Placeholder Image

Subtitles section Play video

  • For me, this story begins about 15 years ago,

  • when I was a hospice doctor at the University of Chicago.

  • And I was taking care of people who were dying and their families

  • in the South Side of Chicago.

  • And I was observing what happened to people and their families

  • over the course of their terminal illness.

  • And in my lab, I was studying the widower effect,

  • which is a very old idea in the social sciences,

  • going back 150 years,

  • known as "dying of a broken heart."

  • So, when I die, my wife's risk of death can double,

  • for instance, in the first year.

  • And I had gone to take care of one particular patient,

  • a woman who was dying of dementia.

  • And in this case, unlike this couple,

  • she was being cared for

  • by her daughter.

  • And the daughter was exhausted from caring for her mother.

  • And the daughter's husband,

  • he also was sick

  • from his wife's exhaustion.

  • And I was driving home one day,

  • and I get a phone call from the husband's friend,

  • calling me because he was depressed

  • about what was happening to his friend.

  • So here I get this call from this random guy

  • that's having an experience

  • that's being influenced by people

  • at some social distance.

  • And so I suddenly realized two very simple things:

  • First, the widowhood effect

  • was not restricted to husbands and wives.

  • And second, it was not restricted to pairs of people.

  • And I started to see the world

  • in a whole new way,

  • like pairs of people connected to each other.

  • And then I realized that these individuals

  • would be connected into foursomes with other pairs of people nearby.

  • And then, in fact, these people

  • were embedded in other sorts of relationships:

  • marriage and spousal

  • and friendship and other sorts of ties.

  • And that, in fact, these connections were vast

  • and that we were all embedded in this

  • broad set of connections with each other.

  • So I started to see the world in a completely new way

  • and I became obsessed with this.

  • I became obsessed with how it might be

  • that we're embedded in these social networks,

  • and how they affect our lives.

  • So, social networks are these intricate things of beauty,

  • and they're so elaborate and so complex

  • and so ubiquitous, in fact,

  • that one has to ask what purpose they serve.

  • Why are we embedded in social networks?

  • I mean, how do they form? How do they operate?

  • And how do they effect us?

  • So my first topic with respect to this,

  • was not death, but obesity.

  • It had become trendy

  • to speak about the "obesity epidemic."

  • And, along with my collaborator, James Fowler,

  • we began to wonder whether obesity really was epidemic

  • and could it spread from person to person

  • like the four people I discussed earlier.

  • So this is a slide of some of our initial results.

  • It's 2,200 people in the year 2000.

  • Every dot is a person. We make the dot size

  • proportional to people's body size;

  • so bigger dots are bigger people.

  • In addition, if your body size,

  • if your BMI, your body mass index, is above 30 --

  • if you're clinically obese --

  • we also colored the dots yellow.

  • So, if you look at this image, right away you might be able to see

  • that there are clusters of obese and

  • non-obese people in the image.

  • But the visual complexity is still very high.

  • It's not obvious exactly what's going on.

  • In addition, some questions are immediately raised:

  • How much clustering is there?

  • Is there more clustering than would be due to chance alone?

  • How big are the clusters? How far do they reach?

  • And, most importantly,

  • what causes the clusters?

  • So we did some mathematics to study the size of these clusters.

  • This here shows, on the Y-axis,

  • the increase in the probability that a person is obese

  • given that a social contact of theirs is obese

  • and, on the X-axis, the degrees of separation between the two people.

  • On the far left, you see the purple line.

  • It says that, if your friends are obese,

  • your risk of obesity is 45 percent higher.

  • And the next bar over, the [red] line,

  • says if your friend's friends are obese,

  • your risk of obesity is 25 percent higher.

  • And then the next line over says

  • if your friend's friend's friend, someone you probably don't even know, is obese,

  • your risk of obesity is 10 percent higher.

  • And it's only when you get to your friend's friend's friend's friends

  • that there's no longer a relationship

  • between that person's body size and your own body size.

  • Well, what might be causing this clustering?

  • There are at least three possibilities:

  • One possibility is that, as I gain weight,

  • it causes you to gain weight.

  • A kind of induction, a kind of spread from person to person.

  • Another possibility, very obvious, is homophily,

  • or, birds of a feather flock together;

  • here, I form my tie to you

  • because you and I share a similar body size.

  • And the last possibility is what is known as confounding,

  • because it confounds our ability to figure out what's going on.

  • And here, the idea is not that my weight gain

  • is causing your weight gain,

  • nor that I preferentially form a tie with you

  • because you and I share the same body size,

  • but rather that we share a common exposure

  • to something, like a health club

  • that makes us both lose weight at the same time.

  • When we studied these data, we found evidence for all of these things,

  • including for induction.

  • And we found that if your friend becomes obese,

  • it increases your risk of obesity by about 57 percent

  • in the same given time period.

  • There can be many mechanisms for this effect:

  • One possibility is that your friends say to you something like --

  • you know, they adopt a behavior that spreads to you --

  • like, they say, "Let's go have muffins and beer,"

  • which is a terrible combination. (Laughter)

  • But you adopt that combination,

  • and then you start gaining weight like them.

  • Another more subtle possibility

  • is that they start gaining weight, and it changes your ideas

  • of what an acceptable body size is.

  • Here, what's spreading from person to person

  • is not a behavior, but rather a norm:

  • An idea is spreading.

  • Now, headline writers

  • had a field day with our studies.

  • I think the headline in The New York Times was,

  • "Are you packing it on?

  • Blame your fat friends." (Laughter)

  • What was interesting to us is that the European headline writers

  • had a different take: They said,

  • "Are your friends gaining weight? Perhaps you are to blame."

  • (Laughter)

  • And we thought this was a very interesting comment on America,

  • and a kind of self-serving,

  • "not my responsibility" kind of phenomenon.

  • Now, I want to be very clear: We do not think our work

  • should or could justify prejudice

  • against people of one or another body size at all.

  • Our next questions was:

  • Could we actually visualize this spread?

  • Was weight gain in one person actually spreading

  • to weight gain in another person?

  • And this was complicated because

  • we needed to take into account the fact that the network structure,

  • the architecture of the ties, was changing across time.

  • In addition, because obesity is not a unicentric epidemic,

  • there's not a Patient Zero of the obesity epidemic --

  • if we find that guy, there was a spread of obesity out from him --

  • it's a multicentric epidemic.

  • Lots of people are doing things at the same time.

  • And I'm about to show you a 30 second video animation

  • that took me and James five years of our lives to do.

  • So, again, every dot is a person.

  • Every tie between them is a relationship.

  • We're going to put this into motion now,

  • taking daily cuts through the network for about 30 years.

  • The dot sizes are going to grow,

  • you're going to see a sea of yellow take over.

  • You're going to see people be born and die --

  • dots will appear and disappear --

  • ties will form and break, marriages and divorces,

  • friendings and defriendings.

  • A lot of complexity, a lot is happening

  • just in this 30-year period

  • that includes the obesity epidemic.

  • And, by the end, you're going to see clusters

  • of obese and non-obese individuals

  • within the network.

  • Now, when looked at this,

  • it changed the way I see things,

  • because this thing, this network

  • that's changing across time,

  • it has a memory, it moves,

  • things flow within it,

  • it has a kind of consistency --

  • people can die, but it doesn't die;

  • it still persists --

  • and it has a kind of resilience

  • that allows it to persist across time.

  • And so, I came to see these kinds of social networks

  • as living things,

  • as living things that we could put under a kind of microscope

  • to study and analyze and understand.

  • And we used a variety of techniques to do this.

  • And we started exploring all kinds of other phenomena.

  • We looked at smoking and drinking behavior,

  • and voting behavior,

  • and divorce -- which can spread --

  • and altruism.

  • And, eventually, we became interested in emotions.

  • Now, when we have emotions,

  • we show them.

  • Why do we show our emotions?

  • I mean, there would be an advantage to experiencing

  • our emotions inside, you know, anger or happiness.

  • But we don't just experience them, we show them.

  • And not only do we show them, but others can read them.

  • And, not only can they read them, but they copy them.

  • There's emotional contagion

  • that takes place in human populations.

  • And so this function of emotions

  • suggests that, in addition to any other purpose they serve,

  • they're a kind of primitive form of communication.

  • And that, in fact, if we really want to understand human emotions,

  • we need to think about them in this way.

  • Now, we're accustomed to thinking about emotions in this way,

  • in simple, sort of, brief periods of time.

  • So, for example,

  • I was giving this talk recently in New York City,

  • and I said, "You know when you're on the subway

  • and the other person across the subway car

  • smiles at you,

  • and you just instinctively smile back?"

  • And they looked at me and said, "We don't do that in New York City." (Laughter)

  • And I said, "Everywhere else in the world,

  • that's normal human behavior."

  • And so there's a very instinctive way

  • in which we briefly transmit emotions to each other.

  • And, in fact, emotional contagion can be broader still.

  • Like we could have punctuated expressions of anger,

  • as in riots.

  • The question that we wanted to ask was:

  • Could emotion spread,

  • in a more sustained way than riots, across time

  • and involve large numbers of people,

  • not just this pair of individuals smiling at each other in the subway car?

  • Maybe there's a kind of below the surface, quiet riot

  • that animates us all the time.

  • Maybe there are emotional stampedes

  • that ripple through social networks.

  • Maybe, in fact, emotions have a collective existence,

  • not just an individual existence.

  • And this is one of the first images we made to study this phenomenon.

  • Again, a social network,

  • but now we color the people yellow if they're happy

  • and blue if they're sad and green in between.

  • And if you look at this image, you can right away see

  • clusters of happy and unhappy people,

  • again, spreading to three degrees of separation.

  • And you might form the intuition

  • that the unhappy people