Characterizing user behavior in online social networks

ABSTRACT

Understanding how users behave when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we present a first of a kind analysis of user workloads in online social networks. Our study is based on detailed clickstream data, collected over a 12-day period, summarizing HTTP sessions of 37,024 users who accessed four popular social networks: Orkut, MySpace, Hi5, and LinkedIn. The data were collected from a social network aggregator website in Brazil, which enables users to connect to multiple social networks with a single authentication. Our analysis of the clickstream data reveals key features of the social network workloads, such as how frequently people connect to social networks and for how long, as well as the types and sequences of activities that users conduct on these sites. Additionally, we crawled the social network topology of Orkut, so that we could analyze user interaction data in light of the social graph. Our data analysis suggests insights into how users interact with friends in Orkut, such as how frequently users visit their friends' or non-immediate friends' pages. In summary, our analysis demonstrates the power of using clickstream data in identifying patterns in social network workloads and social interactions. Our analysis shows that browsing, which cannot be inferred from crawling publicly available data, accounts for 92% of all user activities. Consequently, compared to using only crawled data, considering silent interactions like browsing friends' pages increases the measured level of interaction among users.

References

1. Y.-Y. Ahn, S. Han, H. Kwak, S. Moon, and H. Jeong. Analysis of topological characteristics of huge online social networking services. In WWW, 2007.
2. B. A. Williamson. Social network marketing: ad spending and usage. EMarketer Report, 2007. http://tinyurl.com/2449xx.
3. M. Burke, C. Marlow, and T. Lento. Feed me: Motivating newcomer contribution in social network sites. In ACM CHI, 2009.
4. M. Cha, A. Mislove, B. Adams, and K. Gummadi. Characterizing Social Cascades in Flickr. In ACM SIGCOMM WOSN, 2008.
5. M. Cha, A. Mislove, and K. P. Gummadi. A measurement-driven analysis of information propagation in the Flickr social network. In WWW, 2009.
6. C. N. Chapman and M. Lahav. International ethnographic observation of social networking sites. In ACM CHI Extended Abstracts, 2008.
7. P. Chatterjee, D. L. Hoffman, and T. P. Novak. Modeling the clickstream: implications for web-based advertising efforts. Marketing Science, 2003.
8. H. Chun, H. Kwak, Y.-H. Eom, Y.-Y. Ahn, S. Moon, and H. Jeong. Online social networks: Sheer volume vs social interaction: a case study of Cyworld. In ACM IMC, 2008.
9. F. Duarte, B. Mattos, A. Bestavros, V. Almeida, and J. Almeida. Traffc characteristics and communication patterns in blogosphere. In AAAI ICWSM, 2007.
10. M. Gjoka, M. Sirivianos, A. Markopoulou, and X. Yang. Poking Facebook: characterization of OSN applications. In ACM SIGCOMM WOSN, 2008.
11. S. Golder, D. Wilkinson, and B. Huberman. Rhythms of social interaction: messaging within a massive online network. In ICCT, 2007.
12. Google OpenSocial. http://code.google.com/apis/opensocial/.
13. L. Guo, E. Tan, S. Chen, X. Zhang, and Y. (E.) Zhao. Analyzing patterns of user content generation in online social networks. In ACM SIGKDD, 2009.
14. B. Huberman, D. Romero, and F. Wu. Social networks that matter: Twitter under the microscope. First Monday, 2009.
15. B. A. Huberman, P. L. T. Pirolli, J. E. Pitkow, and R. M. Lukose. Strong regularities in world wide web surfing. Science, 1998.
16. B. Krishnamurthy. A measure of online social networks. In COMSNETS, 2009.
17. B. Krishnamurthy, P. Gill, and M. Arlitt. A few chirps about Twitter. In ACM SIGCOMM WOSN, 2008.
18. J. Leskovec, L. A. Adamic, and B. A. Huberman. The dynamics of viral marketing. ACM TWEB, 2007.
19. D. Liben-Nowell, J. Novak, R. Kumar, P. Raghavan, and A. Tomkins. Geographic routing in social network. PNAS, 2005.
20. MaxMind. GeoIP Database. http://www.maxmind.com/app/ip-location.
21. A. Mislove, M. Marcon, K. P. Gummadi, P. Druschel, and B. Bhattacharjee. Measurement and analysis of online social networks. In ACM IMC, 2007.
22. A. Nazir, S. Raza, and C.-N. Chuah. Unveiling Facebook: a measurement study of social network based applications. In ACM IMC, 2008.
23. Nielsen Online Report. Social networks&blogs now 4th most popular online activity, 2009. http://tinyurl.com/cfzjlt.
24. Orkut Help. http://www.google.com/support/orkut/.
25. R. King. When your social sites need networking, BusinessWeek, 2007. http://tinyurl.com/o4myvu.
26. P. Rodriguez. Web infrastructure for the 21st century. WWW'09 Keynote, 2009. http://tinyurl.com/mmmaa7.
27. S. Schroeder. 20 ways to aggregate your social networking profiles, Mashable, 2007. http://tinyurl.com/2ceus4.
28. N. Sastry, E. Yoneki, and J. Crowcroft. Buzztraq: predicting geographical access patterns of social cascades using social networks. In ACM EuroSys SNS Workshop, 2009.
29. M. Valafar, R. Rejaie, and W. Willinger. Beyond friendship graphs: a study of user interactions in Flickr. In ACM SIGCOMM WOSN, 2009.
30. B. Viswanath, A. Mislove, M. Cha, and K. P. Gummadi. On the evolution of user interaction in Facebook. In ACM SIGCOMM WOSN, 2009.
31. D. J. Watts and J. Peretti. Viral marketing for the real world. Harvard Business Review, 2007.
32. Wikipedia. Orkut. http://en.wikipedia.org/wiki/Orkut.
33. C. Wilson, B. Boe, A. Sala, K. P. N. Puttaswamy, and B. Y. Zhao. User interactions in social networks and their implications. In ACM EuroSys, 2009.

Index Terms

• General and reference

  • Cross-computing tools and techniques

    • Measurement

    • Metrics

• Information systems

  • World Wide Web

    • Web applications

    • Web services