Network phenomena appear to be relevant to the biologic and behavioral trait of obesity, and obesity appears to spread through social ties. These findings have implications for clinical and public health interventions.

We live life in the network. When we wake up in the morning, we check our e-mail, make a quick phone call, walk outside (our movements captured by a high definition video camera), get on the bus (swiping our RFID mass transit cards) or drive (using a transponder to zip through the tolls). We arrive at the airport, making sure to purchase a sandwich with a credit card before boarding the plane, and check our BlackBerries shortly before takeoff. Or we visit the doctor or the car mechanic, generating digital records of what our medical or automotive problems are. We post blog entries confiding to the world our thoughts and feelings, or maintain personal NIH Public Access

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.

Human behaviour is thought to spread through face-to-face social networks, but it is difficult to identify social influence effects in observational studies9–13, and it is unknown whether online social networks operate in the same way14–19. 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.

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. INTRODUCTIONHappiness is a fundamental object of human existence, 1 so much so that the World Health Organization is increasingly emphasising happiness as a component of health. Emotional states can be transferred directly from one individual to another by mimicry and "emotional contagion," 17 perhaps by the copying of emotionally relevant bodily actions, particularly facial expressions, seen in others. [18][19][20] People can "catch" emotional states they observe in others over time frames ranging from seconds to weeks.17 21-23 For example, students randomly assigned to a mildly depressed room-mate became increasingly depressed over a three month period, 24 and the possibility of emotional contagion between strangers, even those in ephemeral contact, has been documented by the effects of "service with a smile" on customer satisfaction and tipping. 25 26 Yet, despite the evidence that certain emotions might spread over short periods from person to person, little is known about the role of social networks in happiness or about whether happiness might spread, by a diverse set of mechanisms, over longer periods or more widely in social networks. As diverse phenomena can spread in social networks, [27][28][29][30][31][32][33][34][35] we investigated whether happiness also does so. We were particularly interested in whether the spread of happiness pertains not just to direct relationships (such as friends) but also to indirect relationships (such as friends ...