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Mobile Social Networking pp 25–64Cite as

Ephemeral Social Networks

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Part of the book series: Computational Social Sciences ((CSS))

Abstract

Location-based mobile applications such as Foursquare help bridge the gap between offline and online. People that we encounter and connect with, around physical resources such as conferences, provide opportunities for extending our social networks from offline to online. We call these proximity-based networks that revolve around encounters and activities ephemeral social networks because they are created at a specific point in time for a specific duration at a specific event. Ephemeral social networking is the next evolution of mobile social networking, which aims to help us to connect with and recall the people we meet in our daily lives. However, there are many questions that need to be answered. What are the characteristics of ephemeral social networks? How to record and identify an ephemeral social network? In this chapter, we explain the theory behind ephemeral social networks, and create a platform called Find & Connect to show its properties. We then describe our application and system for connecting offline to online, then finally study the influence on user behavior of offline on online and vice versa by deploying Find & Connect at three conference events.

This work was done while the author was at Nokia Research Center

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References

  • Atzmueller, M., Benz, D., Doerfel, S., Hotho, A., Jaschke, R., Macek, B. E., et al. (2011). Enhancing social interactions at conferences. Information Technology, 53(3), 101–107.

    Google Scholar 

  • Backstrom, L., Huttenlocher, D., Kleinberg, J., & Lan, X. (2006). Group formation in large social networks: membership, growth, and evolution. In Proceedings of ACM SIGKDD, ACM (pp. 44–54). New York.

    Google Scholar 

  • Barrat, A., Cattuto, C., Szomszor, M., Van den Broeck, W., & Alami, H. (2010). Social dynamics in conferences: Analyses of data from the live social semantics application. In Proceedings of the semantic Web workshop-ISWC2010 (pp. 17–33). Shanghai, China.

    Google Scholar 

  • Bush, V. (1949). As we may think. SIGPC Note, 1(4), 36–44.

    Article  Google Scholar 

  • Cacioppo, J. T., Fowler, J. H., & Christakis, N. A. (2009). Alone in the crowd: The structure and spread of loneliness in a large social network. Journal of Personality and Social Psychology, 97(6), 977.

    Article  Google Scholar 

  • Cattuto, C., Van Den Broeck, W., Barrat, A., Colizza, V., Pinton, J. F., & Vespignani, A. (2010). Dynamics of person-to-person interactions from distributed RFID sensor networks. PLoS One, 5(7), e11596.

    Article  ADS  Google Scholar 

  • Chang, L., Chin, A., Wang, H., Zhu, L., Zhang, K., Yin, F., Wang, H., & Zhang, L. (2011). Enhancing the experience and efficiency at a conference with mobile social networking: Case study with Find & Connect. In Proceedings of HumanCom 2011. Lecture notes in electrical engineering, Vol. 102, (pp. 1–12). Enshi, China: Springer.

    Google Scholar 

  • Chin, A., Xu, B., Hong, D., Wang, Y., Yin, F., Wang, X., Wang, W., & Fan, X. (2012). Using proximity and homophily to connect conference attendees in a mobile social network. In Proceedings of the IEEE ICDCS’12 international workshop on PhoneCom 2012 (pp. 1–8). Macau, China: IEEE Press.

    Google Scholar 

  • Choudhury, T., Borriello, G., Consolvo, S., Haehnel, D., Harrison, B., Hemingway, B., et al. (2008). The mobile sensing platform: An embedded activity recognition system. IEEE Pervasive Computing, 7(2), 32–41.

    Article  Google Scholar 

  • Cox, D., Kindratenko, V., & Pointer, D. (2003). IntelliBadge: Towards providing location-aware value-added services at academic conferences. In Proceedings of ACM UbiComp (pp. 264–280). Seattle, USA: ACM.

    Google Scholar 

  • de Moraes, L. F. M., & Nunes, B. A. A. (2006). Calibration-free WLAN location system based on dynamic mapping of signal strength. In Proceedings of the 4th ACM international workshop on Mobility management and wireless access (pp. 92–99). New York: ACM.

    Google Scholar 

  • Dey, A. K. (2001). Understanding and using context. Personal Ubiquitous Computing, 5(1), 4–7.

    Article  Google Scholar 

  • Dey, A. K., Abowd, G. D., & Salber, D. (2001). A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Human–Computer Interaction, 16, 2–4. Taylor & Francis.

    Article  Google Scholar 

  • Eagle, N., & Pentland, A. (2009). Inferring friendship network structure by using mobile phone data. Proceedings of the National Academy of Sciences, 106(36), 15274–15278.

    Article  ADS  Google Scholar 

  • Easley, D., & Kleinberg, J. (2010). Networks, crowds, and markets: Reasoning about a highly connected world. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Ekahau. (2011). Ekahau real-time location system. http://www.ekahau.com/products/real-time-location-system/overview.html. Accessed 6 Nov 2012.

  • Henricksen, K., Indulska, J., & Rakotonirainy, A. (2002). Modeling context information in pervasive computing systems. Pervasive Computing Lecture Notes in Computer Science, 2414, 167–180. Springer.

    Article  Google Scholar 

  • Huang, S., Proulx, F., & Ratti, C. (2007). iFIND: a peer-to-peer application for real-time location monitoring on the MIT campus. In Proceedings of CUPUM. Brazil.

    Google Scholar 

  • Kermarrec, A. M., & Le Merrer, E. (2012). Offline social networks: stepping away from the Internet. In Proceedings of the Fifth Workshop on Social Network Systems (SNS ’12), 14(2), New York: ACM.

    Google Scholar 

  • Kostakos, V., & O’Neill, E. (2008). Capturing and visualising Bluetooth encounters. In Adjunct proceedings of CHI 2008, workshop on social data analysis (pp. 1–4). Florence, Italy: ACM.

    Google Scholar 

  • Lane, N. D. (2012). Community-aware smartphone sensing systems. IEEE Internet Computing, 16(3), 60–64.

    Article  Google Scholar 

  • Lyu, Y., Hong, D., Wang, Y., Hou, Y., Yang, Z., Chen, Y., Shi, Y., & Chin, A, (2013). A scalable and privacy-aware location-sensing model for ephemeral social network service. In International Journal of Distributed Sensor Networks (11 pages). Hindawi

    Google Scholar 

  • Mcpherson, M., Smith-Lovin, L., & Cook, J. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27, 415–444.

    Article  Google Scholar 

  • Mislove, A., Marcon, M., Gummadi, KP., Druschel, P., & Bhattacharjee, B. (2007). Measurement and analysis of online social networks. In Proc. of the 7th ACM SIGCOMM conference on Internet measurement (IMC ’07). (pp. 29–42). New York: ACM. doi:10.1145/1298306.1298311

  • Ni, L., Liu, M. Y., Lau, Y. C., & Patil, A. P. (2004). LANDMARC: Indoor location sensing using active RFID. Wireless Network. (pp. 701–710). Netherlands.

    Google Scholar 

  • Nokia. (2012). Mobile data challenge. http://research.nokia.com/page/12000. Accessed 6 Nov 2012.

  • Paulos, E., & Goodman, E. (2004). The familiar stranger: Anxiety, comfort, and play in public places. In Proceedings of CHI 2004 (pp. 223–230). New York: ACM.

    Google Scholar 

  • Vannoy, S. A., & Palvia, P. (2010). The social influence model of technology adoption. Communications of the ACM, 53, 149–153.

    Article  Google Scholar 

  • Wang, B., Bodily, J., & Gupta, S. K. S. (2004). Supporting persistent social groups in ubiquitous computing environments using context-aware ephemeral group service. In Proceedings of PERCOM 2004 (pp. 287–296). IEEE Computer Society.

    Google Scholar 

  • Wang, H., Chin, A., & Wang, H. (2011). Interplay between social selection and social influence on physical proximity in friendship formation. In SRS 2011 workshop in conjunction with CSCW 2011.Hangzhou, China.

    Google Scholar 

  • Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Xu, B., Chin, A., Wang, H., Chang, L., Zhang, K., Yin, F., Wang, H., & Zhang, L., (2011). Physical proximity and online user behavior in an indoor mobile social networking application. In Proceedings of IEEE CPSCom 2011 (pp. 273–282). Dalian, China.

    Google Scholar 

  • Xu, B., Chin, A., Wang, H., & Wang, H. (2011). Using physical context in a mobile social networking application for improving friend recommendations. In Proceedings of the PhoneCom 2011 workshop, IEEE CPSCom 2011 (pp. 602–609). Dalian, China.

    Google Scholar 

  • Zhu, L., Chin, A., Zhang, K., Xu, W., Wang, H., & Zhang, L. (2010). Managing workplace resources in office environments through ephemeral social networks. In Proceedings of UIC 2010 (pp. 665–679). Berlin: Springer-Verlag.

    Google Scholar 

  • Zhuang, H., Chin, A., Wu, S., Wang, W., Wang, X., & Tang, J. (2012). Inferring geographic coincidence in ephemeral social networks. Machine Learning and Knowledge Discovery in Databases: Lecture Notes in Computer Science (Proceeding European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD)) 7524, (pp. 613–628). Bristol, UK.

    Google Scholar 

  • Zuo, X., Chin, A., Xu, B., Hong, D., Wang, Y., Fan, X., & Wang, X. (2012). Connecting people from offline to online in a mobile social application. In Proceedings of the IEEE international conference on cyber, physical and social computing (CPSCom’12) (pp. 277–284). Besancon, France.

    Google Scholar 

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Acknowledgments

The author is grateful to the following people who contributed to this research: Xiang Zuo, Bin Xu, Chen Zhao, Xiaoguang Fan, Liang Wu, Xia Wang, Hao Wang, Hao Wang, Yongqiang Lyu, Dezhi Hong, Ying Wang, Lele Chang, Guwei Xu, and Lijun Zhu. The author also would like to acknowledge the engineers of the Find & Connect project: Fangxi Yin, Ke Zhang, Minggang Wang, Yilu Li, and Guanshang Wu. We would also like to thank all the participants who used Find & Connect in the trials and provided valuable feedback, as well as the financial and technical resources of Nokia.

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Authors and Affiliations

  1. Xpress Internet Services China, Nokia, Beijing, 100176, China

    Alvin Chin

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  1. Alvin Chin
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Correspondence to Alvin Chin .

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Editors and Affiliations

  1. Nokia, Beijing, China, People’s Republic

    Alvin Chin

  2. Institut Mines-Telecom/Telecom SudParis, Evry Cedex, France

    Daqing Zhang

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Chin, A. (2014). Ephemeral Social Networks. In: Chin, A., Zhang, D. (eds) Mobile Social Networking. Computational Social Sciences. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8579-7_3

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  • DOI: https://doi.org/10.1007/978-1-4614-8579-7_3

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  • Print ISBN: 978-1-4614-8578-0

  • Online ISBN: 978-1-4614-8579-7

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