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Attention on Weak Ties in Social and Communication Networks

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Complex Spreading Phenomena in Social Systems

Part of the book series: Computational Social Sciences ((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.

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Notes

  1. 1.

    https://developer.twitter.com/en/docs/tweets/sample-realtime/overview/decahose.

  2. 2.

    https://developer.twitter.com/en/docs/accounts-and-users/follow-search-get-users/api-reference/get-followers-ids.

  3. 3.

    A statement about the ethical use of this dataset was issued by Northeastern University’s Institutional Review Board.

  4. 4.

    Note that in our calculation, leaf nodes (with only one out-link) are removed.

References

  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 356–364

    Google Scholar 

  2. Dunbar RIM (1998) The social brain hypothesis. Evol Anthropol 9(10):178–190

    Article  Google Scholar 

  3. 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 

  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 1–8

    Google Scholar 

  5. Weng L, Flammini A, Vespignani A, Menczer F (2012) Competition among memes in a world with limited attention. Nat Sci Rep 2:335

    Google Scholar 

  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 249–257

    Google Scholar 

  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 37–52

    Google Scholar 

  8. Davenport TH, Beck JC (2001) The attention economy: understanding the new currency of business. Harvard Business School Press, Boston

    Google Scholar 

  9. Granovetter M (1973) The strength of weak ties. Am J Sociol 78(6):1

    Article  Google Scholar 

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

    Google Scholar 

  11. Brown J, Reingen P (1987) Social ties and word-of-mouth referral behavior. J Consum Res 14(3):350–362

    Article  Google Scholar 

  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):1477–1490

    Article  Google Scholar 

  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):7332–7336

    Article  ADS  Google Scholar 

  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 211–220

    Google Scholar 

  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 519–528

    Google Scholar 

  16. Friedkin N (1980) A test of structural features of granovetter’s strength of weak ties theory. Soc Netw 2(4):411–422

    Article  Google Scholar 

  17. Lin N, Ensel WM, Vaughn JC (1981) Social resources and strength of ties: structural factors in occupational status attainment. Am Sociol Rev 46:393–405

    Article  Google Scholar 

  18. Granovetter M (1983) The strength of weak ties: a network theory revisited. Sociol Theory 1(1):201–233

    Article  Google Scholar 

  19. Nelson RE (1989) The strength of strong ties: social networks and intergroup conflict in organizations. Acad Manag J 32(2):377–401

    Google Scholar 

  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):1101–1114

    Article  Google Scholar 

  21. Wellman B, Wortley S (1990) Different strokes from different folks: community ties and social support. Am J Sociol 96(3):558–588

    Article  Google Scholar 

  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):295–298

    Article  ADS  Google Scholar 

  23. Putnam RD (2001) Bowling alone: the collapse and revival of American community. Simon and Schuster, New York

    Google Scholar 

  24. Burt RS (2009) Structural holes: the social structure of competition. Harvard University Press, Boston

    Google Scholar 

  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):721–723

    Article  Google Scholar 

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

    Article  ADS  MathSciNet  Google Scholar 

  27. Meo PD, Ferrara E, Fiumara G, Provetti A (2014) On facebook, most ties are weak. Commun. ACM 57(11):78–84

    Article  Google Scholar 

  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):025102

    Article  ADS  Google Scholar 

  29. Miritello G, Moro E, Lara R (2011) Dynamical strength of social ties in information spreading. Phys Rev E 83(4):045102

    Article  ADS  Google Scholar 

  30. Karsai M, Perra N, Vespignani A (2014) Time varying networks and the weakness of strong ties. Sci Rep 4:4001

    Article  ADS  Google Scholar 

  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 6:35724

    Article  ADS  Google Scholar 

  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):1–8

    MathSciNet  Google Scholar 

  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 217–226

    Chapter  Google Scholar 

  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):89–95

    Article  Google Scholar 

  35. Stiller J, Dunbar RIM (2007) Perspective-taking and memory capacity predict social network size. Soc Netw 29(1):93–104

    Article  Google Scholar 

  36. Baronchelli A, Ferrer-i Cancho R, Pastor-Satorras R, Chater N, Christiansen MH (2013) Networks in cognitive science. Trends Cogn Sci 17(7):348–360

    Article  Google Scholar 

  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 15–26

    Chapter  Google Scholar 

  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 695–704

    Google Scholar 

  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 591–600

    Google Scholar 

  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):179

    Article  Google Scholar 

  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):P10011

    Article  Google Scholar 

  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):e29358

    Article  ADS  Google Scholar 

  43. Pajevic S, Plenz D (2012) The organization of strong links in complex networks. Nat Phys 8(5):429–436

    Article  Google Scholar 

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

    Google Scholar 

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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.

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Correspondence to Alessandro Flammini .

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Weng, L., Karsai, M., Perra, N., Menczer, F., Flammini, A. (2018). Attention on Weak Ties in Social and Communication Networks. In: Lehmann, S., Ahn, YY. (eds) Complex Spreading Phenomena in Social Systems. Computational Social Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-77332-2_12

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