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Smokers quit together as a connected cluster in a social network

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Smoking in the US has dropped from 45% to 21% in the past 4 decades.  The latest research in smoking cessation studies how social networks influence the likelihood of quitting.   In this study, the authors were examining 6 areas :  (1) are there social network clusters of smokers and non-smokers (2) relationship between one person’s smoking behavior and the smoking behavior of his/her social network contacts (3) the dependence of 2 on the types of social ties (4) the influence of education on the spread of smoking (5) does cessation behavior occur in sub-networks (6) do smokers occupy special positions on the the social network?

The data consisted of 12067 subjects over a period of 32 years.  Individual behaviors were collected every few years.   Clusters of smokers were identified by finding smokers that were “fully connected”.   In graph theory, this translates to a complete graph.  By calculating certain network attributes such as eigenvector centrality, the researchers created a logistic regressions to identify the effect of centrality on smoking behavior.

Although the % of smokers dropped by 50%, the cluster size of smokers remained relatively unchange.  This indicates that smokers are quitting in connected social groups.

smokers1At the same time, the eigenvector centrality of non smokers fell, relegating them to the periphery of the network.


Among the various other findings, I find this one most interesting:  They found that geographical “distance did not modify the intensity of the effect of the contact’s smoking behavior on the behavior of the subject.  That is, smoking behavior was related between subjects and their contacts, regardless of how far apart they were geographically.”

So what?

There are various smoking cessation social networks out there such as etc.  Since there is evidence that geographical distance does not alter the effect of social networks, we can hypothesize that online social networks might have the same effect as real life face-to-face networks.  Each of these online quitting networks can therefore further tailor their offerings if they can mine their network data and score their users with network “smoking” attributes.  Artificially creating clusters of quitters from the isolated smokers might be a viable intervention option.


The collective dynamics of smoking in a large social network.  Kristakis, N. and Fowler, J. H.  New England Journal of Medicine 2008:358:2249-58



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