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Investigating Personally Identifiable Information Posted on Twitter Before and After Disasters

  • Pezhman SheinidashtegolEmail author
  • Aibek Musaev
  • Travis Atkison
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11517)

Abstract

Through social media, users may consciously reveal information about their personality; however, they could also unintentionally disclose private information related to the locations of where they live/work, car license plates, signatures or even their identification documents. Any disclosed information can endanger an individual’s privacy, possibly resulting in burglaries or identity theft. To the best of our knowledge, this paper is the first to claim and demonstrate that people may reveal information when they are vulnerable and actively seek help; evidently, in the event of a natural disaster, people change their behavior and become more inclined to share their personal information. To examine this phenomenon, we investigate two hurricane events (Harvey and Maria) and one earthquake (Mexico City) using datasets obtained from Twitter. Our findings show a significant change in people’s behavioral pattern in the disaster areas, regarding tweeting images that contain Personally Identifiable Information (PII), before and after a disaster event.

Keywords

Social media Privacy Disasters Information disclosure Hurricane Earthquake 

References

  1. 1.
    EMarketer: number of social media users worldwide from 2010 to 2021 (in billions). Technical report (2017)Google Scholar
  2. 2.
    Siddula, M., Li, L., Li, Y.: An empirical study on the privacy preservation of online social networks. IEEE Access 6, 19912–19922 (2018)CrossRefGoogle Scholar
  3. 3.
    Yin, D., Shen, Y., Liu, C.: Attribute couplet attacks and privacy preservation in social networks. IEEE Access 5, 25295–25305 (2017)CrossRefGoogle Scholar
  4. 4.
    McCallister, E., Grance, T., Scarfone, K.A.: Guide to protecting the confidentiality of Personally Identifiable Information (PII) (2010)Google Scholar
  5. 5.
    Murphy, K.: Web Photos That Reveal Secrets. Like Where You Live, NYTimes (2010)Google Scholar
  6. 6.
    Brunty, J., Helenek, K.: Chapter 3 - investigative uses of social media. In: Brunty, J., Helenek, K. (eds.) Social Media Investigation for Law Enforcement, pp. 41–70. Anderson Publishing, Ltd. (2013)Google Scholar
  7. 7.
    US Bureau of Justice Statistics: Prevalence rate of violent crime in the United States from 2005 to 2016, by age. https://www.statista.com/statistics/424137/prevalence-rate-of-violent-crime-in-the-us-by-age/. Accessed 01 July 2018
  8. 8.
    Kryvasheyeu, Y., et al.: Rapid assessment of disaster damage using social media activity. Sci. Adv. 2(3), e1500779–e1500779 (2016)CrossRefGoogle Scholar
  9. 9.
    Boutell, M., Luo, J.: Beyond pixels: exploiting camera metadata for photo classification. Pattern Recogn. 38(6), 935–946 (2005)CrossRefGoogle Scholar
  10. 10.
    Faiz bin Jeffry, M.A., Mammi, H.K.: A study on image security in social media using digital watermarking with metadata. In: 2017 IEEE Conference on Application, Information and Network Security (AINS), pp. 118–123 (2017)Google Scholar
  11. 11.
    Pew Research Center: Who uses each social media platform. http://www.pewinternet.org/fact-sheet/social-media/. Accessed 01 July 2018
  12. 12.
    Mellon, J., Prosser, C.: Twitter and Facebook are not representative of the general population: political attitudes and demographics of British social media users. Res. Polit. 4(3), 205316801772000 (2017)CrossRefGoogle Scholar
  13. 13.
    Narayanan, A., Shmatikov, V.: Myths and fallacies of “personally identifiable information”. Commun. ACM 53(6), 24 (2010)CrossRefGoogle Scholar
  14. 14.
    Liang, K., Liu, J.K., Lu, R., Wong, D.S.: Privacy concerns for photo sharing in online social networks. IEEE Internet Comput. 19(2), 58–63 (2015)CrossRefGoogle Scholar
  15. 15.
    Peddinti, S.T., Ross, K.W., Cappos, J.: User anonymity on Twitter. IEEE Secur. Privacy 15(3), 84–87 (2017)CrossRefGoogle Scholar
  16. 16.
    Wang, P., He, W., Zhao, J.: A tale of three social networks: user activity comparisons across Facebook, Twitter, and foursquare. IEEE Internet Comput. 18(2), 10–15 (2014)CrossRefGoogle Scholar
  17. 17.
    Du, S., et al.: Modeling privacy leakage risks in large-scale social networks. IEEE Access 6, 17653–17665 (2018)CrossRefGoogle Scholar
  18. 18.
    Cai, Z., He, Z., Guan, X., Li, Y.: Collective data-sanitization for preventing sensitive information inference attacks in social networks. IEEE Trans. Dependable Secure Comput. 5971(c), 1 (2016)Google Scholar
  19. 19.
    Houston, J.B., et al.: Social media and disasters: a functional framework for social media use in disaster planning, response, and research. Disasters 39(1), 1–22 (2015)CrossRefGoogle Scholar
  20. 20.
    Knuth, D., Szymczak, H., Kuecuekbalaban, P., Schmidt, S.: Social media in emergencies how useful can they be. In: Proceedings of the 2016 3rd International Conference on Information and Communication Technologies for Disaster Management, ICT-DM 2016 (2017)Google Scholar
  21. 21.
    Honan: Watch the Virginia earthquake spread across Twitter (2014)Google Scholar
  22. 22.
    Palen, L., et al.: A vision for technology-mediated support for public participation & assistance in mass emergencies & disasters. In: Proceedings of the 2010 ACMBCS Visions of Computer Science Conference, pp. 1–12 (2010)Google Scholar
  23. 23.
    Liu, S.B., Palen, L., Sutton, J., Hughes, A.L., Vieweg, S.: In search of the bigger picture: the emergent role of on-line photo sharing in times of disaster. In: Proceedings of the 5th International ISCRAM Conference, vol. 8, no. May, pp. 140–149 (2008)Google Scholar
  24. 24.
    Hecht, B., Hong, L., Suh, B., Chi, E.H.: Tweets from Justin Bieber’s heart: the dynamics of the location field in user profiles. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 237–246. ACM (2011)Google Scholar
  25. 25.
    Mahmud, J., Nichols, J., Drews, C.: Where is this tweet from? Inferring home locations of Twitter users. In: Proceedings of the Sixth International AAAI Conference on Weblogs and Social Media (ICWSM 2012), pp. 511–514 (2012)Google Scholar
  26. 26.
    Twitter Inc: Historical PowerTrack. http://support.gnip.com/apis/historicalapi/. Accessed 01 July 2018
  27. 27.
  28. 28.
    USGS: Water resources of the united states-national water information system (NWIS) mapper. https://maps.waterdata.usgs.gov/mapper/index.html. Accessed 04 Apr 2019
  29. 29.
    Schmidt, S., Achenbach, J., Somashekhar, S.: Puerto Rico entirely without power as Hurricane Maria hammers island with devastating force (2017)Google Scholar
  30. 30.
    Frailing, K., Wood Harper, D.: School kids and oil rigs: two more pieces of the Post-Katrina Puzzle in New Orleans. Am. J. Econ. Sociol. 69(2), 717–735 (2010)CrossRefGoogle Scholar
  31. 31.
    Frailing, K., Harper, D.W.: The Sociology of Katrina: Perspectives on a Modern Catastrophe. Rowman & Littlefield, Lanham (2010)Google Scholar
  32. 32.
    Frailing, K., Harper, D.W.: Crime and Criminal Justice in Disaster, 2nd edn. Carolina Academic Press, Durham (2012)Google Scholar
  33. 33.
    Frailing, K., Harper, D.W., Serpas, R.: Changes and challenges in crime and criminal justice after disaster. Am. Behav. Sci. 59(10), 1278–1291 (2015)CrossRefGoogle Scholar
  34. 34.
    Twitter: Twitter media policy (2018). Accessed 01 July 2018Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Pezhman Sheinidashtegol
    • 1
    Email author
  • Aibek Musaev
    • 1
  • Travis Atkison
    • 1
  1. 1.The University of AlabamaTuscaloosaUSA

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