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MIT Researcher Finds That Social Networks Influence Health Behaviors

graphic of a social networkScientists have long thought that social networks, which features many distant connections, or “long ties,” produces large-scale changes most quickly. But in a new study, Damon Centola, an assistant professor at the MIT Sloan School of Management, has reached a different conclusion: Individuals are more likely to acquire new health practices while living in networks with dense clusters of connections – that is, when in close contact with people they already know well. Included in this report is a video interview with researcher, Damon Centola.

Researchers often regard these dense clusters of connections to be redundant when it comes to spreading information; networks featuring such clusters are considered less efficient than networks with a greater proportion of long ties. But getting people to change ingrained habits, Centola found, requires the extra reinforcement that comes from those redundancies. In other words, people need to hear a new idea multiple times before making a change.

“For about 35 years, wisdom in the social sciences has been that the more long ties there are in a network, the faster a thing will spread,” says Centola. “It’s startling to see that this is not always the case.” Centola’s paper on the subject, “The Spread of Behavior in an Online Social Network Experiment,” is published in the Sept. 3 issue of the journal Science.

computer representation of clustered networks

These figures show experimentally manipulated on-line social networks. The first community (left) has a clustered network structure, while the second one is a more 'random' casual contact network. Node colors indicate people who adopted a behavior (blue) and those who did not (white), with lighted links showing the active pathways of communication. The clustered networks spread the behavior to more people than the casual contact networks. Image: Damon Centola

To see what difference the form of a social network makes, Centola ran a series of experiments using an Internet-based health community he developed. The 1,528 people in the study had anonymous online profiles and a series of health interests; they were matched with other participants sharing the same interests — “health buddies,” as Centola calls them in the paper. Participants received e-mail updates notifying them about the activities of their health buddies.

Centola placed participants into one of two distinct kinds of networks — those oriented around long ties, and those featuring larger clusters of people — and ran six separate trials over a period of a few weeks to see which groups were more likely to register for an online health forum website offering ratings of health resources.

Overall, 54 percent of the people in clustered networks registered for the health forum, compared to 38 percent in the networks oriented around longer ties; the rate of adoption in the clustered networks was also four times as fast. Moreover, people were more likely to participate regularly in the health forum if they had more health buddies who registered for it. Only 15 percent of forum participants with one friend in the forum returned to it, but more than 30 percent of subjects with two friends returned to it, and over 40 percent with three friends in the forum made repeat visits.

“Social reinforcement from multiple health buddies made participants much more willing to adopt the behavior,” notes Centola in the paper. Significantly, he writes, this effect on individuals “translates into a system-level phenomenon whereby large-scale diffusion can reach more people, and spread more quickly, in clustered networks than in random networks.”

Centola thinks the existence of this effect has important implications for health officials. A “simple contagion,” in network theory, can spread with a single contact; a “complex contagion” requires multiple exposures for transmission. A disease, Centola suggests, can spread as a simple contagion, but behavior that can prevent the disease — such as going to a clinic for a vaccination — might spread only as a complex contagion, thus needing to be spurred by reinforcement from multiple neighbors in a social network.

“If there is a significant difference between simple and complex contagions, that actually matters for our policy interventions,” says Centola. The public promotion of screenings and other forms of disease prevention might best be aimed at communities and groups that act as closely clustered networks.

Centola thinks there is also further work to be done evaluating the effects of online social networks on behavior. “There is a natural implication in terms of what this means for designing online communities,” says Centola. His new research, building on his current paper, aims to find new designs for online communities, in order to promote good health practices.

Material adapted from MIT.

Reference
“The Spread of Behavior in an Online Social Network Experiment,” by Damon Centola. Science, 03 September, 2010.

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One Response to MIT Researcher Finds That Social Networks Influence Health Behaviors

  1. avatar
    Rick May 17, 2013 at 1:42 PM #

    I agree though from a slighly different perspective same conclusion. The main differences in the process of thinking is in the separation, collection, manifestation, and storage of any information. This is due to two different architectural array of neurons: LowDC vs HighDC (low density clustering or high density clustering). If neurons were people we could “allegorically/ symbolically” understand that rumors/information spreads faster the more people are around in tight groups and the opposite is true if less people and spread further apart. Another way to look as data and neuron is like the layouts of major cities where the center area is more populated but has many more roads in a tight web-like mesh facilitating flow of traffic along more than one path. As we move much further out we hardly see any roads and usually there is but only one path for “traffic/our information” to get to it’s destination. Still neurons are more like sensors too so if 100 motion activated flood lights are on only one house wall any movement by a person will give a clear evidence even from a great distance by the turning on of every single floodlight. However if the same number of motion activated lights is spread over a bigger area (one per house wall across several city blocks), we would not get one single bright light but we can more easily tell exactly where the object is at anytime due the the turning of individual light at different time; additionally, we can also tell in what direction and how fast that person is moving. In the brain this all translates as more neuron closer together means high sensitivity (over reactive/emotions) which is opposite of low sensitivity (under-reactive sequential/logic). A Logical array lends itself to the understanding of maps and navigation though now it is more similar to modern GPS in that like the human brain it is self aware from what direction information starts, what the conclusion will be, and how fast it is going to manifest itself. It allow for predictability thus maneuvering.

    NOTE: Another way to look at all this is that many sudden geological or cosmic catastrophes are basically emotional manifestations of nature phenomenons as is evident by charts and graphs displaying hopefully only short-term instantaneous powerful burst of energy as compared to gradual processes such as the gentle heating of the Earth over an extended period of time. Any “planet” going through too many geological sudden burst of uncontrollable energy would not be able to sustain life as it could be self-destructive! However since all energy is both a particle and a wave, there is no way around it, just as in mathematics, the impulsive sinusoidal curve (along the x-axis of time) does spent half it lifespan on the negative side of the graph and half on the positive. The goal would be for the oscillating bipolar energy to dampened and reach what engineers call the steady-state or oneness with and along the axis of Time.

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