Science and Engineering Ethics

, Volume 20, Issue 4, pp 1027–1043 | Cite as

Ethical Considerations when Employing Fake Identities in Online Social Networks for Research

  • Yuval Elovici
  • Michael FireEmail author
  • Amir Herzberg
  • Haya Shulman
Original Paper


Online social networks (OSNs) have rapidly become a prominent and widely used service, offering a wealth of personal and sensitive information with significant security and privacy implications. Hence, OSNs are also an important—and popular—subject for research. To perform research based on real-life evidence, however, researchers may need to access OSN data, such as texts and files uploaded by users and connections among users. This raises significant ethical problems. Currently, there are no clear ethical guidelines, and researchers may end up (unintentionally) performing ethically questionable research, sometimes even when more ethical research alternatives exist. For example, several studies have employed “fake identities” to collect data from OSNs, but fake identities may be used for attacks and are considered a security issue. Is it legitimate to use fake identities for studying OSNs or for collecting OSN data for research? We present a taxonomy of the ethical challenges facing researchers of OSNs and compare different approaches. We demonstrate how ethical considerations have been taken into account in previous studies that used fake identities. In addition, several possible approaches are offered to reduce or avoid ethical misconducts. We hope this work will stimulate the development and use of ethical practices and methods in the research of online social networks.


Online social network Fake profile Ethics Data mining 


  1. Altshuler, Y., Pan, W., & Pentland, A. S. (2012). Trends prediction using social diffusion models. In Social computing, behavioral-cultural modeling and prediction (pp. 97–104). Springer.Google Scholar
  2. Athanasopoulos, E., Makridakis, A., Antonatos, S., Antoniades, D., Ioannidis, S., Anagnostakis, K., et al. (2008). Antisocial networks: Turning a social network into a botnet. Information security (pp. 146–160).Google Scholar
  3. BGU Social Networks Security Research Group. (2013). BGU social networks dataset collection.
  4. Boshmaf, Y., Muslukhov, I., Beznosov, K., & Ripeanu, M. (2011). The socialbot network: When bots socialize for fame and money. In Proceedings of the 27th annual computer security applications conference, ACM (pp. 93–102).Google Scholar
  5. CAIDA. (2012). Anonymized Internet Traces 2012 Dataset.
  6. Elishar, A., Fire, M., Kagan, D., & Elovici, Y. (2012). Organizational intrusion: Organization mining using socialbots. In Proceedings of ASE international conference on social informatics, Washington DC, USA, December.Google Scholar
  7. Eynon, R., Fry, J., & Schroeder, R. (2008). The ethics of internet research. In Handbook of online research methods (pp. 23–42). Sage, London.Google Scholar
  8. Eynon, R., Schroeder, R., & Fry, J. (2009). New techniques in online research: Challenges for research ethics. Twenty-First Century Society, 4(2), 187–199.CrossRefGoogle Scholar
  9. Eysenbach, G., & Till, J. E. (2001). Ethical issues in qualitative research on internet communities. BMJ, 323(7321), 1103–1105.CrossRefGoogle Scholar
  10. Facebook Inc. (2012). Quarterly report pursuant to section 13 or 15(d) of the securities exchange act of 1934.
  11. Fire, M., Tenenboim, L., Lesser, O., Puzis, R., Rokach, L., & Elovici, Y. (2011). Link prediction in social networks using computationally efficient topological features. In IEEE third international conference on privacy, security, risk and trust (PASSAT) and IEEE third international conference on social computing (SocialCom) (pp. 73–80). IEEE.Google Scholar
  12. Fire, M., Katz, G., & Elovici, Y. (2012). Strangers intrusion detection-detecting spammers and fake profiles in social networks based on topology anomalies. Human Journal, 1(1), 26–39.Google Scholar
  13. Fire, M., Kagan, D., Elishar, A., & Elovici, Y. (2012a). Social privacy protector—protecting users’ privacy in social networks. In SOTICS 2012, the second international conference on social eco-informatics (pp. 46–50).Google Scholar
  14. Fire, M., Kagan, D., Puzis, R., Rokach, L., & Elovici, Y. (2012b). Data mining opportunities in geosocial networks for improving road safety. In IEEE 27th convention of electrical and electronics engineers in Israel (IEEEI), 2012 (pp. 1–4). IEEE.Google Scholar
  15. Fire, M., Puzis, R., & Elovici, Y. (2013). Organization mining using online social networks. ArXiv preprint arXiv:13033741.Google Scholar
  16. Flicker, S., Haans, D., & Skinner, H. (2004). Ethical dilemmas in research on internet communities. Qualitative Health Research, 14(1), 124–134.CrossRefGoogle Scholar
  17. Herzberg, A., & Margulies, R. (2011). Forcing johnny to login safely—long-term user study of forcing and training login mechanisms. In V. Atluri, C. Díaz (Ed.), ESORICS, lecture notes in computer science (vol. 6879, pp. 452–471). Springer, BerlinGoogle Scholar
  18. Huber, M., Mulazzani, M., Weippl, E., Kitzler, G., & Goluch, S. (2011). Friend-in-the-middle attacks: Exploiting social networking sites for spam. Internet Computing, IEEE, 15(3), 28–34.CrossRefGoogle Scholar
  19. Huber, M., Mulazzani, M., Leithner, M., Schrittwieser, S., Wondracek, G., & Weippl, E. (2011a). Social snapshots: Digital forensics for online social networks. In Proceedings of the 27th annual computer security applications conference (pp. 113–122). ACM.Google Scholar
  20. Jernigan, C., & Mistree, B. F. (2009). Gaydar: Facebook friendships expose sexual orientation. First Monday, 14(10).Google Scholar
  21. Kontaxis, G., Polakis, I., Ioannidis, S., & Markatos E. P. (2011). Detecting social network profile cloning. In IEEE international conference on pervasive computing and communications workshops (PERCOM workshops) (pp. 295–300). IEEEGoogle Scholar
  22. Kunegis, J. (2013). KONECT—the Koblenz Network Collection.
  23. Kwak, H., Lee, C., Park, H., & Moon, S. (2010). What is twitter, a social network or a news media? In Proceedings of the 19th international conference on world wide web (pp. 591–600). ACM.Google Scholar
  24. Liu, Y., Gummadi, K. P., Krishnamurthy, B., & Mislove, A. (2011). Analyzing facebook privacy settings: User expectations vs. reality. In Proceedings of the 2011 ACM SIGCOMM conference on internet measurement conference (pp. 61–70). ACM.Google Scholar
  25. Lucas, R. (2012). Ethics and social eco-informatics. In SOTICS 2012, the second international conference on social eco-informatics (pp. 1–6).Google Scholar
  26. Margulies, R., & Herzberg, A. (2013). Conducting ethical yet realistic usable security studies. In Cyber-security research ethics dialog and strategy workshop (CREDS).Google Scholar
  27. Microsoft-Research. (2013). LETOR: Learning to rank for information retrieval.
  28. Mislove, A., Marcon, M., Gummadi, K. P., Druschel, P., & Bhattacharjee, B. (2007). Measurement and analysis of online social networks. In Proceedings of the 7th ACM SIGCOMM conference on Internet measurement (pp. 29–42). ACM.Google Scholar
  29. Mislove, A., Viswanath, B., Gummadi, K. P., & Druschel, P. (2010). You are who you know: Inferring user profiles in online social networks. In Proceedings of the third ACM international conference on web search and data mining (pp. 251–260). ACM.Google Scholar
  30. Narayanan, A., & Shmatikov, V. (2006). How to break anonymity of the netflix prize dataset. ArXiv preprint cs/0610105.Google Scholar
  31. Narayanan, A., Shi, E., & Rubinstein, B. I. (2011). Link prediction by de-anonymization: How we won the kaggle social network challenge. In The 2011 international joint conference on neural networks (IJCNN) (pp. 1825–1834). IEEE.Google Scholar
  32. Pontes, T., Vasconcelos, M. A., Almeida, J. M., Kumaraguru, P., & Almeida, V. (2012). We know where you live: Privacy characterization of foursquare behavior. In UbiComp (pp. 898–905).Google Scholar
  33. Rahman, M. S., Huang, T. K., Madhyastha, H. V., & Faloutsos, M. (2012). Efficient and scalable socware detection in online social networks. In Proceedings of the 21st USENIX conference on Security symposium, USENIX Association (pp. 32–32).Google Scholar
  34. Rosenberg, A. (2010). Virtual world research ethics and the private/public distinction. International Journal of Internet Research Ethics, 3(12), 23–36.Google Scholar
  35. Rydstedt, G., Bursztein, E., Boneh, D., & Jackson, C. (2010). Busting frame busting: a study of clickjacking vulnerabilities at popular sites. In IEEE Oakland Web 2.Google Scholar
  36. Stanford. (2013). Stanford large network dataset collection.
  37. Thelwall, M., & Stuart, D. (2006). Web crawling ethics revisited: Cost, privacy, and denial of service. Journal of the American Society for Information Science and Technology, 57(13), 1771–1779.CrossRefGoogle Scholar
  38. Wilkinson, D., & Thelwall, M. (2011). Researching personal information on the public web methods and ethics. Social Science Computer Review, 29(4), 387–401.CrossRefGoogle Scholar
  39. Zafarani, R., & Liu, H. (2009). Social computing data repository at asu., Arizona State University, School of Computing, Informatics and Decision Systems Engineering.
  40. Zimmer, M. (2010). But the data is already public: On the ethics of research in Facebook. Ethics and Information Technology, 12(4), 313–325.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Yuval Elovici
    • 1
  • Michael Fire
    • 1
    Email author
  • Amir Herzberg
    • 2
  • Haya Shulman
    • 3
  1. 1.Telekom Innovation Laboratories, Department of Information Systems EngineeringBen Gurion University of the NegevBeer-ShevaIsrael
  2. 2.Department of Computer ScienceBar Ilan UniversityRamat GanIsrael
  3. 3.Fachbereich InformatikTechnische Universität Darmstadt/EC-SPRIDEDarmstadtGermany

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