Identification of leaders, lurkers, associates and spammers in a social network: context-dependent and context-independent approaches

Social Network Analysis and Mining

Volumn 1, Issue 3, 2011, Pages 241-254

  Fazeen, Mohamed ^a   Dantu, Ram ^a   Guturu, Parthasarathy ^a  

^a UNIVERSITY OF NORTH TEXAS   (United States)

Author keywords

Context dependent and context independent data analysis; Fuzzy logic; MLP; Naive Bayesian classifier; Random Forest; Social networks; Twitter

Indexed keywords

BAYESIAN NETWORKS; DECISION TREES; FUZZY LOGIC; SOCIAL NETWORKING (ONLINE);

CLASSIFICATION ACCURACY; CLASSIFICATION PERFORMANCE; CONTEXT INDEPENDENT; CONTEXTUAL INFORMATION; NAIVE BAYESIAN CLASSIFIER; RANDOM FOREST CLASSIFIER; RANDOM FORESTS; TWITTER;

CLASSIFICATION (OF INFORMATION);

EID: 84857957559     PISSN: 18695450     EISSN: 18695469     Source Type: Journal    
DOI: 10.1007/s13278-011-0017-9     Document Type: Review

References (32)

1. Bellman RE (1957) Dynamic programming. Rand Corporation
2. Berkowitz SD (1982) An introduction to structure analysis: the network approach to social research. Butterworth
3. Breiman L (2001) Random forests. Mach Learning 45(1):5–32
4. Cha M, Mislove A, Gummadi KP (2009) A measurement-driven analysis of information propagation in the flickr social network. In: Proceedings of 18th international conference on World wide web, pp 721–730
5. Cheong M, Lee V (2009) Integrating web-based intelligence retrieval and decision-making from the twitter trends knowledge base. In: Proceedings of 2nd ACM workshop on social web search and mining, Hong Kong, pp 1–8
6. Cingolani P (2010) jfuzzylogic, open source fuzzy logic library and FCL language implementation. http://jfuzzylogicsourceforgenet/html/indexhtml
7. Commission IE (1997) Technical committee no. 6: Industrial process measurement and control. sub-committee 65 b: Devices. iec 1131-programmable controllers.http://www.fuzzytechcom/binaries/ieccd1pdf
8. Dorein P, Sockman FN (1997) Evolution of social networks, vol 1. Overseas Publishers Association, Amsterdam
9. Garton L, Haythornthwaite C, Wellman B (1997) Studying online social networks. J Comp Mediated Commun 3(1):75–105
10. Kim S, Han S (2009) The method of inferring trust in web-based social network using fuzzy logic. In: Proceedings of international workshop on machine intelligence research, vol 2, pp 140–144
11. Klym N, Montpetit MJ (2008) Innovation at the edge: social TV and beyond. MIT Communications Futures Program (CFP). http://cfp.mit.edu/publications/CFP_Papers/Social%20TV%20Final%202008.09.01%20for%20distribution.pdf
12. Kossinets G, Watts DJ (2006) Empirical analysis of an evolving social network. Sci Agric 311(5757):88–90
13. Kumar R, Novak J, Tomkins A (2010) Structure and evolution of online social networks. In: Link mining: models, algorithms, and applications. Springer, Berlin
14. Lin Y, Sundaram H, Chi Y, Tatemura J, Tseng B (2007) Splog detection using content, time and link structures. In: Proceedings of IEEE international conference on multimedia and expo, pp 2030–2033
15. Ma W, Tran D, Sharma D (2009) A novel spam email detection system based on negative selection. In: Proceedings of 4th international conference on computer science and convergence information technology, Seoul, pp 987–992
16. Marsden PV, Campbell KE (1984) Measuring tie strength. Social Forces 63(2):482–501
17. Mathioudakis M, Koudas N, Marbach P (2010) Early online identification of attention gathering items in social media. In: Proceedings of 3rd ACM international conference web search and data mining, pp 301–310
18. McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: Homophily in social networks. Annu Rev Sociol 27:415–444
19. Mislove A, Marcon M, Gummadi KP, Druschel P, Bhattacharjee B (2007) Measurement and analysis of online social networks. In: Proceedings of 7th ACM SIGCOMM conference on international measurement, pp 29–42
20. Onnela JP, Saramaki J, Hyvonen J, Szabo G, Lazer D, Kaski K, Kertesz J, Barabasi AL (2007) Structure and tie strengths in mobile communication networks. Proc Natl Acad Sci USA 104(18):7332–7336
21. Rojas R (1996) Neural networks—a systematic introduction. Springer, Berlin
22. Scott J (1992) Social network analysis. Sage, Newbury Park
23. Strauss D, Ikeda M (1990) Pseudolikelihood estimation for social networks. J Am Stat Assoc 85:204–212
24. Wasserman S, Faust K (1994) Social network analysis. Cambridge University Press, Cambridge
25. Wasserman S, Pattison P (1996) Logit models and logistic regressions for social networks: I. an introduction to markov graphs and p*. Psychometrika 61(3):401–425
26. Watts DJ, Dodds PS, Newman MEJ (2002) Identity and search in social networks. Sci Agric 296(17):1302–1305
27. Weng J, Lim E, Jiang J, He Q (2010) Twitterrank: finding topic-sensitive influential twitterers. In: Proceedings of 3rd ACM international conference on web search and data mining, New York, pp 261–270
28. White H, Boorman S, Breiger R (1976) Social structure from multiple networks: I. blockmodels of roles and positions. Am J Sociol 81:730–780
29. Wikipedia (2010) Twitter. http://enwikipediaorg/wiki/Twitter
30. Wu C, Zhou B (2009) Analysis of tag within online social networks. In: Procedings of ACM 2009 international conference, pp 21–30
31. Yeh C, Chiang S (2009) Revisit bayesian approaches for spam detection. In: Proceedings of. 9th international conference for young computer scientists, Hunan, pp 659–664
32. Zhou B, Pei J, Luk W (2009) A brief survey on anonymization techniques for privacy preserving publishing of social network data. In: Proceedings of ACM SIGKDD Explorations Newsletter, vol 10, pp 12–22