskovec, J., L.A. Adamic, and B.A. Huberman, The dynamics of viral marketing. ACM Trans. Web, 2007. 1(1): p. 5.
21. Ghosh, S., et al., Understanding and combating link farming in the twitter social network, in Proceedings of the 21st international conference on World Wide Web. 2012, ACM: Lyon, France. p. 61-70.
22. Gosling, S.D., et al., Manifestations of personality in online social networks: Self-reported Facebook-related behaviors and observable profile information. Cyberpsychology, Behavior, and Social Networking, 2011. 14(9): p. 483-488.
23. Golbeck, J., C. Robles, and K. Turner, Predicting personality with social media, in CHI '11 Extended Abstracts on Human Factors in Computing Systems. 2011, ACM: Vancouver, BC, Canada. p. 253-262.
24. Kosinski, M., D. Stillwell, and T. Graepel, Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 2013.
25. H. Mao, X. Shuai, A. Kapadia: Loose tweets: an analysis of privacy leaks on twitter. <http://dx.doi.org/10.1145/2046556.2046558>
26. E. De Cristofaro, C. Soriente, G. Tsudik, A. Williams: Hummingbird: Privacy at the Time of Twitter. IEEE Symposium on Security and Privacy. <http://doi.ieeecomputersociety.org/10.1109/SP.2012.26>
27. A. Narayanan, V. Shmatikov. De-anonymizing Social Networks. http://dx.doi.org/10.1109/SP.2009.22
28. A. Campan, T Truta. Data and structural k-anonymity in social networks. doi:10.1007/978-3-642-01718-6_4
29. A. Molina-Markham, P. Shenoy, K. Fu, E. Cecchet, D. Irwin. Private memoirs of a smart meter. <http://dx.doi.org/10.1145/1878431.1878446>
30. M. Jawurek, M. Johns, F. Kerschbaum. Plug-in privacy for Smart Meterin billing. <http://arxiv.org/abs/1012.2248>

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