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
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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
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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
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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
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User behavior analysis
• User overall behavior
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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
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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
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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 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
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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
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32
Unique messages
received
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Messages sent vs. messages received
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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
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Questions?
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