Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Online Interactions

  • Chapter
Social Phenomena

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

  • 1715 Accesses

Abstract

The ubiquitous use of the Internet has led to the emergence of countless social media and social networking platforms, which generate large-scale digital data records of human behaviors online. Here we review the literature on online interactions, focusing on two main themes: social link formation and online communication. The former is often studied in the context of network evolution models and link prediction or recommendation tasks; the latter combines classic social science theories on collective human behaviors with analysis of big data enabled by advanced computation techniques. But the structure of the network, and the flow of information through the network influence each other. We present a case study to illustrate the connections between social link formation and online communication. Analysis of longitudinal micro-blogging data reveals that people tend to follow others after seeing many messages by them. We believe that research on online interactions will benefit from a deeper understanding of the mutual interactions between the dynamics on the network (communication) and the dynamics of the network (evolution).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

eBook
USD 15.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Yahoo! Meme was a social micro-blogging system similar to Twitter, active between 2009 and 2012.

  2. 2.

    Lyapunov’s condition, \(\frac{1} {\sigma _{n}^{4}} \sum _{\ell=1}^{n}E[(X(\ell) - p(\ell))^{4}]\mathop{\longrightarrow }\limits^{n \rightarrow \infty }0\) where X(ℓ) is a random Bernoulli variable with success probability p(ℓ) [80], is consistent with numerical tests. Details are omitted for brevity.

References

  1. Cho, A. (2009). Ourselves and our interactions: The ultimate physics problem? Science, 325, 406.

    Article  ADS  Google Scholar 

  2. Kumar, R., Novak, J., & Tomkins, A. (2006). Structure and evolution of online social networks. In Proceedings of SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM.

    Google Scholar 

  3. Lazer, D., Pentland, A., Adamic, L., Aral, S., Barabási, A. -L., Brewer, D., et al. (2009). Computational social science. Science, 323(5915), 721–723.

    Article  Google Scholar 

  4. Vespignani, A. (2009). Predicting the behavior of techno-social systems. Science, 325(5939), 425–428.

    Article  MathSciNet  ADS  Google Scholar 

  5. Barabási, A. -L., & Albert, R. (2005). The origin of bursts and heavy tails in human dynamics. Nature, 435(7039), 207–211.

    Article  ADS  Google Scholar 

  6. Albert, R., & Barabási, A. -L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1), 47–97.

    Article  MathSciNet  ADS  Google Scholar 

  7. Barrat, A., Barthélemy, M., & Vespignani, A. (2008). Dynamical processes on complex networks. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  8. Newman, M. E. J. (2003). The structure and function of complex networks. SIAM Review, 45(2), 167–256.

    Article  MathSciNet  ADS  Google Scholar 

  9. Newman, M. E. J. (2010). Networks: An introduction. Oxford: Oxford University Press.

    Book  Google Scholar 

  10. Wasserman, S., & Faust, K. (1994). Social network analysis. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  11. Erdös, P., & Rényi, A. (1960). On the evolution of random graphs. Publications of the Mathematical Institute of the Hungarian Academy of Sciences, 5, 17–61.

    Google Scholar 

  12. Newman, M. E. J., Watts, D. J., & Strogatz, S. H. (2002). Random graph models of social networks. Proceedings of the National Academy of Sciences (PNAS), 99(Suppl 1), 2566–2572.

    Article  ADS  Google Scholar 

  13. Milgram, S. (1967). The small world problem. Psychology Today, 2(1), 60–67.

    MathSciNet  Google Scholar 

  14. Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442.

    Article  ADS  Google Scholar 

  15. Krackhardt, D., & Handcock, M. S. (2007). Heider vs. simmel: Emergent features in dynamic structure. In E. M. Airoldi, D. M. Blei, S. E. Fienberg, A. Goldenberg, E. P. Xing, A. X. Zheng (Eds.), Statistical network analysis: models, issues, and new directions (pp. 14–27). Berlin: Springer.

    Chapter  Google Scholar 

  16. Leskovec, J., Backstrom, L., Kumar, R., & Tomkins, A. (2008). Microscopic evolution of social networks. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (pp. 462–470).

    Google Scholar 

  17. Romero, D. M., & Kleinberg, J. (2010). The directed closure process in hybrid social-information networks, with an analysis of link formation on Twitter. In Proceedings of AAAI International Conference on Weblogs and Social Media (ICWSM).

    Google Scholar 

  18. Perra, N., Gonçalves, B., Pastor-Satorras, R., & Vespignani, A. (2012). Time scales and dynamical processes in activity driven networks. Nature Scientific Reports, 2, 469.

    ADS  Google Scholar 

  19. Rocha, L. E. C., Liljeros, F., & Holme, P. (2011). Simulated epidemics in an empirical spatiotemporal network of 50,185 sexual contacts. PLoS Computational Biology, 7(3), e1001109.

    Article  ADS  Google Scholar 

  20. Barbieri, N., Bonchi, F., & Manco, G. (2013). Cascade-based community detection. In Proceedings of ACM International Conference on Web Search and Data Mining (WSDM) (pp. 33–42).

    Google Scholar 

  21. Barabási, A. -L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.

    Article  MathSciNet  ADS  Google Scholar 

  22. Dorogovtsev, S., Mendes, J., & Samukhin, A. (2000). Structure of growing networks with preferential linking. Physical Review Letters, 85(21), 4633–4636.

    Article  ADS  Google Scholar 

  23. Fortunato, S., Flammini, A., & Menczer, F. (2006). Scale-free network growth by ranking. Physical Review Letters, 96(21), 218701.

    Article  ADS  Google Scholar 

  24. Kleinberg, J., Kumar, R., Raghavan, P., Rajagopalan, S., & Tomkins, A. (1999). The web as a graph: measurements, models and methods. Lecture Notes in Computer Science (LNCS), 1627, 1–18.

    Article  MathSciNet  Google Scholar 

  25. Kumar, R., Raghavan, P., Rajagopalan, S., Sivakumar, D., Tomkins, A., & Upfal, E. (2000). Stochastic models for the web graph. In Proceedings of IEEE Annual Symposium on Foundations of Computer Science (pp. 57–65).

    Google Scholar 

  26. Gallos, L., Rybski, D., Liljeros, F., Havlin, S., & Makse, H. (2012). How people interact in evolving online affiliation networks. Physical Review X, 2(3), 031014.

    Article  ADS  Google Scholar 

  27. McPherson, M., Lovin, L., & Cook, J. (2001). Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27(1), 415–444.

    Article  Google Scholar 

  28. Papadopoulos, F., Kitsak, M., Ángeles Serrano, M., Boguña, M., & Krioukov, D. (2012). Popularity versus similarity in growing networks. Nature, 489(7417), 537–540.

    Article  ADS  Google Scholar 

  29. Weng, L., & Lento, T. (2014). Topic-based clusters in egocentric networks on Facebook. In Proceedings of AAAI International Conference on Weblogs and Social Media (ICWSM).

    Google Scholar 

  30. Simmel, G., & Wolff, K. H. (1950). The Sociology of Georg Simmel. New York: The Free Press.

    Google Scholar 

  31. Granovetter, M. (1973). The strength of weak ties. American Journal of Sociology, 78(6), 1.

    Article  Google Scholar 

  32. Clauset, A., Moore, C., & Newman, M. (2008). Hierarchical structure and the prediction of missing links in networks. Nature, 453(1), 98–101.

    Article  ADS  Google Scholar 

  33. Weng, L., Ratkiewicz, J., Perra, N., Gonçalves, B., Castillo, C., Bonchi, F., et al. (2013). The role of information diffusion in the evolution of social networks. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (pp. 356–364).

    Google Scholar 

  34. Cowan, G. (1998). Statistical data analysis. Oxford: Oxford Science Publications.

    Google Scholar 

  35. Adamic, L. A., & Adar, E. (2003). Friends and neighbors on the web. Social Networks, 25(3), 211–230.

    Article  Google Scholar 

  36. Liben-Nowell, D., & Kleinberg, J. (2007). The link-prediction problem for social networks. Journal of American Society for Information Science and Technology (JASIST), 58(7), 1019–1031.

    Article  Google Scholar 

  37. Backstrom, L., & Leskovec, J. (2011). Supervised random walks: Predicting and recommending links in social networks. In Proceedings of ACM International Conference on Web Search and Data Mining (WSDM) (pp. 635–644). ACM.

    Google Scholar 

  38. Lou, T., Tang, J., Hopcroft, J., Fang, Z., & Ding, X. (2010). Learning to predict reciprocity and triadic closure in social networks. ACM Transactions on Embedded Computing Systems, 9(4), 5.

    Google Scholar 

  39. Schifanella, R., Barrat, A., Cattuto, C., Markines, B., & Menczer, F. (2010). Folks in folksonomies: social link prediction from shared metadata. In Proceedings of ACM International Conference on Web Search and Data Mining (WSDM) (pp. 271–280).

    Google Scholar 

  40. Anderson, R. M., May, R. M., & Anderson, B. (1992). Infectious diseases of humans: Dynamics and control (Vol. 28). Oxford: Oxford University Press.

    Google Scholar 

  41. Daley, D. J., & Kendall, D. G. (1964). Epidemics and rumours. Nature, 204(4963), 1118–1119.

    Article  ADS  Google Scholar 

  42. Goffman, W., & Newill, V. A. (1964). Generalization of epidemic theory: An application to the transmission of ideas. Nature, 204(4955), 225–228.

    Article  ADS  Google Scholar 

  43. Granovetter, M. S. (1978). Threshold models of collective behavior. American Journal of Sociology, 83(6), 1420–1433.

    Article  Google Scholar 

  44. Morris, S. (2000). Contagion. Review of Economic Studies, 67(1), 57–78.

    Article  MathSciNet  Google Scholar 

  45. Backstrom, L., Huttenlocher, D., Kleinberg, J., & Lan, X. (2006). Group formation in large social networks: Membership, growth, and evolution. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (pp. 44–54).

    Google Scholar 

  46. Bakshy, E., Karrer, B., & Adamic, L. (2009). Social influence and the diffusion of user-created content. In Proceedings of ACM Conference on Electronic Commerce (pp. 325–334).

    Google Scholar 

  47. Cosley, D., Huttenlocher, D., Kleinberg, J., Lan, X., & Suri, S. (2010). Sequential influence models in social networks. In Proceedings of AAAI International Conference on Weblogs and Social Media (ICWSM).

    Google Scholar 

  48. Romero, D. M., Meeder, B., & Kleinberg, J. (2011). Differences in the mechanics of information diffusion across topics: Idioms, political hashtags, and complex contagion on twitter. In Proceedings of International Conference on World Wide Web (WWW).

    Google Scholar 

  49. Centola, D. (2010). The spread of behavior in an online social network experiment. Science, 329(5996), 1194–1197.

    Article  ADS  Google Scholar 

  50. Weng, L., Menczer, F., & Ahn, Y. -Y. (2013). Virality prediction and community structure in social networks. Nature Scientific Reports, 3(2522).

    Google Scholar 

  51. Weng, L., Flammini, A., Vespignani, A., & Menczer, F. (2012). Competition among memes in a world with limited attention. Nature Scientific Reports, 2(335).

    Google Scholar 

  52. Kossinets, G., & Watts, D. J. (2009). Origins of homophily in an evolving social network1. American Journal of Sociology, 115(2), 405–450.

    Article  Google Scholar 

  53. McPherson, J. M., & Smith-Lovin, L. (1987). Homophily in voluntary organizations: Status distance and the composition of face-to-face groups. American Sociological Review, 52(3), 370–379.

    Article  Google Scholar 

  54. Aral, S., Muchnik, L., & Sundararajan, A. (2009). Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. Proceedings of the National Academy of Sciences (PNAS), 106(51), 21544–21549.

    Article  ADS  Google Scholar 

  55. Şimşek, O., & Jensen, D. (2008). Navigating networks by using homophily and degree. Proceedings of the National Academy of Sciences (PNAS), 105(35), 12758–12762.

    Article  ADS  Google Scholar 

  56. Crandall, D., Cosley, D., Huttenlocher, D., Kleinberg, J., & Suri, S. (2008). Feedback effects between similarity and social influence in online communities. In Proceedings of ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (pp. 160–168).

    Google Scholar 

  57. Jamieson, K. H., & Cappella, J. N. (2009). Echo chamber: Rush Limbaugh and the conservative media establishment. Oxford: Oxford University Press.

    Google Scholar 

  58. Conover, M., Ratkiewicz, J., Francisco, M., Gonçalves, B., Flammini, A., & Menczer, F. (2011). Political polarization on twitter. In Proceedings of 5th International AAAI Conference on Weblogs and Social Media (ICWSM).

    Google Scholar 

  59. Shalizi, C., & Thomas, A. (2011). Homophily and contagion are generically confounded in observational social network studies. Sociological Methods & Research, 40(2), 211–239.

    Article  MathSciNet  Google Scholar 

  60. Granovetter, M. (1995). Getting a job: A study of contacts and careers. Chicago: University of Chicago Press.

    Google Scholar 

  61. Bakshy, E., Rosenn, I., Marlow, C., & Adamic, L. (2012). The role of social networks in information diffusion. In Proceedings of ACM International World Wide Web Conference (WWW) (pp. 519–528).

    Google Scholar 

  62. Brown, J., & Reingen, P. (1987). Social ties and word-of-mouth referral behavior. Journal of Consumer Research, 14(3), 350–362.

    Article  Google Scholar 

  63. Friedkin, N. (1980). A test of structural features of granovetter’s strength of weak ties theory. Social Networks, 2(4), 411–422.

    Article  Google Scholar 

  64. Granovetter, M. (1983). The strength of weak ties: A network theory revisited. Sociological Theory, 1(1), 201–233.

    Article  Google Scholar 

  65. Levin, D. Z., & Cross, R. (2004). The strength of weak ties you can trust: The mediating role of trust in effective knowledge transfer. Management Science, 50(11), 1477–1490.

    Article  Google Scholar 

  66. Lin, N., Ensel, W. M., & Vaughn, J. C. (1981). Social resources and strength of ties: Structural factors in occupational status attainment. American Sociological Review, 46, 393–405.

    Article  Google Scholar 

  67. Nelson, R. E. (1989). The strength of strong ties: Social networks and intergroup conflict in organizations. Academy of Management Journal, 32(2), 377–401.

    Article  Google Scholar 

  68. Onnela, J. -P., Saramäki, J., Hyvönen, J., Szabó, G., Lazer, D., Kaski, K., et al. (2007). Structure and tie strengths in mobile communication networks. Proceedings of the National Academy of Sciences (PNAS), 104(18), 7332–7336.

    Article  ADS  Google Scholar 

  69. Gilbert, E., & Karahalios, K. (2009). Predicting tie strength with social media. In Proceedings of ACM International Conference on Human Factors in Computing Systems (CHI) (pp. 211–220).

    Google Scholar 

  70. Wellman, B., & Wortley, S. (1990). Different strokes from different folks: Community ties and social support. American Journal of Sociology, 96(3), 558–588.

    Article  Google Scholar 

  71. Bond, R. M., Fariss, C. J., Jones, J. J., Kramer, A. D., Marlow, C., Settle, J. E., et al. (2012). A 61-million-person experiment in social influence and political mobilization. Nature, 489(7415), 295–298.

    Article  ADS  Google Scholar 

  72. Putnam, R. D. (2001). Bowling alone: The collapse and revival of American community. New York: Simon and Schuster.

    Google Scholar 

  73. Dunbar, R. I. M. (1998). The social brain hypothesis. Evolutionary Anthropology, 9(10), 178–190.

    Article  Google Scholar 

  74. Huberman, B., Romero, D., & Wu, F. (2009). Social networks that matter: Twitter under the microscope. First Monday, 14(1), 8.

    Google Scholar 

  75. Gonçalves, B., Perra, N., & Vespignani, A. (2011). Modeling users’ activity on Twitter networks: Validation of Dunbar’s number. PLoS One, 6(8), e22656.

    Article  ADS  Google Scholar 

  76. Wu, F., & Huberman, B. A. (2007). Novelty and collective attention. Proceedings of the National Academy of Sciences (PNAS), 104(45), 17599–17601.

    Article  ADS  Google Scholar 

  77. Perra, N., Baronchelli, A., Mocanu, D., Gonçalves, B., Pastor-Satorras, R., & Vespignani, A. (2012). Random walks and search in time varying networks. Physical Review Letters, 109, 238701.

    Article  ADS  Google Scholar 

  78. Shaw, L. B., & Schwartz, I. B. (2010). Enhanced vaccine control of epidemics in adaptive networks. Physical Review E, 81, 046120.

    Article  ADS  Google Scholar 

  79. Volz, E., & Meyers, L. A. (2009). Epidemic thresholds in dynamic contact networks. Journal of the Royal Society Interface, 6, 233241.

    Article  Google Scholar 

  80. Billingsley, P. (1995). Probability and measure (p. 362). New York: Wiley.

    Google Scholar 

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

    Book  Google Scholar 

Download references

Acknowledgements

We thank Jacob Ratkiewicz, Nicola Perra, Bruno Gonçalves, Carlos Castillo, Francesco Bonchi, and Rossano Schifanella for their contributions to the case study presented in this chapter; Yahoo Labs for making the Meme data available; and the James S. McDonnell Foundation, National Science Foundation, and DARPA grant W911NF-12-1-0037 for partial support of this research.

Author information

Authors and Affiliations

  1. Center for Complex Networks and Systems Research, School of Informatics and Computing, Indiana University Bloomington, Bloomington, IN, USA

    Lilian Weng, Filippo Menczer & Alessandro Flammini

Authors
  1. Lilian Weng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Filippo Menczer
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alessandro Flammini
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Lilian Weng .

Editor information

Editors and Affiliations

  1. Centre de Physique Théorique, Aix-Marseille Université Campus de Luminy, Case 907, Marseille, France

    Bruno Gonçalves

  2. MoBS Lab, Northeastern University, Boston, Massachusetts, USA

    Nicola Perra

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Weng, L., Menczer, F., Flammini, A. (2015). Online Interactions. In: Gonçalves, B., Perra, N. (eds) Social Phenomena. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-14011-7_6

Download citation

Publish with us

Policies and ethics

Search

Navigation