Advertisement

Attention on Weak Ties in Social and Communication Networks

  • Lilian Weng
  • Márton Karsai
  • Nicola Perra
  • Filippo Menczer
  • Alessandro Flammini
Chapter
Part of the Computational Social Sciences book series (CSS)

Abstract

Granovetter’s weak tie theory of social networks is built around two central hypotheses. The first states that strong social ties carry the large majority of interaction events; the second maintains that weak social ties, although less active, are often relevant for the exchange of especially important information (e.g., about potential new jobs in Granovetter’s work). While several empirical studies have provided support for the first hypothesis, the second has been the object of far less scrutiny. A possible reason is that it involves notions relative to the nature and importance of the information that are hard to quantify and measure, especially in large scale studies. Here, we search for empirical validation of both Granovetter’s hypotheses. We find clear empirical support for the first. We also provide empirical evidence and a quantitative interpretation for the second. We show that attention, measured as the fraction of interactions devoted to a particular social connection, is high on weak ties—possibly reflecting the postulated informational purposes of such ties—but also on very strong ties. Data from online social media and mobile communication reveal network-dependent mixtures of these two effects on the basis of a platform’s typical usage. Our results establish a clear relationships between attention, importance, and strength of social links, and could lead to improved algorithms to prioritize social media content.

Notes

Acknowledgements

We would like to thank Albert-László Barabási for the mobile phone cell dataset used in this research, Twitter for providing public streaming data, and the Enron Email Analysis Project at UC Berkeley for cleaning up and sharing the Enron email dataset. MK acknowledges support from LABEX MiLyon. This work was partially funded by NSF grant CCF-1101743 and the James S. McDonnell Foundation.

References

  1. 1.
    Weng L, Ratkiewicz J, Perra N, Gonçalves B, Castillo C, Bonchi F, Schifanella R, Menczer F, Flammini A (2013). The role of information diffusion in the evolution of social networks. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 356–364Google Scholar
  2. 2.
    Dunbar RIM (1998) The social brain hypothesis. Evol Anthropol 9(10):178–190CrossRefGoogle Scholar
  3. 3.
    Gonçalves B, Perra N, Vespignani A (2011) Modeling users’ activity on twitter networks: validation of Dunbar’s number. PLoS One 6(8):e22656ADSCrossRefGoogle Scholar
  4. 4.
    Backstrom L, Bakshy E, Kleinberg J, Lento T, Rosenn I (2011) Center of attention: how facebook users allocate attention across friends. In: Proceedings of the AAAI international conference on weblogs and social media (ICWSM), pp 1–8Google Scholar
  5. 5.
    Weng L, Flammini A, Vespignani A, Menczer F (2012) Competition among memes in a world with limited attention. Nat Sci Rep 2:335Google Scholar
  6. 6.
    Hodas NO, Lerman K (2012) How visibility and divided attention constrain social contagion. In: Proceedings of the ASE/IEEE international conference on social computing, p 249–257Google Scholar
  7. 7.
    Simon H (1971) Designing organizations for an information-rich world. In: Greenberger M (ed) Computers, communication, and the public interest, vol 72. The Johns Hopkins Press, Baltimore, pp 37–52Google Scholar
  8. 8.
    Davenport TH, Beck JC (2001) The attention economy: understanding the new currency of business. Harvard Business School Press, BostonGoogle Scholar
  9. 9.
    Granovetter M (1973) The strength of weak ties. Am J Sociol 78(6):1CrossRefGoogle Scholar
  10. 10.
    Granovetter M (1995) Getting a job: a study of contacts and careers. University of Chicago Press, ChicagoGoogle Scholar
  11. 11.
    Brown J, Reingen P (1987) Social ties and word-of-mouth referral behavior. J Consum Res 14(3):350–362CrossRefGoogle Scholar
  12. 12.
    Levin DZ, Cross R (2004) The strength of weak ties you can trust: the mediating role of trust in effective knowledge transfer. Manag Sci 50(11):1477–1490CrossRefGoogle Scholar
  13. 13.
    Onnela J-P, Saramäki J, Hyvönen J, Szabó G, Lazer D, Kaski K, Kertész J, Barabási A-L (2007) Structure and tie strengths in mobile communication networks. Proc Natl Acad Sci (PNAS) 104(18):7332–7336ADSCrossRefGoogle Scholar
  14. 14.
    Gilbert E, Karahalios K (2009) Predicting tie strength with social media. In: Proceedings of the ACM international conference on human factors in computing systems (CHI), pp 211–220Google Scholar
  15. 15.
    Bakshy E, Rosenn I, Marlow C, Adamic L (2012) The role of social networks in information diffusion. In: Proceedings of the ACM international conference world wide web (WWW), pp 519–528Google Scholar
  16. 16.
    Friedkin N (1980) A test of structural features of granovetter’s strength of weak ties theory. Soc Netw 2(4):411–422CrossRefGoogle Scholar
  17. 17.
    Lin N, Ensel WM, Vaughn JC (1981) Social resources and strength of ties: structural factors in occupational status attainment. Am Sociol Rev 46:393–405CrossRefGoogle Scholar
  18. 18.
    Granovetter M (1983) The strength of weak ties: a network theory revisited. Sociol Theory 1(1):201–233CrossRefGoogle Scholar
  19. 19.
    Nelson RE (1989) The strength of strong ties: social networks and intergroup conflict in organizations. Acad Manag J 32(2):377–401Google Scholar
  20. 20.
    Haythornthwaite C, Wellman B (1998) Work, friendship, and media use for information exchange in a networked organization. J Am Soc Inf Sci 49(12):1101–1114CrossRefGoogle Scholar
  21. 21.
    Wellman B, Wortley S (1990) Different strokes from different folks: community ties and social support. Am J Sociol 96(3):558–588CrossRefGoogle Scholar
  22. 22.
    Bond RM, Fariss CJ, Jones JJ, Kramer ADI, Marlow C, Settle JE, Fowler JH (2012) A 61-million-person experiment in social influence and political mobilization. Nature 489(7415):295–298ADSCrossRefGoogle Scholar
  23. 23.
    Putnam RD (2001) Bowling alone: the collapse and revival of American community. Simon and Schuster, New YorkGoogle Scholar
  24. 24.
    Burt RS (2009) Structural holes: the social structure of competition. Harvard University Press, BostonGoogle Scholar
  25. 25.
    Lazer D, Pentland A, Adamic L, Aral S, Barabási A-L, Brewer D, Christakis N, Contractor N, Fowler J, Gutmann M, Jebara T, King G, Macy M, Roy D, Alstyne MV (2009) Computational social science. Science 323(5915):721–723CrossRefGoogle Scholar
  26. 26.
    Vespignani A (2009) Predicting the behavior of techno-social systems. Science 325(5939):425–428ADSMathSciNetCrossRefGoogle Scholar
  27. 27.
    Meo PD, Ferrara E, Fiumara G, Provetti A (2014) On facebook, most ties are weak. Commun. ACM 57(11):78–84CrossRefGoogle Scholar
  28. 28.
    Karsai M, Kivelä M, Pan RK, Kaski K, Kertész J, Barabási A-L, Saramäki J (2011) Small but slow world: how network topology and burstiness slow down spreading. Phys Rev E 83(2):025102ADSCrossRefGoogle Scholar
  29. 29.
    Miritello G, Moro E, Lara R (2011) Dynamical strength of social ties in information spreading. Phys Rev E 83(4):045102ADSCrossRefGoogle Scholar
  30. 30.
    Karsai M, Perra N, Vespignani A (2014) Time varying networks and the weakness of strong ties. Sci Rep 4:4001ADSCrossRefGoogle Scholar
  31. 31.
    Ubaldi E, Perra N, Karsai M, Vezzani A, Burioni R, Vespignani A (2016) Asymptotic theory of time-varying social networks with heterogeneous activity and tie allocation. Sci Rep 6:35724ADSCrossRefGoogle Scholar
  32. 32.
    Sun K, Baronchelli A, Perra N (2015) Contrasting effects of strong ties on sir and sis processes in temporal networks. Eur Phys J B 88(12):1–8MathSciNetGoogle Scholar
  33. 33.
    Klimt B, Yang Y (2004) The Enron corpus: a new dataset for email classification research. In: Proceedings of the European conference on machine learning (ECML), pp 217–226CrossRefGoogle Scholar
  34. 34.
    Miritello G, Moro E, Lara R, Martínez-López R, Belchamber J, Roberts SGB, Dunbar RIM (2013) Time as a limited resource: communication strategy in mobile phone networks. Soc Netw 35(1):89–95CrossRefGoogle Scholar
  35. 35.
    Stiller J, Dunbar RIM (2007) Perspective-taking and memory capacity predict social network size. Soc Netw 29(1):93–104CrossRefGoogle Scholar
  36. 36.
    Baronchelli A, Ferrer-i Cancho R, Pastor-Satorras R, Chater N, Christiansen MH (2013) Networks in cognitive science. Trends Cogn Sci 17(7):348–360CrossRefGoogle Scholar
  37. 37.
    Arnaboldi V, Conti M, Passarella A, Dunbar R (2013) Dynamics of personal social relationships in online social networks: a study on twitter. In: Proceedings of the first ACM conference on online social networks. ACM, New York, pp 15–26CrossRefGoogle Scholar
  38. 38.
    Romero DM, 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 the ACM international conference on world wide web (WWW), pp 695–704Google Scholar
  39. 39.
    Kwak H, Lee C, Park H, Moon S (2010) What is twitter, a social network or a news media? In: Proceedings of the ACM international conference on world wide web (WWW), pp 591–600Google Scholar
  40. 40.
    Onnela J-P, Saramäki J, Hyvönen J, Szabó G, De Menezes MA, Kaski K, Barabási A-L, Kertész J (2007) Analysis of a large-scale weighted network of one-to-one human communication. New J Phys 9(6):179CrossRefGoogle Scholar
  41. 41.
    Cheng X-Q, Ren F-X, Shen H-W, Zhang Z-K, Zhou T (2010) Bridgeness: a local index on edge significance in maintaining global connectivity. J Stat Mech Theory Exp 2010(10):P10011CrossRefGoogle Scholar
  42. 42.
    Grabowicz PA, Ramasco JJ, Moro E, Pujol JM, Eguiluz VM (2012) Social features of online networks: the strength of intermediary ties in online social media. PLoS One 7(1):e29358ADSCrossRefGoogle Scholar
  43. 43.
    Pajevic S, Plenz D (2012) The organization of strong links in complex networks. Nat Phys 8(5):429–436CrossRefGoogle Scholar
  44. 44.
    Huberman B, Romero D, Wu F (2009) Social networks that matter: twitter under the microscope. First Monday 14(1):8Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Lilian Weng
    • 1
  • Márton Karsai
    • 2
  • Nicola Perra
    • 3
  • Filippo Menczer
    • 4
  • Alessandro Flammini
    • 4
  1. 1.Affirm Inc.San FranciscoUSA
  2. 2.Univ Lyon, ENS de Lyon, Inria, CNRSLyonFrance
  3. 3.Centre for Business Networks AnalysisUniversity of GreenwichLondonUK
  4. 4.Center for Complex Networks and Systems Research, School of Informatics and ComputingIndiana UniversityBloomingtonUSA

Personalised recommendations