Companies using social capital data for betting on people’s lives

Flickr photo by idletype

The Wall Street Journal recently noted  how insurance companies (Aviva PLC, Prudential Financial, AIG) bet on whom to insure at what rates through data mining.  Much of the info gleaned from online purchases and other digital traces is more lifestyle: is the insurance applicant an athlete? a TV addict? a hunter?

But some of the information is social capital-related:

Increasingly, some gather online information, including from social-networking sites. Acxiom Corp., one of the biggest data firms, says it acquires a limited amount of “public” information from social-networking sites, helping “our clients to identify active social-media users, their favorite networks, how socially active they are versus the norm, and on what kind of fan pages they participate.”

For insurers and data-sellers alike, the new techniques could open up a regulatory can of worms. The information sold by marketing-database firms is lightly regulated. But using it in the life-insurance application process would “raise questions” about whether the data would be subject to the federal Fair Credit Reporting Act, says Rebecca Kuehn of the Federal Trade Commission’s division of privacy and identity protection. The law’s provisions kick in when “adverse action” is taken against a person, such as a decision to deny insurance or increase rates. The law requires that people be notified of any adverse action and be allowed to dispute the accuracy or completeness of data, according to the FTC.

The article also notes that Celent, an insurance consulting division of Marsh & McLennan, indicates that such online social-network data could be mined for policing fraud and in making pricing decisions: “A life insurer might want to scrutinize an applicant who reports no family history of cancer, but indicates online an affinity with a cancer-research group, says Mike Fitzgerald, a Celent senior analyst.  ‘Whether people actually realize it or not, they are significantly increasing their personal transparency,’ he says. ‘It’s all public, and it’s electronically mineable.’  ”

We’ve written earlier about other life insurers using social capital data in making insurance decisions, but in those cases, the individual was being asked directly about his social and civic involvement.  [See also this blog post about social capital and healthcare.]

We applaud the life insurers for coming to the late realization that social capital data is strongly related to health, but strongly believe they should be more transparent about what they are doing.  Then it wouldn’t violate privacy concerns and it would have the added benefit of making the insured better aware of the positive health impact of being more involved civicly and socially, which might actually induce those who are less engaged to become more so.

See earlier blog post on loss of digital privacy and digital traces left online.

Read “Insurers Test Data Profiles to Identify Risky Clients” (Wall St. Journal, 11/17/2010, by Leslie Scism and Mark Maremount)

The Friendship Paradox: using social networks to predict spread of epidemics

Nick Christakis and James Fowler (whose research we’ve previously highlighted) is back with research that shows how one can easily use “sensors” in a network to track and get early warning regarding the spread of epidemics.

They took advantage of the “friendship paradox” to do so.  In any real-life network, our friends are more popular than we are.  [This is true mathematically in any group with some loners and some social butterflies.  If you poll members in the group about their friendships, far more of those friends who are reported are going to be the social butterflies.  If far more people reported friendships with the loners, they wouldn’t be loners.  See discussion here.]

Thus by asking random people in a network, in this case Harvard students, about their friends, researchers know that their friends are more centrally located in these networks.    Then one can track behavior among the random group and their friends, in this case the spread of H1N1 flu (swine flu) among 744 Harvard students in 2009.

Those more central in these networks (the “friend” group) got the flu a full 16-47 days earlier than the random group.  Thus, for public authorities, monitoring such a “friend” group could give one early indication of a spreading epidemic; they could serve as “canaries in the coal mine”.  If the process of spreading was person-to-person rather than being exposed to some impersonal information (via a website or a broadcast), one could also track the difference between a random group and a friend group to predict other more positive epidemics, like the spread of information, or the diffusion of a product, or a social norm.

We write in general on this blog about the positive benefits of social ties (social capital), but Fowler and Christakis’ study also shows you that having friends and being centrally located has its costs: in this case getting the flu faster.  [In some ways, this is analogous to Gladwell’s discussion in the Tipping Point of how Mavens, Connectors and Salesmen may be disproportionately influential in the spread of ideas through networks, although Fowler and Christakis are far more mathematical in identifying who these central folks are.]

The “friends group manifested the flu roughly two weeks prior to the random group using one method of detection, and a full 46 days prior to the epidemic peak using another method.

‘We think this may have significant implications for public health,’ said Christakis. ‘Public health officials often track epidemics by following random samples of people or monitoring people after they get sick. But that approach only provides a snapshot of what’s currently happening. By simply asking members of the random group to name friends, and then tracking and comparing both groups, we can predict epidemics before they strike the population at large. This would allow an earlier, more vigorous, and more effective response.’

‘If you want a crystal ball for finding out which parts of the country are going to get the flu first, then this may be the most effective method we have now,’ said Fowler. ‘Currently used methods are based on statistics that lag the real world – or, at best, are contemporaneous with it. We show a way you can get ahead of an epidemic of flu, or potentially anything else that spreads in networks.’

Christakis also notes that if you provided a random 30% in a population with immunity to a flu, you don’t protect the greater public, but if you took a random 30% of the population, asked them to name their friends, and then provided immunization to their friends, in a typical network the “friend” immunization strategy would achieve as high immunity protection for the entire network as giving 96% of the population immunity shots, but at less than 1/3 the cost.

The following video shows how the nodes that light up first (markers for getting the flu) are more central and far less likely to be at the periphery of the social network.  The red dots are people getting the flu; the yellow dots are friends of people with the flu and the size of the dot is proportional to how many of their friends have the flu.

Good summary of this research and its implications here: Nick Christakis TED talk (June 2010) – How social networks predict spread of flu.  Nick also discusses some of the implications of computational social science, which we’ve previously discussed here under the heading of digital traces.  Nick discusses how one could use data gathered from these networks (either passively or actively) to do things like predict recessions from patterns of fuel consumption by truckers, to communicate with drivers of a road of impending traffic jams ahead of them (by monitoring from cell phone users on the road ahead of them how rapidly they are changing cell phone towers) to asking those central in a mobile cellphone network (easily mapable today) to text their daily temperature (to monitor for impending flu epidemics).  Obviously these raise issues of privacy, which Nick does not discuss.

News release of study

Academic article in PLoS ONE

James Fowler on The Colbert Report discussing the book by Fowler and Christakis called Connected.

Nick Christakis presenting a talk at TED — The Hidden Influence of Social Networks. (February 2010).  In the talk he notes that while almost half of the variation in our number of friends is genetically-based (46%), that another equally large portion (47%) of whether your friends know each other is a function of whether your friends are the type that introduce (“knit”) their friends together or keep them apart (what they call “transitivity”).  About a third of whether you are in the center of social networks or not is genetically inherited.  Christakis believes that these social networks are critically important to transmitting ideas, and kindness, and information and goodness; and if society realized how valuable these networks were, we’d focus far more of our time, energy and resources into helping these networks to flourish.