Syntactic Analysis for Monitoring Personal Information Leakage on Social Network Services: A Case Study on Twitter

  • Dongjin Choi
  • Ilsun You
  • Pankoo Kim
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7804)


Social network services such as Twitter and Facebook can be considered as a new media different from the typical media group. The information on social media spread much faster than any other traditional news media due to the fact that people can upload information with no constrain to time or location. Because of this reason, people got fascinated by SNS and it sinks into our life. People express their emotional status to let others know what they feel about information or events. However, there is a high possibility that people not only share information with others, but also they expose personal information unintentionally such as place to live, phone number, date of birth, and more. This will be serious problem if someone has impure mind. It is actually happening in cyber-stalking, offline stalking or others. There are also many spam messages in SNS because of the fact that information in SNS spread much faster than any other media and it is easy to send a message to others. In other words, SNS provides vast backbone environment to spammers to hunt normal pure users. In order to prevent information leakage and detect spam messages, many researchers traditionally have been studied for monitoring email systems, web blogs, and so on. In this paper, we dealt with text message data in Twitter which is one of the most popular social network services over the world in order to reveal various hidden patterns. Twitter data is severely dangerous to organizations and more is that anyone who has Twitter account can access to any users by “following” function. The following function does not require permission from the requested person to confirm to ready their timelines. This study will be focused on the user to whom exchange text messages and what types of information they reciprocated with others by monitoring 50 million tweets on November in 2009 which was collected by Stanford University.


Information flow Social network services Information leakage Twitter 


  1. 1.
    Krishnamurthy, B.: I know what you will do next summer. ACM SIGCOMM Computer Communication Review 40(5), 65–70 (2010)CrossRefGoogle Scholar
  2. 2.
    Yim, G., Hori, Y.: Guest Editorial: Information Leakage Prevention in Emerging Technologies. Journal of Internet Services and Information Security 2(3-4), 1–2 (2012)Google Scholar
  3. 3.
    Choi, D., Jin, S., Yoon, H.: A personal Information Leakage Prevention Method on the Internet. In: IEEE 10th International Symposium on Consumer Electronics, pp. 1–5 (2006)Google Scholar
  4. 4.
    Zhang, D.Y., Zeng, Y., Wang, L., Li, H., Geng, Y.: Modeling and evaluating information leakage caused by inference in supply chains. Computers in Industry 62(3), 351–363 (2011)CrossRefGoogle Scholar
  5. 5.
    Lindamood, J., Heatherly, R., Kantarcioglu, M., Thuraisingham, B.: Inferring private information using social network data. In: Proceedings of the 18th International Conference on World Wide Web, pp. 1145–1146 (2009)Google Scholar
  6. 6.
    Lucas, M.M., Borisov, N.: FlyByNight: Mitigating the Privacy Risks of Social Networking. In: Proceedings of the 7th ACM Workshop on Privacy in the Electronic Society, pp. 1–8 (2008)Google Scholar
  7. 7.
    Irani, D., webb, S., Pu, C., Li, K.: Modeling Unintended Personal-Information Leakage from Multiple Online Social Networks. IEEE Internet Computing 15(3), 13–19 (2011)CrossRefGoogle Scholar
  8. 8.
    Lam, I.F., Chen, K.T., Chen, L.J.: Involuntary Information Leakage in Social Network Services. In: Proceedings of the 3rd International Workshop on Security: Advanced in Information and Computer Security, pp. 167–183 (2008)Google Scholar
  9. 9.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a Social Network or a News Media. In: 19th International Conference on World Wide Web, pp. 591–600 (2010)Google Scholar
  10. 10.
    Java, A., Song, X., Finin, T., Tseng, B.: Why We Twitter: Understanding Microblogging Usage and Communities. In: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, pp. 56–65 (2007)Google Scholar
  11. 11.
    Yang, J., Leskovec, J.: Patterns of Temporal Variation in Online Media. In: ACM International Conference on Web Search and Data Mining, pp. 177–186 (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Dongjin Choi
    • 1
  • Ilsun You
    • 2
  • Pankoo Kim
    • 1
  1. 1.Dept. of Computer EngineeringChosun UniversityGwangjuRepublic of Korea
  2. 2.Korean Bible UniversitySeoulRepublic of Korea

Personalised recommendations