Using cell phone data to research social networks

Social network analysis is a growing area within sociology and other disciplines. The Wall Street Journal reports on the advantages of examining cell phone data:

As a tool for field research, the cellphone is unique. Unlike a conventional land-line telephone, a mobile phone usually is used by only one person, and it stays with that person everywhere, throughout the day. Phone companies routinely track a handset’s location (in part to connect it to the nearest cellphone tower) along with the timing and duration of phone calls and the user’s billing address…

Advances in statistics, psychology and the science of social networks are giving researchers the tools to find patterns of human dynamics too subtle to detect by other means. At Northeastern University in Boston, network physicists discovered just how predictable people could be by studying the travel routines of 100,000 European mobile-phone users.

After analyzing more than 16 million records of call date, time and position, the researchers determined that, taken together, people’s movements appeared to follow a mathematical pattern. The scientists said that, with enough information about past movements, they could forecast someone’s future whereabouts with 93.6% accuracy.

The pattern held true whether people stayed close to home or traveled widely, and wasn’t affected by the phone user’s age or gender.

The rest of the article then goes on to talk about a lot of interesting research on topics like social contagions (see an example of this research here) and social relationships using this data.

Some may be concerned about privacy, particularly with recent reports about iPhones and iPads containing a file that records the movements of users. I have a few thoughts about this:

1. Compared to other possible data sources (surveys, time diaries, interviews, ethnography), this seems like a treasure trove of information. The article suggests that nearly 75% of people in the world have cell phones – what other data source can compare with that? Could the research potential outweigh individual privacy concerns? In thinking about some of these research questions, it would be very difficult to use more traditional methods to address the same concerns. And just the sheer number of cases a researcher could access and work with is fantastic. In order to build more complex models of human behavior, this is exactly the kind of data one could use.

2. I would be less concerned about researchers using this data than companies. Researchers don’t particularly care about the individual cases in the data but rather are looking for broad patterns. I would also guess that the cell phone data is anonymized so that researchers would have a difficult time pinpointing specific individuals even if they wanted to.

3. How much of a surprise is it that this available data is being used? Don’t cell phone carriers include some sort of statement in their contracts about using data in such ways? One option here would be to not get a smart phone. But if you want a smart phone (and it seems that a lot of Americans do), then this is the tradeoff. This is similar to the tradeoff with Facebook: users willingly give their information to enhance their social lives and then the company can look for ways to profit from this information.

h/t Instapundit

Obesity as “social contagion”

Findings in recent years that certain medical conditions, such as obesity, are strongly influenced by social networks have seemed to shake up thinking about how such conditions spread. A new model that explores obesity as a “social contagion” suggests obesity will eventually “spread” to at least 42% of the US population:

The social contagion hypothesis garnered widespread attention in 2007 when researchers from UC San Diego, documented that obesity can spread through a social network — just like viruses spread — because people “infect” each other with their perceptions of weight. That study was published in the New England Journal of Medicine, and subsequent research has confirmed the validity of the social contagion theory.

In a paper published Thursday in the journal PLoS Computational Biology, Harvard scientists applied a mathematical model of social contagion to 40 years of data from the Framingham Heart Study, a study that has followed more than 5,000 adult residents of Framingham, Mass., since 1948 to assess their heart health. Among the participants of that study, obesity increased from 14% in the 1970s to 30% in 2000 and continues to increase. Based on that data, the rapid upswing in obesity rates is due largely to social-network influence, said the authors of the new study. But, they noted, any subsequent rise in obesity rates will be slower than it has in the past. It may take another 40 years to reach that 42% obesity mark.

Interesting findings and I bet it is a complicated (but interesting) method by which such models are constructed.

If similar models were applied to other conditions or diseases, what are the results? In addition to obesity, what conditions are strongly influenced by social networks? Additionally, how can social networks be changed or altered so that an issue like obesity is combated?