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Whispers in the dark: analysis of an anonymous social network

Published:05 November 2014Publication History

ABSTRACT

Social interactions and interpersonal communication has undergone significant changes in recent years. Increasing awareness of privacy issues and events such as the Snowden disclosures have led to the rapid growth of a new generation of anonymous social networks and messaging applications. By removing traditional concepts of strong identities and social links, these services encourage communication between strangers, and allow users to express themselves without fear of bullying or retaliation.

Despite millions of users and billions of monthly page views, there is little empirical analysis of how services like Whisper have changed the shape and content of social interactions. In this paper, we present results of the first large-scale empirical study of an anonymous social network, using a complete 3-month trace of the Whisper network covering 24 million whispers written by more than 1 million unique users. We seek to understand how anonymity and the lack of social links affect user behavior. We analyze Whisper from a number of perspectives, including the structure of user interactions in the absence of persistent social links, user engagement and network stickiness over time, and content moderation in a network with minimal user accountability. Finally, we identify and test an attack that exposes Whisper users to detailed location tracking. We have notified Whisper and they have taken steps to address the problem.

References

  1. ALMUHIMEDI, H.,WILSON, S., LIU, B., SADEH, N., AND ACQUISTI, A. Tweets are forever: a large-scale quantitative analysis of deleted tweets. In Proc. of CSCW (2013). Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. ANDREESEN, M. Public tweets. Twitter, March 2014.Google ScholarGoogle Scholar
  3. ASHBROOK, D., AND STARNER, T. Using gps to learn significant locations and predict movement across multiple users. Personal Ubiquitous Comput. 7, 5 (2003), 275--286. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. ASSOCIATED PRESS. Whispers, secrets and lies? anonymity apps rise. USA Today, March 2014.Google ScholarGoogle Scholar
  5. BACKSTROM, L., DWORK, C., AND KLEINBERG, J. Wherefore art thou r3579x?: anonymized social networks, hidden patterns, and structural steganography. In Proc. of WWW (2007). Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. BERNSTEIN, M. S., MONROY-HERNÁNDEZ, A., HARRY, D., ANDRÉ, P., PANOVICH, K., AND VARGAS, G. G. 4chan and/b: An analysis of anonymity and ephemerality in a large online community. In Proc. of ICWSM (2011).Google ScholarGoogle Scholar
  7. BLONDEL, V. D., GUILLAUME, J.-L., LAMBIOTTE, R., AND LEFEBVRE, E. Fast unfolding of communities in large networks. JSTAT 2008, 10 (2008).Google ScholarGoogle ScholarCross RefCross Ref
  8. CHA, M., HADDADI, H., BENVENUTO, F., AND GUMMADI, K. Measuring User Influence in Twitter: The Million Follower Fallacy. In Proc. of ICWSM (2010).Google ScholarGoogle Scholar
  9. CHANG, Y., TANG, L., INAGAKI, Y., AND LIU, Y.What is tumblr: A statistical overview and comparison. CoRR abs/1403.5206 (2014).Google ScholarGoogle Scholar
  10. CLAUSET, A., SHALIZI, C. R., AND NEWMAN, M. E. Power-law distributions in empirical data. SIAM review 51, 4 (2009), 661--703. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. GARCIA, D., MAVRODIEV, P., AND SCHWEITZER, F. Social resilience in online communities: The autopsy of friendster. In Proc. of COSN (2013). Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. GILBERT, E., BAKHSHI, S., CHANG, S., AND TERVEEN, L. "i need to try this!": A statistical overview of pinterest. In Proc. of CHI (2013). Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. GILBERT, E., AND KARAHALIOS, K. Predicting tie strength with social media. In Proc. of CHI (2009). Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. GONG, N. Z., XU, W., HUANG, L., MITTAL, P., STEFANOV, E., SEKAR, V., AND SONG, D. Evolution of social-attribute networks: measurements, modeling, and implications using google+. In Proc. of IMC (2012). Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. GONZALEZ, M. A., GOMEZ, J., LOPEZ-GUERRERO, M., RANGEL, V., AND OCA, M. M. GUIDE-gradient: A guiding algorithm for mobile nodes in wlan and ad-hoc networks. Wirel. Pers. Commun. 57, 4 (2011). Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. GROVE, J. V. Secrets and lies: Whisper and the return of the anonymous app. CNet News, January 2014.Google ScholarGoogle Scholar
  17. GUO, L., TAN, E., CHEN, S., ZHANG, X., AND ZHAO, Y. E. Analyzing patterns of user content generation in online social networks. In Proc. of KDD (2009). Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. GUYON, I., AND ELISSEEFF, A. An introduction to variable and feature selection. JMLR 3 (2003), 1157--1182. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. HALL, M., FRANK, E., HOLMES, G., PFAHRINGER, B., REUTEMANN, P., AND WITTEN, I. H. The weka data mining software: an update. SIGKDD Explor. Newsl. 11, 1 (2009). Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. HAN, D., ANDERSEN, D. G., KAMINSKY, M., PAPAGIANNAKI, K., AND SESHAN, S. Access point localization using local signal strength gradient. In Proc. of PAM (2009). Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. HOSSEINMARDI, H., HAN, R., LV, Q., MISHRA, S., AND GHASEMIANLANGROODI, A. Analyzing negative user behavior in a semi-anonymous social network. CoRR abs/1404.3839 (2014).Google ScholarGoogle Scholar
  22. JONES, J. J., SETTLE, J. E., BOND, R. M., FARISS, C. J., MARLOW, C., AND FOWLER, J. H. Inferring tie strength from online directed behavior. PLoS ONE 8, 1 (2013), e52168.Google ScholarGoogle Scholar
  23. KNUTTILA, L. User unknown: 4chan, anonymity and contingency. First Monday 16, 10 (2011).Google ScholarGoogle ScholarCross RefCross Ref
  24. KWAK, H., CHOI, Y., EOM, Y.-H., JEONG, H., AND MOON, S. Mining communities in networks: a solution for consistency and its evaluation. In Proc. of IMC (2009). Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. KWAK, H., LEE, C., PARK, H., AND MOON, S.What is Twitter, a social network or a news media? In Proc. of WWW (2010). Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. MISLOVE, A., VISWANATH, B., GUMMADI, K. P., AND DRUSCHEL, P. You are who you know: inferring user profiles in online social networks. In Proc. of WSDM (2010). Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. NARAYANAN, A., AND SHMATIKOV, V. Robust de-anonymization of large sparse datasets. In Proc. of IEEE S&P (2008). Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. NEWMAN, M. E. Modularity and community structure in networks. PNAS 103, 23 (2006), 8577--8582.Google ScholarGoogle ScholarCross RefCross Ref
  29. NEWMAN, M. E. J. Assortative mixing in networks. Physical Review Letters 89, 20 (2002), 208701.Google ScholarGoogle ScholarCross RefCross Ref
  30. PETROVIC, S., OSBORNE, M., AND LAVRENKO, V. I wish i didn't say that! analyzing and predicting deleted messages in twitter. CoRR abs/1305.3107 (2013).Google ScholarGoogle Scholar
  31. ROESNER, F., GILL, B. T., AND KOHNO, T. Sex, lies, or kittens' investigating the use of snapchat's self-destructing messages. In Proc. of FC (2014).Google ScholarGoogle Scholar
  32. SCHOENEBECK, S. Y. The secret life of online moms: Anonymity and disinhibition on youbemom.com. In Proc. of ICWSM (2013).Google ScholarGoogle Scholar
  33. STRAPPARAVA, C., AND VALITUTTI, A. Wordnet affect: an affective extension of wordnet. In Proc. of LREC (2004).Google ScholarGoogle Scholar
  34. STUTZMAN, F., GROSS, R., AND ACQUISTI, A. Silent listeners: The evolution of privacy and disclosure on facebook. Journal of Privacy and Confidentiality 4, 2 (2013).Google ScholarGoogle ScholarCross RefCross Ref
  35. SULER, J., AND PHILLIPS, W. L. The bad boys of cyberspace: Deviant behavior in a multimedia chat community. Cyberpsy., Behavior, and Soc. Networking 1, 3 (1998), 275--294.Google ScholarGoogle Scholar
  36. UGANDER, J., KARRER, B., BACKSTROM, L., AND MARLOW, C. The anatomy of the facebook social graph. CoRR abs/1111.4503 (2011).Google ScholarGoogle Scholar
  37. WAKITA, K., AND TSURUMI, T. Finding community structure in mega-scale social networks: {extended abstract}. In Proc. of WWW (2007). Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. WATTS, D. J., AND STROGATZ, S. Collective dynamics of "small-world" networks. Nature, 393 (1998), 440--442.Google ScholarGoogle ScholarCross RefCross Ref
  39. WILSON, C., BOE, B., SALA, A., PUTTASWAMY, K., AND ZHAO, B. User Interactions in Social Networks and Their Implications. In Proc. of EuroSys (2009). Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. WORTHAM, J. New social app has juicy posts, all anonymous. NY Times, March 2014.Google ScholarGoogle Scholar
  41. WORTHAM, J. Whatsapp deal bets on a few fewer "friends". NY Times, February 2014.Google ScholarGoogle Scholar
  42. XU, T., CHEN, Y., JIAO, L., ZHAO, B. Y., HUI, P., AND FU, X. Scaling microblogging services with divergent traffic demands. In Proc. of Middleware (2011). Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. ZHANG, Z., ZHOU, X., ZHANG, W., ZHANG, Y.,WANG, G., ZHAO, B. Y., AND ZHENG, H. I am the antenna: Accurate outdoor AP location using smartphones. In Proc. of MobiCom (2011). Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. ZHELEVA, E., AND GETOOR, L. To join or not to join: the illusion of privacy in social networks with mixed public and private user profiles. In Proc. of WWW (2009). Google ScholarGoogle ScholarDigital LibraryDigital Library

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          cover image ACM Conferences
          IMC '14: Proceedings of the 2014 Conference on Internet Measurement Conference
          November 2014
          524 pages
          ISBN:9781450332132
          DOI:10.1145/2663716

          Copyright © 2014 ACM

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          Publication History

          • Published: 5 November 2014

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