Exploring User Social Behavior in Mobile Social Applications

January 21, 2018 | Author: Anonymous | Category: Social Science, Psychology, Conformity
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Exploring User Social Behavior in Mobile Social Applications Konglin Zhu*, Pan Hui$, Yang Chen*, Xiaoming Fu*, Wenzhong Li+


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


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], …


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


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


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


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


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

Message statistics


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


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


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


Messages sent




Total message received




Unique messages received




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


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


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


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



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