Predicting susceptibility to social bots on Twitter

Proceedings of the 2013 IEEE 14th International Conference on Information Reuse and Integration, IEEE IRI 2013

Volumn , Issue , 2013, Pages 6-13

  Wald, Randall ^a   Khoshgoftaar, Taghi M ^a   Napolitano, Amri ^a   Sumner, Chris ^b  

^a FLORIDA ATLANTIC UNIVERSITY   (United States)

^b Online Privacy Foundation   (United States)

Author keywords

feature selection; social bots; Twitter

Indexed keywords

ALGORITHMS; DECISION TREES; FEATURE EXTRACTION; FORECASTING; INFORMATION USE;

FEATURE RANKING; RANDOM FOREST ALGORITHM; SOCIAL BOTS; SOCIAL NETWORKING SITES; SPAM DETECTION; TWITTER;

SOCIAL NETWORKING (ONLINE);

EID: 84891046628     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IRI.2013.6642447     Document Type: Conference Paper

References (50)

1. A. A. Amleshwaram, N. Reddy, S. Yadav, G. Gu, and C. Yang, "CATS:Characterizing automation of Twitter spammers," in Fifth InternationalConference on Communication Systems and Networks (COMSNETS).IEEE, 2013, pp. 1-10.
2. E. Bakshy, J. M. Hofman, W. A. Mason, and D. J. Watts, "Everyonesan influencer: quantifying influence on twitter," in Proceedings of thefourth ACM international conference on Web search and data mining,ser. WSDM 11. New York, NY, USA: ACM, 2011, pp. 65-74. [Online]. Available: http://doi.acm.org/10. 1145/1935826.1935845
3. R. Battiti, "Using mutual information for selecting features in supervisedneural net learning," IEEE Transactions On Neural Networks, pp. 537-550, 1994.
4. Y. Boshmaf, I. Muslukhov, K. Beznosov, and M. Ripeanu, "The socialbotnetwork: when bots socialize for fame and money," in Proceedingsof the 27th Annual Computer Security Applications Conference, ser.ACSAC 11. New York, NY, USA: ACM, 2011, pp. 93-102. [Online].Available: http://doi.acm.org/10. 1145/2076732.2076746
5. D. Boyd, S. Golder, and G. Lotan, "Tweet, tweet, retweet: Conversationalaspects of retweeting on Twitter," in 43rd Hawaii InternationalConference on System Sciences (HICSS). IEEE, 2010, pp. 1-10.
6. L. Breiman, "Random forests," Machine Learning, vol. 45, no. 1,pp. 5-32, Oct 2001. [Online]. Available: http://dx.doi.org/10.1023/A: 1010933404324
7. G. Brown, T. Howe, M. Ihbe, A. Prakash, and K. Borders, "Socialnetworks and context-aware spam," in Proceedings of the 2008 ACMconference on Computer supported cooperative work, ser. CSCW 08.New York, NY, USA: ACM, 2008, pp. 403-412. [Online]. Available:http://doi.acm.org/10.1145/ 1460563.1460628
8. J. Campo-A vila, N. Moreno-Vergara, and M. Trella-Lopez, "Analizyingfactors to increase the influence of a twitter user," in Highlights inPractical Applications of Agents and Multiagent Systems, ser. Advancesin Intelligent and Soft Computing, J. B. Perez, J. M. Corchado, M. N.Moreno, V. Julian, P. Mathieu, J. Canada-Bago, A. Ortega, and A. F.Caballero, Eds. Springer Berlin Heidelberg, 2011, vol. 89, pp. 69-76. [Online]. Available: http://dx.doi.org/10.1007/978-3-642-19917-2 9
9. M. Cha, H. Haddadi, F. Benevenuto, and K. P. Gummadi, "Measuringuser influence in Twitter: The million follower fallacy," in Proceedingsof International AAAI Conference on Weblogs and Social, 2010.
10. Z. Chu, S. Gianvecchio, H. Wang, and S. Jajodia, "Detecting automationof Twitter accounts: Are you a human, bot, or cyborg?" IEEE Transactionson Dependable and Secure Computing, pp. 811-824, 2012.
11. W. J. Conover, Practical Nonparametric Studies. John Wiley and Sons,2nd edition, 1971.
12. J. Cotterell, Social Networks in Youth and Adolescence, ser.Adolescence and Society. Taylor &Francis, 2007. [Online]. Available:http://books.google. com/books?idoK2bVXb4DoC
13. O. V. Deryugina, "Chatterbots," Scientific and Technical InformationProcessing, vol. 37, no. 2, pp. 143-147, 2010. [Online]. Available:http://dx.doi.org/10.3103/S0147688210020097
14. D. J. Dittman, T. M. Khoshgoftaar, R. Wald, and J. Van Hulse,"Comparative analysis of DNA microarray data through the use offeature selection techniques," in Ninth IEEE International Conferenceon Machine Learning and Applications (ICMLA), December 2010, pp.147-152.
15. P. Doreian and F. Stokman, Evolution of social networks, ser. Thejournal of mathematical sociology. Gordon &Breach PublishingGroup, 1997, no. v. 1. [Online]. Available: http://books.google.com/books?id=9o3vLXNTGxsC
16. S. Ghosh, B. Viswanath, F. Kooti, N. K. Sharma, G. Korlam,F. Benevenuto, N. Ganguly, and K. P. Gummadi, "Understanding andcombating link farming in the twitter social network," in Proceedings ofthe 21st international conference on World Wide Web, ser. WWW 12.New York, NY, USA: ACM, 2012, pp. 61-70. [Online]. Available:http://doi.acm.org/10.1145/2187836.2187846
17. C. Grier, K. Thomas, V. Paxson, and M. Zhang, "@spam: theunderground on 140 characters or less," in Proceedings of the 17thACM conference on Computer and communications security, ser. CCS10. New York, NY, USA: ACM, 2010, pp. 27-37. [Online]. Available:http://doi.acm.org/10.1145/1866307.1866311
18. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, andI. H. Witten, "The WEKA data mining software: An update," SIGKDDExplorations Newsletter, vol. 11, no. 1, pp. 10-18, 2009.
19. S. Haykin, Neural Networks: A Comprehensive Foundation 2nd edition.Prentice Hall, 1998.
20. T. Hwang. (2011) Help robots take over the internet:The socialbots 2011 competition: Web ecologyproject. [Online]. Available: http://www. webecologyproject.org/2011/01/help-robots-take-over-The-internet-The-socialbots- 2011-competition
21. I. Kononenko, "Estimating attributes: Analysis and extensions of relief,"in Machine Learning: ECML-94, ser. Lecture Notes in ComputerScience, F. Bergadano and L. De Raedt, Eds. Springer Berlin/ Heidelberg, 1994, vol. 784, pp. 171-182. [Online]. Available:http://dx.doi.org/10.1007/3- 540-57868-4 57
22. R. Kumar, J. Novak, and A. Tomkins, "Structure and evolutionof online social networks," in Link Mining: Models, Algorithms,and Applications, P. S. Yu, J. Han, and C. Faloutsos, Eds.Springer New York, 2010, pp. 337-357. [Online]. Available: http://dx.doi.org/10.1007/978-1-4419-6515-8 13
23. H. Kwak, C. Lee, H. Park, and S. Moon, "What is Twitter, asocial network or a news media?" in Proceedings of the 19thinternational conference on World wide web, ser. WWW 10. NewYork, NY, USA: ACM, 2010, pp. 591-600. [Online]. Available:http://doi.acm.org/10.1145/1772690.1772751
24. S. Le Cessie and J. C. V. Houwelingen, "Ridge estimators in logisticregression," Journal of the Royal Statistical Society. Series C (AppliedStatistics), pp. 191-201, 1992.
25. T.-Y. Liu, "EasyEnsemble and feature selection for imbalance data sets,"in IJCBS 09: International Joint Conference on Bioinformatics, SystemsBiology and Intelligent Computing, 2009., August 2009, pp. 517-520.
26. S. Livingstone and D. R. Brake, "On the rapid rise of socialnetworking sites: New findings and policy implications," Children&Society, vol. 24, no. 1, pp. 75-83, 2010. [Online]. Available:http://dx.doi.org/10.1111/j.1099-0860.2009.00243.x
27. H. Loebner. (2013) Home page of the Loebner Prize. [Online].Available: http://www.loebner.net/Prizef/loebner-prize.html
28. C. Lumezanu and N. Feamster, "Observing common spam inTwitter and email," in Proceedings of the 2012 ACM conferenceon Internet measurement conference, ser. IMC 12. New York,NY, USA: ACM, 2012, pp. 461-466. [Online]. Available: http://doi.acm.org/10.1145/2398776.2398824
29. M. McCord and M. Chuah, "Spam detection on Twitter using traditionalclassifiers," in Autonomic and Trusted Computing, ser. Lecture Notesin Computer Science, J. M. A. Calero, L. T. Yang, F. G. Marmol,L. J. Garc?a Villalba, A. X. Li, and Y. Wang, Eds. SpringerBerlin Heidelberg, 2011, vol. 6906, pp. 175-186. [Online]. Available:http://dx.doi.org/10.1007/978-3-642- 23496-5 13
30. S. M. Rodrigo and J. G. F. Abraham, "Development and implementationof a chat bot in a social network," in Ninth International Conference onInformation Technology: New Generations (ITNG). IEEE, 2012, pp.751-755.
31. N. Seliya, T. M. Khoshgoftaar, and J. Van Hulse, "A study on therelationships of classifier performance metrics," in 21st InternationalConference on Tools with Artificial Intelligence, November 2009, pp.59-66.
32. J. Song, S. Lee, and J. Kim, "Spam filtering in Twitter using senderreceiverrelationship," in Recent Advances in Intrusion Detection, ser.Lecture Notes in Computer Science, R. Sommer, D. Balzarotti, andG. Maier, Eds. Springer Berlin Heidelberg, 2011, vol. 6961, pp. 301-317. [Online]. Available: http://dx.doi.org/10.1007/978-3-642-23644-016
33. J. Souza, N. Japkowicz, and S. Matwin, "Stochfs: A framework forcombining feature selection outcomes through a stochastic process," inKnowledge Discovery in Databases: PKDD 2005, ser. Lecture Notes inComputer Science. Springer Berlin / Heidelberg, 2005, vol. 3721, pp.667-674.
34. C. Sumner, A. Byers, R. Boochever, and G. Park, "Predicting darktriad personality traits from Twitter usage and a linguistic analysis oftweets," in Machine Learning and Applications (ICMLA), 2012 11thInternational Conference on, vol. 2, 2012, pp. 386-393.
35. Y. Takhteyev, A. Gruzd, and B. Wellman, "Geography of Twitternetworks," Social Networks, vol. 34, no. 1, pp. 73-81, 2012,capturing Context: Integrating Spatial and Social Network Analyses. [Online]. Available: http://www.sciencedirect.com/science/article/pii/ S0378873311000359
36. Y. R. Tausczik and J. W. Pennebaker, "The psychological meaningof words: Liwc and computerized text analysis methods," Journal ofLanguage and Social Psychology, vol. 29, no. 1, pp. 24-54, 2010. [Online]. Available: http://jls.sagepub.com/content/29/1/24.abstract
37. K. Thomas, C. Grier, J. Ma, V. Paxson, and D. Song, "Design andevaluation of a real-time url spam filtering service," in IEEE Symposiumon Security and Privacy (SP). IEEE, 2011, pp. 447-462.
38. K. Thomas, C. Grier, D. Song, and V. Paxson, "Suspended accounts inretrospect: an analysis of twitter spam," in Proceedings of the 2011ACM SIGCOMM conference on Internet measurement conference, ser.IMC 11. New York, NY, USA: ACM, 2011, pp. 243-258. [Online].Available: http://doi.acm.org/10.1145/ 2068816.2068840
39. H. Thomases, Twitter Marketing: An Hour a Day, ser. Serious skills.Wiley, 2009. [Online]. Available: http://books.google.com/books?id=oGBN0pyfkF4C
40. A. M. Turing, "Computing machinery and intelligence," Mind, vol. 59,no. 236, pp. 433-460, 1950.
41. V. G. Tusher, R. Tibshirani, and G. Chu, "Significance analysis ofmicroarrays applied to the ionizing radiation response," Proceedings ofthe National Academy of Sciences, vol. 98, no. 9, pp. 5116-5121, 2001. [Online]. Available: http://www.pnas.org/content/98/9/5116.abstract
42. J. Van Hulse, T. M. Khoshgoftaar, and A. Napolitano, "A comparativeevaluation of feature ranking methods for high dimensional bioinformaticsdata," in Information Reuse and Integration (IRI), 2011 IEEEInternational Conference on, August 2011, pp. 315-320.
43. J. Van Hulse, T. M. Khoshgoftaar, A. Napolitano, and R. Wald, "Featureselection with high-dimensional imbalanced data," in IEEE InternationalConference on Data Mining Workshops, 2009. ICDMW 09., Y. Saygin,J. X. Yu, H. Kargupta, W. Wang, S. Ranka, P. S. Yu, and X. Wu, Eds.IEEE Computer Society, December 2009, pp. 507-514.
44. C. Wagner, S. Mitter, C. Korner, and M. Strohmaier, "When socialbots attack: Modeling susceptibility of users in online social networks,"Making Sense of Microposts (# MSM2012), p. 2, 2012.
45. R. Wald, T. M. Khoshgoftaar, A. Napolitano, and C. Sumner, "UsingTwitter content to predict psychopathy," in Machine Learning andApplications (ICMLA), 2012 11th International Conference on, vol. 2,2012, pp. 394-401.
46. R. Wallace, "The elements of AIML style," Alice AI Foundation, 2003.
47. A. H. Wang, "Dont follow me: Spam detection in twitter," in Proceedingsof the 2010 International Conference on Security and Cryptography(SECRYPT). IEEE, 2010, pp. 1-10.
48. M. Wasikowski and X. wen Chen, "Combating the small sample classimbalance problem using feature selection," IEEE Transactions onKnowledge and Data Engineering, vol. 22, no. 10, pp. 1388-1400,October 2010.
49. J. Weizenbaum, "Eliza-A computer program for the study ofnatural language communication between man and machine," Commun.ACM, vol. 9, no. 1, pp. 36-45, Jan. 1966. [Online]. Available:http://doi.acm.org/10.1145/365153. 365168
50. I. H. Witten and E. Frank, Data Mining: Practical machine learningtools and techniques, 2nd ed. Morgan Kaufmann, 2005.