Badly evolved? Exploring long-surviving suspicious users on twitter

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 10539 LNCS, Issue , 2017, Pages 218-233

  Alfifi, Majid ^a   Caverlee, James ^a  

^a Department of Electrical and Computer Engineering   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION);

CLASSIFICATION TASKS; LINGUISTIC PATTERNS; SOCIAL MEDIA; TEMPORAL CHANGE;

SOCIAL NETWORKING (ONLINE);

EID: 85029479182     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-67217-5_14     Document Type: Conference Paper

References (28)

1. Benevenuto, F., Magno, G., Rodrigues, T., Almeida, V.: Detecting spammers on Twitter. In: Collaboration, Electronic Messaging, Anti-abuse and Spam Conference (CEAS), vol. 6, p. 12 (2010)
2. Berger, J.M., Morgan, J.: The ISIS Twitter census: defining and describing the population of ISIS supporters on twitter. In: The Brookings Project on US Relations with the Islamic World, vol. 3, no. 20 (2015)
3. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)
4. Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
5. Castillo, C., Mendoza, M., Poblete, B.: Information credibility on Twitter. In: Proceedings of the 20th International Conference on World Wide Web, pp. 675– 684. ACM (2011)
6. Conover, M., Ratkiewicz, J., Francisco, M.R., Gonçalves, B., Menczer, F., Flammini, A.: Political polarization on Twitter. ICWSM 133, 89–96 (2011)
7. Danescu-Niculescu-Mizil, C., West, R., Jurafsky, D., Leskovec, J., Potts, C.: No country for old members: user lifecycle and linguistic change in online communities. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 307–318. International World Wide Web Conferences Steering Committee (2013)
8. Davis, C.A., Varol, O., Ferrara, E., Flammini, A., Menczer, F.: Botornot: a system to evaluate social bots. In: Proceedings of the 25th International Conference Companion on World Wide Web, pp. 273–274. International World Wide Web Conferences Steering Committee (2016)
9. Ferrara, E., Wang, W.-Q., Varol, O., Flammini, A., Galstyan, A.: Predicting online extremism, content adopters, and interaction reciprocity
10. Grier, C., Thomas, K., Paxson, V., Zhang, M.: @ spam: the underground on 140 characters or less. In: Proceedings of the 17th ACM Conference on Computer and Communications Security, pp. 27–37. ACM (2010)
11. Hu, X., Tang, J., Zhang, Y., Liu, H.: Social spammer detection in microblogging. In: Twenty-Third International Joint Conference on Artificial Intelligence (2013)
12. Katz, S.: Estimation of probabilities from sparse data for the language model component of a speech recognizer. IEEE Trans. Acoust. Speech Sig. Process. 35(3), 400–401 (1987)
13. King, G., Pan, J., Roberts, M.E.: How the Chinese government fabricates social media posts for strategic distraction, not engaged argument. Harvard University (2016)
14. Lee, K., Caverlee, J., Webb, S.: Uncovering social spammers: social honeypots+ machine learning. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 435–442. ACM (2010)
15. Lin, P.C., Huang, P.M.: A study of effective features for detecting long-surviving twitter spam accounts. In: 2013 15th International Conference on Advanced Communication Technology (ICACT), pp. 841–846. IEEE (2013)
16. Lotan, G., Graeff, E., Ananny, M., Gaffney, D., Pearce, I., et al.: The Arab spring— the revolutions were tweeted: information flows during the 2011 Tunisian and Egyptian revolutions. Int. J. Commun. 5, 31 (2011)
17. Meng, X., Bradley, J., Yavuz, B., Sparks, E., Venkataraman, S., Liu, D., Freeman, J., Tsai, D., Amde, M., Owen, S., et al.: Mllib: machine learning in apache spark. J. Mach. Learn. Res. 17(34), 1–7 (2016)
18. Mustafaraj, E., Metaxas, P.T.: From obscurity to prominence in minutes: political speech and real-time search. In: Proceedings of the WebSci10: Extending the Frontiers of Society On-Line (2010)
19. Ratkiewicz, J., Conover, M., Meiss, M.R., Gonçalves, B., Flammini, A., Menczer, F.: Detecting and tracking political abuse in social media. ICWSM 11, 297–304 (2011)
20. Song, J., Lee, S., Kim, J.: Spam filtering in Twitter using sender-receiver relationship
21. Starbird, K., Palen, L.: (How) will the revolution be retweeted?: information diffusion and the 2011 Egyptian uprising. In: Proceedings of the ACM 2012 Conference on Computer Supported Cooperative Work, pp. 7–16. ACM (2012)
22. Stringhini, G., Kruegel, C., Vigna, G.: Detecting spammers on social networks. In: Proceedings of the 26th Annual Computer Security Applications Conference, pp. 1–9. ACM (2010)
23. Thomas, K., Grier, C., Paxson, V.: Adapting social spam infrastructure for political censorship. In: LEET (2012)
24. Thomas, K., Grier, C., Song, D., Paxson, V.: Suspended accounts in retrospect: an analysis of Twitter spam. In: Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, pp. 243–258. ACM (2011)
25. Wang, A.H.: Don’t follow me: spam detection in Twitter. In: Proceedings of the 2010 International Conference on Security and Cryptography (SECRYPT), pp. 1–10. IEEE (2010)
26. Wei, W., Joseph, K., Liu, H., Carley, K.M.: The fragility of Twitter social networks against suspended users. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, pp. 9–16. ACM (2015)
27. Yang, C., Harkreader, R.C., Gu, G.: Die free or live hard? Empirical evaluation and new design for fighting evolving Twitter spammers
28. Zaharia, M., Chowdhury, M., Das, T., Dave, A., Ma, J., McCauley, M., Franklin, M.J., Shenker, S., Stoica, I.: Resilient distributed datasets: a fault-tolerant abstraction for in-memory cluster computing. In: Proceedings of the 9th USENIX Conference on Networked Systems Design and Implementation, p. 2. USENIX Association (2012)