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 Mobile social application (MS app): Goose Experiment methodology User behavior analysis Information propagation in MS apps Conclusions

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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], …

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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 4

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

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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 6

User behavior analysis • User overall behavior – – – –

User activity User sessions User mobility Message statistics

• User social behavior – User encounters – User interactions

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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 8

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

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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

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

Message statistics

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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 UGoe

Encounters distribution in NJU 12

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

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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

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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 15

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 Status Unicast

Broadcast

Messages sent

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32

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Total message received

40

21

32

Unique messages received

20

21

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Messages sent vs. messages received 16

Delays of messages

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

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Delays of messages

Information propagation in MS apps (4) • Information epidemics[7] – Susceptible-InfectiousSusceptible • 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

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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

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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 20

Questions?

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