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Cyber-Physical Social Networks

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Published:24 March 2017Publication History
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Abstract

In the offline world, getting to know new people is heavily influenced by people’s physical context, that is, their current geolocation. People meet in classes, bars, clubs, public transport, and so on. In contrast, first-generation online social networks such as Facebook or Google+ do not consider users’ context and thus mainly reflect real-world relationships (e.g., family, friends, colleagues). Location-based social networks, or second-generation social networks, such as Foursquare or Facebook Places, take the physical location of users into account to find new friends. However, with the increasing number and wide range of popular platforms and services on the Web, people spend a considerable time moving through the online worlds. In this article, we introduce cyber-physical social networks (CPSN) as the third generation of online social networks. Beside their physical locations, CPSN consider also users’ virtual locations for connecting to new friends. In a nutshell, we regard a web page as a place where people can meet and interact. The intuition is that a web page is a good indicator for a user’s current interest, likings, or information needs. Moreover, we link virtual and physical locations, allowing for users to socialize across the online and offline world. Our main contributions focus on the two fundamental tasks of creating meaningful virtual locations as well as creating meaningful links between virtual and physical locations, where “meaningful” depends on the application scenario. To this end, we present OneSpace, our prototypical implementation of a cyber-physical social network. OneSpace provides a live and social recommendation service for touristic venues (e.g., hotels, restaurants, attractions). It allows mobile users close to a venue and web users browsing online content about the venue to connect and interact in an ad hoc manner. Connecting users based on their shared virtual and physical locations gives way to a plethora of novel use cases for social computing, as we will illustrate. We evaluate our proposed methods for constructing and linking locations and present the results of a first user study investigating the potential impact of cyber-physical social networks.

References

  1. Hamed Abdelhaq, Christian Sengstock, and Michael Gertz. 2013. EvenTweet: Online localized event detection from twitter. Proc. VLDB Endow. 6, 12 (2013), 1326--1329. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Yong-Yeol Ahn, Seungyeop Han, Haewoon Kwak, Sue Moon, and Hawoong Jeong. 2007. Analysis of topological characteristics of huge online social networking services. In WWW’07. ACM, 835--844. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Saleema Amershi and Meredith Ringel Morris. 2008. CoSearch: A system for co-located collaborative web search. In SIGCHI’08. ACM, 1647--1656. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Sitaram Asur and Bernardo A. Huberman. 2010. Predicting the future with social media. In WI-IAT’10. IEEE, 492--499. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Lars Backstrom, Eric Sun, and Cameron Marlow. 2010. Find me if you can: Improving geographical prediction with social and spatial proximity. In WWW’10. ACM, 61--70. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Jie Bao, Yu Zheng, David Wilkie, and Mohamed Mokbel. 2015. Recommendations in location-based social networks: A survey. GeoInformatica 19, 3 (2015), 525--565. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Jingwen Bian, Yang Yang, and Tat-Seng Chua. 2014. Predicting trending messages and diffusion participants in microblogging network. In SIGIR’14. ACM, 537--546. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Robert M. Bond, Christopher J. Fariss, Jason J. Jones, Adam D. I. Kramer, Cameron Marlow, Jaime E. Settle, and James H. Fowler. 2012. A 61-million-person experiment in social influence and political mobilization. Nature 489, 7415 (2012), 295--298.Google ScholarGoogle Scholar
  9. Meeyoung Cha, Hamed Haddadi, Fabricio Benevenuto, and Krishna P. Gummadi. 2010. Measuring user influence in twitter: The million follower fallacy. In ICWSM’10, Vol. 10. AAAI Press, 10--17.Google ScholarGoogle Scholar
  10. Terence Chen, Mohamed Ali Kâafar, and Roksana Boreli. 2013. The where and when of finding new friends: Analysis of a location-based social discovery network. In ICWSM’13. AAAI Press.Google ScholarGoogle Scholar
  11. Eunjoon Cho, Seth A. Myers, and Jure Leskovec. 2011. Friendship and mobility: User movement in location-based social networks. In SIGKDD’11. ACM, 1082--1090. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. Max J. Egenhofer and Robert D. Franzosa. 1991. Point-set topological spatial relations. Int. J. Geogr. Inf. Syst. 5, 2 (1991), 161--174.Google ScholarGoogle ScholarCross RefCross Ref
  13. Ronen Feldman. 2013. Techniques and applications for sentiment analysis. Commun. ACM 56, 4 (2013), 82--89. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Sharad Goel, Jake M. Hofman, and M. Irmak Sirer. 2012. Who does what on the web: A large-scale study of browsing behavior. In ICWSM’12.Google ScholarGoogle Scholar
  15. Adrien Guille, Hakim Hacid, Cecile Favre, and Djamel A. Zighed. 2013. Information diffusion in online social networks: A survey. SIGMOD Rec. 42, 2 (2013), 17--28. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Keith N. Hampton, Lauren Sessions Goulet, Lee Rainie, and Kristen Purcell. 2011. Social Networking Sites and Our Lives. (2011). Pew Internet 8 American Life Project, May 6.Google ScholarGoogle Scholar
  17. Schoen Harald, Gayo-Avello Daniel, Takis Metaxas Panagiotis, Mustafaraj Eni, Strohmaier Markus, and Gloor Peter. 2013. The power of prediction with social media. Internet Res. 23, 5 (2013), 528--543.Google ScholarGoogle ScholarCross RefCross Ref
  18. Clayton J. Hutto and Eric Gilbert. 2014. VADER: A parsimonious rule-based model for sentiment analysis of social media text. In ICWSM’14.Google ScholarGoogle Scholar
  19. Jing Jiang, Christo Wilson, Xiao Wang, Wenpeng Sha, Peng Huang, Yafei Dai, and Ben Y. Zhao. 2013. Understanding latent interactions in online social networks. ACM Trans. Web 7, 4, Article 18 (2013), 18:1--18:39. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Long Jin, Yang Chen, Tianyi Wang, Pan Hui, and A. V. Vasilakos. 2013. Understanding user behavior in online social networks: A survey. IEEE Commun. Mag. 51, 9 (2013), 144--150.Google ScholarGoogle ScholarCross RefCross Ref
  21. Robert Krueger, Dennis Thom, and Thomas Ertl. 2014. Visual analysis of movement behavior using web data for context enrichment. In PacificVis’14. IEEE, 193--200. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. Jiří Kysela, Josef Horálek, and Filip Holík. 2015. Measuring information quality of geosocial networks. In New Trends in Intelligent Information and Database Systems. Springer, 171--180.Google ScholarGoogle Scholar
  23. Cliff Lampe, Nicole Ellison, and Charles Steinfield. 2006. A Face(Book) in the crowd: Social searching vs. social browsing. In CSCW’06. ACM, 167--170. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Gilly Leshed, Eben M. Haber, Tara Matthews, and Tessa Lau. 2008. CoScripter: Automating 8 sharing how-to knowledge in the enterprise. In CHI’08. ACM, 1719--1728. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. Bang Hui Lim, Dongyuan Lu, Tao Chen, and Min-Yen Kan. 2015. #Mytweet via instagram: Exploring user behaviour across multiple social networks. In ASONAM’15. ACM, 113--120. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Janne Lindqvist, Justin Cranshaw, Jason Wiese, Jason Hong, and John Zimmerman. 2011. I’m the mayor of my house: Examining why people use foursquare—a social-driven location sharing application. In CHI’11. ACM, 2409--2418. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Meredith Ringel Morris and Eric Horvitz. 2007. SearchTogether: An interface for collaborative web search. In UIST’07. 3--12. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Meredith Ringel Morris, Andreas Paepcke, and Terry Winograd. 2006. TeamSearch: Comparing techniques for co-present collaborative search of digital media. In TABLETOP’06. IEEE Computer Society, 97--104. Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Jacob Poushter, Jill Carle, James Bell, and Richard Wike. 2015. Internet Seen as Positive Influence on Education but Negative on Morality in Emerging and Developing Nations. Pew Internet 8 American Life Project, March 19.Google ScholarGoogle Scholar
  30. Daniel M. Romero, Wojciech Galuba, Sitaram Asur, and Bernardo A. Huberman. 2011. Influence and passivity in social media. In WWW’11 Companion. ACM, 113--114. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Salvatore Scellato, Anastasios Noulas, and Cecilia Mascolo. 2011. Exploiting place features in link prediction on location-based social networks. In SIGKDD’11. ACM, 487--501. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Sindy R. Sumter, Laura Vandenbosch, and Loes Ligtenberg. 2017. Love me tinder: Untangling emerging adults motivations for using the dating application tinder. Telemat. Inf. 34, 1 (2017), 67--78. Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. John Tang, Mirco Musolesi, Cecilia Mascolo, and Vito Latora. 2010. Characterising temporal distance and reachability in mobile and online social networks. SIGCOMM Comput. Commun. Rev. 40, 1 (2010), 118--124. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Jih-Hsin Tang, Ming-Chun Chen, Cheng-Ying Yang, Tsai-Yuan Chung, and Yao-An Lee. 2016. Personality traits, interpersonal relationships, online social support, and Facebook addiction. Telemat. Inf. 33, 1 (2016), 705--714. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Io Taxidou and Peter M. Fischer. 2014. Online analysis of information diffusion in twitter. In WWW’14 Companion. ACM, 1313--1318. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Christian von der Weth, Lekha Chaisorn, and Mohan Kankanhalli. 2015. Micro-Location Detection in Tweets. Technical Report. Interactive 8 Digital Media Institute, National University of Singapore.Google ScholarGoogle Scholar
  37. Christian von der Weth and Manfred Hauswirth. 2013. Finding information through integrated ad-hoc socializing in the virtual and physical world. In WI-IAT’13. IEEE, 37--44. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Christian von der Weth, Vinod Hedge, and Manfred Hauswirth. 2014. Virtual location-based services: Merging the physical and virtual world. In ICWS’14. IEEE, 113--120. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Dashun Wang, Dino Pedreschi, Chaoming Song, Fosca Giannotti, and Albert-Laszlo Barabasi. 2011. Human mobility, social ties, and link prediction. In SIGKDD’11. ACM, 1100--1108. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Heather Wiltse and Jeffrey Nichols. 2009. PlayByPlay: Collaborative web browsing for desktop and mobile devices. In CHI’09. ACM, New York, NY, 1781--1790. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Shaomei Wu, Jake M. Hofman, Winter A. Mason, and Duncan J. Watts. 2011. Who says what to whom on twitter. In WWW’11. ACM, 705--714. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Rongjing Xiang, Jennifer Neville, and Monica Rogati. 2010. Modeling relationship strength in online social networks. In WWW’10. ACM, 981--990. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Wei Xie, Feida Zhu, Jing Jiang, Ee-Peng Lim, and Ke Wang. 2013. TopicSketch: Real-time bursty topic detection from twitter. In ICDM’13. 837--846.Google ScholarGoogle Scholar
  44. Minhui Xue, Limin Yang, Keith W. Ross, and Haifeng Qian. 2016. Characterizing user behaviors in location-based find-and-flirt services: Anonymity and demographics. Peer-to-Peer Networking and Applications (2016), 1--11.Google ScholarGoogle Scholar

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  1. Cyber-Physical Social Networks

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        • Published in

          cover image ACM Transactions on Internet Technology
          ACM Transactions on Internet Technology  Volume 17, Issue 2
          Special Issue on Advances in Social Computing and Regular Papers
          May 2017
          249 pages
          ISSN:1533-5399
          EISSN:1557-6051
          DOI:10.1145/3068849
          • Editor:
          • Munindar P. Singh
          Issue’s Table of Contents

          Copyright © 2017 ACM

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          Publication History

          • Published: 24 March 2017
          • Accepted: 1 September 2016
          • Revised: 1 July 2016
          • Received: 1 February 2016
          Published in toit Volume 17, Issue 2

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