On URL Changes and Handovers in Social Media

  • Hossein HamooniEmail author
  • Nikan Chavoshi
  • Abdullah Mueen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10046)


Social media sites (e.g. Twitter and Pinterest) allow users to change the name of their accounts. A change in the account name results in a change in the URL of the user’s homepage. We develop an algorithm that extracts such changes from streaming data and discover that a large number of social media accounts are performing synchronous and collaborative URL changes. We identify various types of URL changes such as handover, exchange, serial handover and loop exchange. All such behaviors are likely to be automated behavior and, thus, indicate accounts that are either already involved in malicious activities or being prepared to do so.

In this paper, we focus on URL handovers where a URL is released by a user and claimed by another user. We find interesting association between handovers and temporal, textual and network behaviors of users. We show several anomalous behaviors from suspicious users for each of these associations. We identify that URL handovers are instantaneous automated operations. We further investigate to understand the benefits of URL handovers, and identify that handovers are strongly associated with reusable internal links and successful avoidance of suspension by the host site. Our handover detection algorithm, which makes such analysis possible, is scalable to process millions of posts (e.g. tweets, pins) and shared publicly online.


  1. 1.
  2. 2.
  3. 3.
  4. 4.
    Akoglu, L., Chandy, R., Faloutsos, C.: Opinion fraud detection in online reviews by network effects. In: ICWSM, pp. 2–11 (2013)Google Scholar
  5. 5.
    Asur, S., Huberman, B.A.: Predicting the future with social media. In: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, vol. 1, pp. 492–499. IEEE (2010)Google Scholar
  6. 6.
    Cheng, J., Danescu-Niculescu-Mizil, C., Leskovec, J.: Antisocial behavior in online discussion communities. In: Proceedings of ICWSM (2015)Google Scholar
  7. 7.
    Chu, Z., Gianvecchio, S., Wang, H., Jajodia, S.: Detecting automation of Twitter accounts: are you a human, bot, or cyborg? IEEE Trans. Dependable Secure Comput. 9(6), 811–824 (2012)CrossRefGoogle Scholar
  8. 8.
    Costa, A.F., Yamaguchi, Y., Traina, A.J.M., Traina Jr., C., Faloutsos, C.: RSC: mining and modeling temporal activity in social media. In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2015. ACM (2015)Google Scholar
  9. 9.
    Dhillon, I.S.: Co-clustering documents and words using bipartite co-clustering documents and words using bipartite spectral graph partitioning. In: Proceedings of 7th ACM SIGKDD Conference, pp. 269–274 (2001)Google Scholar
  10. 10.
    Lee, K., Caverlee, J., Webb, S.: Uncovering social spammers. In: Proceeding of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR 2010, p. 435. ACM Press (2010)Google Scholar
  11. 11.
    Li, H., Mukherjee, A., Liu, B., Kornfield, R., Emery, S.: Detecting campaign promoters on Twitter using Markov random fields. In: IEEE International Conference on Data Mining (ICDM), pp. 290–299 (2014)Google Scholar
  12. 12.
    Matsubara, Y., Sakurai, Y., Ueda, N., Yoshikawa, M.: Fast and exact monitoring of co-evolving data streams. In: IEEE International Conference on Data Mining, pp. 390–399. IEEE (2014)Google Scholar
  13. 13.
    Mueen, A.: Enumeration of time series motifs of all lengths. In: Proceedings - IEEE International Conference on Data Mining, ICDM, ICDM, pp. 547–556 (2013)Google Scholar
  14. 14.
    Ratkiewicz, J., Conover, M., Meiss, M., Goncalves, B., Flammini, A., Menczer, F.: Detecting and tracking political abuse in social media (2011)Google Scholar
  15. 15.
    Subrahmanian, V., Azaria, A., Durst, S., Kagan, V., Galstyan, A., Lerman, K., Zhu, L., Ferrara, E., Flammini, A., Menczer, F., Waltzman, R., Stevens, A., Dekhtyar, A., Gao, S., Hogg, T., Kooti, F., Liu, Y., Varol, O., Shiralkar, P., Vydiswaran, V., Mei, Q., Huang, T.: The DARPA Twitter bot challenge. IEEE Computer (2016, in press)Google Scholar
  16. 16.
    Thomas, K., Grier, C., Song, D., Paxson, V.: Suspended accounts in retrospect: an analysis of Twitter spam. In: Proceedings of the ACM, IMC 2011, pp. 243–258 (2011)Google Scholar
  17. 17.
    Thomas, K., Li, F., Grier, C., Paxson, V.: Consequences of connectivity: characterizing account hijacking on Twitter. In: Proceedings of the ACM SIGSAC Conference on Computer and Communications Security - CCS 2014, pp. 489–500. ACM Press (2014)Google Scholar
  18. 18.
    Thomas, K., Paxson, V., Mccoy, D., Grier, C.: Trafficking fraudulent accounts: the role of the underground market in Twitter spam and abuse trafficking fraudulent accounts. In: USENIX Security Symposium, SEC 2013, pp. 195–210 (2013)Google Scholar
  19. 19.
    Vlachos, M., Gunopulos, D., Das, G.: Rotation invariant distance measures for trajectories. In: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD 2004, p. 707. ACM Press (2004)Google Scholar
  20. 20.
    Wikipedia. Jaccard index – wikipedia, the free encyclopedia.

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Hossein Hamooni
    • 1
    Email author
  • Nikan Chavoshi
    • 1
  • Abdullah Mueen
    • 1
  1. 1.University of New MexicoAlbuquerqueUSA

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