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Understanding User Behavior in Online Social Networks: A Survey

Understanding User Behavior in Online Social Networks: A Survey. Long Jin, University of California, San Diego Yang Chen, Duke University Tianyi Wang, Tsinghua University Communications Magazine, IEEE, 2013, 51(9 ) Presented By Tong Shensi 2 015.11.12. Introduction

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Understanding User Behavior in Online Social Networks: A Survey

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  1. Understanding User Behavior inOnline Social Networks: A Survey Long Jin, University of California, San Diego Yang Chen, Duke University Tianyi Wang, Tsinghua University Communications Magazine, IEEE, 2013, 51(9) PresentedByTongShensi 2015.11.12

  2. Introduction • Connectivity and Interaction • Traffic Activity • Mobile Social Behavior • Conclusions

  3. Introduction • Motivation • OnlineSocialNetworks(OSN)havedramaticallyexpanded • OSNuserbehaviorcoversvarioussocialactivitiesthatuserscandoonline • Friendshipcreation • Contentpublishing • Profilebrowsing • Messaging • Commenting

  4. Introduction • Motivation(cont.) • OSNuserbehaviorisimportant • Internetserviceproviders • OSNTrafficisgrowingquicklyandbecomingsignificant • Guidethemtodosomeinfrastructuralaction • OSNserviceproviders • Understandingcustomers’attitudetowarddifferentfunctions • Understandingusers’geographicdistributionandtrafficacitivity • OSNusers • Enhanceuserexperience • Blockingmalicioususers

  5. Introduction • Organization • Connectivityandinteraction • SocialgraphcanrepresentrelationshipbetweenusersinOSNs • HasbeenwidelyusedinOSNresearch • Trafficactivity • UnderstandthenetworkusageofOSNs • Mobilesocialbehavior • Enhancetheperformanceofmobilesocialapplicationsandsystems • Maliciousbehavior • Security&Privacy

  6. Introduction • Connectivity and Interaction • Traffic Activity • Mobile Social Behavior • Conclusions

  7. ConnectivityandInteraction • Motivation&Challenges • SocialgraphcanrepresentrelationshipsamongusersinOSNs • Types • Undirectedgraphs • Directedgraphs • Thehugesizeofsocialgraph • Samplingandcrawlingtechiques

  8. ConnectivityandInteraction • Solution&Discussion • UndirectedGraphModel • Everyuserisdenotedasanode • Friendshipbetweenanyuserpairisrepresentedbyanedge • Wilsonet al. foundthatuserstendtointeractwithonlyasmallsetoffriends • Onlyvisibleinteractionbetweentwousercreateanedge • Laterperformsbetter

  9. ConnectivityandInteraction • Solution&Discussion(cont.) • DirectedGraphModel • Jiang et al. studied latent graph • Renren tracks the most recent nine visitors to every users’ profile • A directed edge from A to B indicates A has visited B’s profile • Prevalent and frequent than visible intersection • Uncorrelated with the frequency of content updates or number of friends

  10. ConnectivityandInteraction • Solution&Discussion(cont.) • DirectedGraphModel(cont.) • Hwak et al. studied Twitter’s graph • A directed edge from A to B indicates A has subscribed to receive B’s latest news • Basic information overview of Twitter • Distribution of followers/followees • analyzes how the number of followers or followees affects the number of tweets

  11. ConnectivityandInteraction • Solution&Discussion(cont.) • Graph Sampling • A fast increase in the number of users • Make the size of social graphs larger and larger • Challenge performing any analysis with limited computation and storage capability • Graph sampling • Preserve the origin graph’s property • Breadth-First Sampling(BFS) • Random Walk(RW)

  12. ConnectivityandInteraction • Future Work • Dynamic feature • Much of existing work study in a relatively static way • Dynamic feature could deeply understand OSN’s user behavior • Like new users join OSNs, make new friends…

  13. Introduction • Connectivity and Interaction • Traffic Activity • Mobile Social Behavior • Conclusions

  14. Traffic Activity • Motivation & Challenge • Graph contains limited information • Can interpret how users use OSNs better • For ISP, they have strong incentive to get better understanding of how the traffic pattern between end users and OSN sites will evolve

  15. Traffic Activity • Solution & Discussion • Traffic Monitoring • Benevenuto et al. analysis user behavior based on detailed clickstream data • The frequency of accesing OSNs • Total time spent on OSNs • Session duration of OSNs • Silent or latent Interactions such as browsing account for more than 90 percent of user activity

  16. Traffic Activity • Solution & Discussion • Traffic Monitoring(cont.) • Schneider et al. also study clickstream data • But focus on ISPs aspect • Like which features account for most traffic bytes

  17. Traffic Activity • Solution & Discussion • Locality of Interest • Facebook is heavily dependent on centralized US data center • Slow response time & unnecessary traffic • Wittieet al. analysis these two problems • Partitioning & distribution • 79 percent faster and 91 percent less bandwidth

  18. Traffic Activity • Solution & Discussion • Navigation Characteristic • Dunn et al. try to understand the similarities and differences in the web sites users visit through OSNs vs. through search engines. • OSN visitors are less likely to navigate to external web sites • OSNs direct visitors to a narrower subset of the web than search engines

  19. Traffic Activity • Future Work • Most existing analysis are led by either academic groups or ISPs, without OSN service provider • Academic groups use extensive crawling to obtain data, which encounter many restrictions • ISPs can only get a partial view of the whole site • Envision that OSN providers can collaborate with researchers in order to understand user behavior in an insightful way

  20. Introduction • Connectivity and Interaction • Traffic Activity • Mobile Social Behavior • Conclusions

  21. Mobile Social Behavior • Motivation & Challenge • More and more OSN services have been expanded to mobile platforms • More mobile-centric functions have been integrated into OSNs • Understanding mobile social networks(MSN) user behavior is very helpful for the design and implementation of MSN systems

  22. Mobile Social Behavior • Solution & Discussion • Mobile Social Application • Calculates similarity score to recommend nearby friends • Geographical Prediction in OSN • Predict a users location according to his/her friends’ location • Friendship and Mobility in LBSN • Analysis the relationship between friendship and human movement

  23. Mobile Social Behavior • Future Work • There are several fundamental issues that require continuous exploration in the research related to user behavior in MSNs • Social data delivery and social applications in mobile environments rouse challenges in several layers of the Internet protocol stack

  24. Introduction • Connectivity and Interaction • Traffic Activity • Mobile Social Behavior • Conclusions

  25. Conclusion • Study user behavior in OSNs from four different perspectives • Connection and interaction • Traffic activity • Mobile social behavior • Malicious behavior • Will enhance the user experience from various aspects • We believe future research will generate more interesting research problem

  26. Q&A

  27. Thank you

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