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Exploring User Social Behavior in Mobile Social Applications

Exploring User Social Behavior in Mobile Social Applications. Konglin Zhu * , Pan Hui $ , Yang Chen * , Xiaoming Fu * , Wenzhong Li + * University of Goettingen, $ Deutsche Telekom, + Nanjing University. Outline. Background Motivation M obile social application (MS app): Goose

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Exploring User Social Behavior in Mobile Social Applications

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  1. Exploring User Social Behavior in Mobile Social Applications Konglin Zhu*, Pan Hui$, Yang Chen*, Xiaoming Fu*, Wenzhong Li+ *University of Goettingen, $Deutsche Telekom, +Nanjing University

  2. Outline • Background • Motivation • Mobile social application (MS app): Goose • Experiment methodology • User behavior analysis • Information propagation in MS apps • Conclusions

  3. Background • Mobile devices are increasing • 1.2 billion mobile phones are sold in 2009 • There are around 5 billion mobile phone subscriptions worldwide • Mobile social applications are popular • Mobile version of Facebook, Twitter • April 2010: 62% mobile users in Twitter • Facebook has 250 million mobile users • Location based mobile social applications • Foursquare • Non-Internet social applications • PeopleNet[1], Prism[2], Goose[3], …

  4. Motivations • Lack of knowledge on mobile social user behavior • Previous mobile devices deployment only investigates the user encounters, but no interactions • Information propagation in mobile social networks • Most previous information propagation models are simulation based, no real deployment • Our objectives: • Understand user behavior in mobile social applications • User overall behavior • User social behavior • Investigate information propagation in mobile social networks • DTN routing efficiency • Information epidemics

  5. Mobile social application: Goose • Goose • A mobile social application implemented on Nokia Symbian system • Function of Goose • Exchange contact • Exchange and update user profiles • Update status • Post new status on the Goose wall • Message • Unicast message via Bluetooth or SMS • Broadcast message via Bluetooth • Search friends • Search a specific friend from other friends‘ contact lists

  6. Experiment methodology • Deployment • We deploy our software in two campuses • 12 volunteers in University of Goettingen • 15 volunteers in Nanjing University • The experiments last 15 days in each campus • Data collection • Bluetooth MAC address • The time duration users run Goose • Cellular ID, nearby devices (every 2 minutes) • Incoming and outgoing events • Message ID, message type, time received, sender, previous relays, message size

  7. User behavior analysis • User overall behavior • User activity • User sessions • User mobility • Message statistics • User social behavior • User encounters • User interactions

  8. User overall behavior (1) • User activity • Active user is the user active at a certain time • It shows the periodicity bursts of active users (a) User activity in NJU (b) User activity in UGoe

  9. User overall behavior (2) • User sessions • A session is the time difference between switching on and switching off Goose • It reflects the frequency of using Goose (a) User sessions in NJU (a) User sessions in UGoe

  10. User overall behavior (3) • User mobility on campus • Trace a user’s mobility by recording cellular ID • A typical user’s time duration on each cellular User time duration in each cellular

  11. User overall behavior (4) • Message statistics • Communication messages are more than other messages • UGoe has more event types than NJU Message statistics

  12. User social behavior (1) • Heavy tail of User encounters • Heavy tail distribution[4] • It is known as scale-free network, it has been observed in many complex networks, such as Internet, WWW, email sending • The number of encounters in a day by each user Encounters distribution in NJU Encounters distribution in UGoe

  13. User social behavior (2) • Pareto principles of user interaction • Pareto principle • Known as 80-20 rule: 80% of the effects comes from 20% of causes • Both encounters and interactions show Pareto principle • More encounters suggest more interactions between users User interactions vs. user encounters

  14. User social behavior (2) • Pareto principles of user interaction • Pareto principle • Known as 80-20 rule: 80% of the effects comes from 20% of causes • Both encounters and interactions shows Pareto principle • More encounters suggests more interactions between users Pareto principle of user interactions User interactions vs. user encounters

  15. Information propagation in MS apps (1) • Small world phenomenon[5] • The distance between two people is within 6 hops • Most of messages are sent to destination within 6 hops Relays of messages

  16. Information propagation in MS apps (2) • DTN routing efficiency • Goose uses Bubble Rap[6] as the routing strategy for message forwarding • Forward the message based on the popularity of nodes • It shows the number of messages sent and received Messages sent vs. messages received Delays of messages

  17. Information propagation in MS apps (3) • Message delays • Varies from 0 minutes to 10,000 minutes • Unicast messages have shorter delay than broadcast and status updates Delays of messages

  18. Information propagation in MS apps (4) • Information epidemics[7] • Susceptible-Infectious-Susceptible • Each node can be: • Susceptible • Infectious • An infectious node can infect others with λ • We initialized an epidemic message in one device in UGoe • The infectious scale reach 50% in a short term, and 80% in the long run Information epidemics

  19. Conclusions • We study the user overall behavior and find that the user activity is similar as human work pattern • We explore the user social behavior in which the user encounters follows a heavy tail distribution and user interactions follows Pareto principle • We demonstrate the information propagation efficiency by DTN routing and information epidemics model in mobile social networks • We expect to extend the function of Goose and have a larger size of deployment

  20. References [1] M. Motani, V. Srinivasan, and P. Nuggehalli, PeopleNet: “Engineering a Wireless Virtual Social Network”. In Proc. of MobiCom, 2005 [2] T. Das, P. Mohan, V. N. Padmanabhan, R. Ramjee and A. Sharma, “PRISM: Platform for RemoteSensing using Mobile Smartphones”. MobiSys 2010. [3] N. V. Rodriguez, P. Hui and J. Crowcroft, “Has Anyone Seen My Goose? Social Network Services in Developing Regions”. CSE 2009 [4] A. L. Barabasi, “The Origin of Bursts and Heavy Tails in Human Dynamics”. Nature 2005. [5] D. J. Watts and S. H. Strogatz, “Collective Dynamics of Small-world Networks”, Nature 1998. [6] P. Hui, J. Crowcroft, and E. Yoneki. “Bubble rap: Social-based forwardingin delay tolerant networks”. MobiHoc 2008. [7] A. Chaintreau , P. Hui , J. Crowcroft , C. Diot , R. Gass and J. Scott, “Impact of human mobility on opportunistic forwarding algorithms”. IEEE Trans. Mob. Comp, 2007

  21. Questions? Thanks

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