James H. Fowler

The Collective Dynamics of Smoking in a Large Social Network

BACKGROUND The prevalence of smoking has decreased substantially in the United States over the past 30 years. We examined the extent of the person-to-person spread of smoking behavior and the extent to which groups of widely connected people quit together. METHODS We studied a densely interconnected social network of 12,067 people assessed repeatedly from 1971 to 2003 as part of the Framingham Heart Study. We used network analytic methods and longitudinal statistical models. RESULTS Discernible clusters of smokers and nonsmokers were present in the network, and the clusters extended to three degrees of separation. Despite the decrease in smoking in the overall population, the size of the clusters of smokers remained the same across time, suggesting that whole groups of people were quitting in concert. Smokers were also progressively found in the periphery of the social network. Smoking cessation by a spouse decreased a persons chances of smoking by 67% (95% confidence interval [CI], 59 to 73). Smoking cessation by a sibling decreased the chances by 25% (95% CI, 14 to 35). Smoking cessation by a friend decreased the chances by 36% (95% CI, 12 to 55 ). Among persons working in small firms, smoking cessation by a coworker decreased the chances by 34% (95% CI, 5 to 56). Friends with more education influenced one another more than those with less education. These effects were not seen among neighbors in the immediate geographic area. CONCLUSIONS Network phenomena appear to be relevant to smoking cessation. Smoking behavior spreads through close and distant social ties, groups of interconnected people stop smoking in concert, and smokers are increasingly marginalized socially. These findings have implications for clinical and public health interventions to reduce and prevent smoking.

Computational Social Science

A field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.

Dynamic spread of happiness in a large social network: longitudinal analysis over 20 years in the Framingham Heart Study

Objectives To evaluate whether happiness can spread from person to person and whether niches of happiness form within social networks. Design Longitudinal social network analysis. Setting Framingham Heart Study social network. Participants 4739 individuals followed from 1983 to 2003. Main outcome measures Happiness measured with validated four item scale; broad array of attributes of social networks and diverse social ties. Results Clusters of happy and unhappy people are visible in the network, and the relationship between people’s happiness extends up to three degrees of separation (for example, to the friends of one’s friends’ friends). People who are surrounded by many happy people and those who are central in the network are more likely to become happy in the future. Longitudinal statistical models suggest that clusters of happiness result from the spread of happiness and not just a tendency for people to associate with similar individuals. A friend who lives within a mile (about 1.6 km) and who becomes happy increases the probability that a person is happy by 25% (95% confidence interval 1% to 57%). Similar effects are seen in coresident spouses (8%, 0.2% to 16%), siblings who live within a mile (14%, 1% to 28%), and next door neighbours (34%, 7% to 70%). Effects are not seen between coworkers. The effect decays with time and with geographical separation. Conclusions People’s happiness depends on the happiness of others with whom they are connected. This provides further justification for seeing happiness, like health, as a collective phenomenon.

A 61-Million-Person Experiment in Social Influence and Political Mobilization

Human behaviour is thought to spread through face-to-face social networks, but it is difficult to identify social influence effects in observational studies, and it is unknown whether online social networks operate in the same way–. Here we report results from a randomized controlled trial of political mobilization messages delivered to 61 million Facebook users during the 2010 US congressional elections. The results show that the messages directly influenced political self-expression, information seeking and real-world voting behaviour of millions of people. Furthermore, the messages not only influenced the users who received them but also the users’ friends, and friends of friends. The effect of social transmission on real-world voting was greater than the direct effect of the messages themselves, and nearly all the transmission occurred between ‘close friends’ who were more likely to have a face-to-face relationship. These results suggest that strong ties are instrumental for spreading both online and real-world behaviour in human social networks.

Life in the network: the coming age of computational social science

A field is emerging that leverages the capacity to collect and analyze data at a scale that may reveal patterns of individual and group behaviors.

Cooperative behavior cascades in human social networks

Theoretical models suggest that social networks influence the evolution of cooperation, but to date there have been few experimental studies. Observational data suggest that a wide variety of behaviors may spread in human social networks, but subjects in such studies can choose to befriend people with similar behaviors, posing difficulty for causal inference. Here, we exploit a seminal set of laboratory experiments that originally showed that voluntary costly punishment can help sustain cooperation. In these experiments, subjects were randomly assigned to a sequence of different groups to play a series of single-shot public goods games with strangers; this feature allowed us to draw networks of interactions to explore how cooperative and uncooperative behaviors spread from person to person to person. We show that, in both an ordinary public goods game and in a public goods game with punishment, focal individuals are influenced by fellow group members’ contribution behavior in future interactions with other individuals who were not a party to the initial interaction. Furthermore, this influence persists for multiple periods and spreads up to three degrees of separation (from person to person to person to person). The results suggest that each additional contribution a subject makes to the public good in the first period is tripled over the course of the experiment by other subjects who are directly or indirectly influenced to contribute more as a consequence. These results show experimentally that cooperative behavior cascades in human social networks.

Alone in the Crowd: The Structure and Spread of Loneliness in a Large Social Network

The discrepancy between an individuals loneliness and the number of connections in a social network is well documented, yet little is known about the placement of loneliness within, or the spread of loneliness through, social networks. The authors use network linkage data from the population-based Framingham Heart Study to trace the topography of loneliness in peoples social networks and the path through which loneliness spreads through these networks. Results indicated that loneliness occurs in clusters, extends up to 3 degrees of separation, is disproportionately represented at the periphery of social networks, and spreads through a contagious process. The spread of loneliness was found to be stronger than the spread of perceived social connections, stronger for friends than family members, and stronger for women than for men. The results advance understanding of the broad social forces that drive loneliness and suggest that efforts to reduce loneliness in society may benefit by aggressively targeting the people in the periphery to help repair their social networks and to create a protective barrier against loneliness that can keep the whole network from unraveling.

The Spread of Alcohol Consumption Behavior in a Large Social Network

BACKGROUND Alcohol consumption has important health-related consequences and numerous biological and social determinants. OBJECTIVE To explore quantitatively whether alcohol consumption behavior spreads from person to person in a large social network of friends, coworkers, siblings, spouses, and neighbors, followed for 32 years. DESIGN Longitudinal network cohort study. SETTING The Framingham Heart Study. PARTICIPANTS 12 067 persons assessed at several time points between 1971 and 2003. MEASUREMENTS Self-reported alcohol consumption (number of drinks per week on average over the past year and number of days drinking within the past week) and social network ties, measured at each time point. RESULTS Clusters of drinkers and abstainers were present in the network at all time points, and the clusters extended to 3 degrees of separation. These clusters were not only due to selective formation of social ties among drinkers but also seem to reflect interpersonal influence. Changes in the alcohol consumption behavior of a persons social network had a statistically significant effect on that persons subsequent alcohol consumption behavior. The behaviors of immediate neighbors and coworkers were not significantly associated with a persons drinking behavior, but the behavior of relatives and friends was. LIMITATIONS A nonclinical measure of alcohol consumption was used. Also, it is unclear whether the effects on long-term health are positive or negative, because alcohol has been shown to be both harmful and protective. Finally, not all network ties were observed. CONCLUSION Network phenomena seem to influence alcohol consumption behavior. This has implications for clinical and public health interventions and further supports group-level interventions to reduce problematic drinking.

Social network sensors for early detection of contagious outbreaks.

Current methods for the detection of contagious outbreaks give contemporaneous information about the course of an epidemic at best. It is known that individuals near the center of a social network are likely to be infected sooner during the course of an outbreak, on average, than those at the periphery. Unfortunately, mapping a whole network to identify central individuals who might be monitored for infection is typically very difficult. We propose an alternative strategy that does not require ascertainment of global network structure, namely, simply monitoring the friends of randomly selected individuals. Such individuals are known to be more central. To evaluate whether such a friend group could indeed provide early detection, we studied a flu outbreak at Harvard College in late 2009. We followed 744 students who were either members of a group of randomly chosen individuals or a group of their friends. Based on clinical diagnoses, the progression of the epidemic in the friend group occurred 13.9 days (95% C.I. 9.9–16.6) in advance of the randomly chosen group (i.e., the population as a whole). The friend group also showed a significant lead time (p<0.05) on day 16 of the epidemic, a full 46 days before the peak in daily incidence in the population as a whole. This sensor method could provide significant additional time to react to epidemics in small or large populations under surveillance. The amount of lead time will depend on features of the outbreak and the network at hand. The method could in principle be generalized to other biological, psychological, informational, or behavioral contagions that spread in networks.

Social networks and cooperation in hunter-gatherers

Social networks show striking structural regularities, and both theory and evidence suggest that networks may have facilitated the development of large-scale cooperation in humans. Here, we characterize the social networks of the Hadza, a population of hunter-gatherers in Tanzania. We show that Hadza networks have important properties also seen in modernized social networks, including a skewed degree distribution, degree assortativity, transitivity, reciprocity, geographic decay and homophily. We demonstrate that Hadza camps exhibit high between-group and low within-group variation in public goods game donations. Network ties are also more likely between people who give the same amount, and the similarity in cooperative behaviour extends up to two degrees of separation. Social distance appears to be as important as genetic relatedness and physical proximity in explaining assortativity in cooperation. Our results suggest that certain elements of social network structure may have been present at an early point in human history. Also, early humans may have formed ties with both kin and non-kin, based in part on their tendency to cooperate. Social networks may thus have contributed to the emergence of cooperation.