Relationship mining

Each day trillions of emails, phone calls, comments on blogs, twitter messages, exchanges in online social networks, etc. are done. Not only the number of communications has increased, but also each of these transactions leaves a digital trace that can be recorded to reconstruct our high-frequency human activity. It is not only the amount and variety of data that is recorded what is important. Also its high-frequency character and its comprehensive nature have allowed researchers, companies and agencies to investigate individual and group dynamics at an unprecedented level of detail and applied them to client modeling, organizational analysis or epidemic spreading [1].

However, for technical or privacy reasons only the existence but not of the content of those exchanges is known. Thus we can quantify the intensity and frequency of the interaction but not its type. For decades, social science has measured relationships between individuals in the currency of tie strength, introduced by Granovetter [1]. Weak ties (loose acquaintances) can help to disseminate ideas and/or innovations between different groups, help to find a job or new information; while strong ties (family, trusted friends) hold together organizations and social groups and can affect emotional health. Despite its success to explain these phenomena, tie strength of human relationships is vaguely defined in most large-scale social empirical work. Specifically, relationships are generally quantified by the intensity or duration of communication, although they are known to have significant drawbacks as tie strength predictor [3,4]. Multiplexity, rhythm and depth of the communication seem to be better predictors of tie strength than intensity [4]. Incorporating those metrics in the data mining of online communication might improve the definition of relationships between individuals and in turn transform our understanding of individual dynamics and its impact in our lives, organizations and society [5]. The challenge is to unveil social relationships in social media and not just mere interactions between individuals, which in general over-represent the real structure of a social group [6] (see figure). And this is of paramount importance to understand the propagation of ideas, opinions, commercial messages, etc. in social networks, since most links declared in social networks might be meaningless from a relationship point of view.

undressing1

Undressing the social network: considering all e-mail interactions in a academic social network (left) yields to a highly dense and connected social network, while strong interactions (based on the individual relative frequency of communication) render the social group sparser and disconnected

References

  1. D. Lazer et al. Computational Social Science, Science 323, 721 (2009)
  2. M. S. Granovetter, The Strength of Weak Ties, The American Journal of Sociology 78(6), 1360 (1973)
  3. P. V. Marsden, and K. E. Campbell Measuring Tie Strength Social Forces 63(2), 482 (1990).
  4. E. Gilbert and K. Karahalios, Predicting Tie Strength with Social Media, presented in CHI 2009.
  5. C. T. Butts, Revisting the Foundations of Network Analysis, Science 325, 414 (2009)
  6. B. A. Huberman, D. M. Romero, and F. Wu, Social networks that matter, First Monday 14(1) (2009).

Note: This article appears in the Catalog of the exhibition “Culturas del Cambio: Átomos Sociales y Vidas Electrónicas” in the Center Arts Santa Mónica. Thanks to  Josep Perelló for his kind invitation to contribute

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