REPORTS Acknowledgments: We thank the Deutsche Forschungsgemein- with the graphics, and A. Holzapfel for editorial work Figs. S1 to S5 schaft (grants WE2039/8-1, RI525/17-1, KU683/9-1, and (all from Univ. of Cologne). Tables S1 and S2 KU683/12-1) and the U.S. NSF Paleoclimate Program for References financial support, the Alfred Wegener Institute for Polar and Supporting Online Material Marine Research for logistic support, J. Rethemeyer for www.sciencemag.org/cgi/content/full/334/6060/1265/DC1 conducting part of the AMS 14C measurements, B. Weninger Materials and Methods 3 June 2011; accepted 18 October 2011 for discussing the 14C data, D. Sprenk for assisting SOM Text 10.1126/science.1209299 obese individuals both receiving less exposure An Experimental Study of Homophily to valuable health innovations and having fewer sources of positive social influence, resulting in in the Adoption of Health Behavior lower levels of adoption (8, 9, 16, 18). By con- trast, populations in which obese individuals are Damon Centola better socially connected to healthier individuals should provide greater access to health innova- How does the composition of a population affect the adoption of health behaviors and innovations? tions (5, 6, 11, 20) and more social support for Homophily—similarity of social contacts—can increase dyadic-level influence, but it can also force less adopting new behaviors (7, 8, 16, 21), thereby healthy individuals to interact primarily with one another, thereby excluding them from interactions increasing both overall levels of adoption and the with healthier, more influential, early adopters. As a result, an important network-level effect of use of health innovations among the obese homophilyisthatthepeoplewhoaremostinneed of a health innovation may be among the least population (8, 9, 21, 22). likely to adopt it. Despite the importance of this thesis, confounding factors in observational data have An empirical test of these individual and made it difficult to test empirically. We report results from a controlled experimental study on the network-level effects of homophily has proven spread of a health innovation through fixed social networks in which the level of homophily was difficult because homophily in observational data independently varied. We found that homophily significantly increased overall adoption of a new health is usually confounded with other relevant factors behavior, especially among those most in need of it. such as the topological structure of social net- works (5, 11, 23), interpersonal affect in relation- ocial networks are a primary channel for alters than by homophilous ties to similarly low- ships (12, 24), and shared history and frequency the spread of health behaviors (1–3). How- status individuals (12, 15). of interaction among connected individuals Sever, it is not just the existence of social Although these accounts of homophily may (11, 12, 24). Moreover, individual-level factors ties between individuals that matters for diffu- be in tension with one another at the dyadic level, can be difficult to distinguish from relational sion. Just as important are the demographic com- at the network level both views support the the- ones: Are obese individuals simply less likely position of the population and the distribution of sis that homophily will reduce overall adoption, to adopt a health behavior, or is it their social individual characteristics throughout the social thereby increasing health inequalities across di- environment that reduces their likelihood of network (4–6). Homophily—the tendency of so- verse populations (12–14, 16). This network- adoption (25, 26)? Addressing these difficulties cial contacts to be similar to one another—can level effect emerges from the fact that homophily requires the ability to independently control the affect the extent of a behavior’s adoption in a can result in less healthy individuals having fewer degree of homophily in a social network while population (7–12). At the dyadic level, research social ties to healthier early adopters, which lim- simultaneously holding the topology of the net- on diffusion has suggested that homophilous ties its their level of exposure to health innovations work, and the distribution of individual- and can promote the spread of behavior between (3, 7, 11, 12). Moreover, fewer ties to healthy indi- population-level parameters, constant. individuals (11–13). This is because actors are viduals also means that the exposure that the less We addressed these issues by studying the more likely to be influenced by alters who are healthy individuals do receive is less likely to come effects of homophily on adoption experimen- similar to themselves. However, research on so- from healthier members of the population—who tally. We conducted an Internet-based social net- cial influence has also suggested that the effects may be more effective at influencing others to work experiment (27) in which we manipulated of status can interact with those of homophily adopt new behaviors (7–11)—thereby reducing the the level of homophily in people’ssocialnet- (12, 14, 15). Homophily among high-status likelihood of adoption among those less healthy works. Our study focused on the spread of a individuals may help to promote diffusion, but individuals who are ultimately exposed to the in- health behavior through a World Wide Web social low-status individuals may be more likely to be novation (15, 17). networking environment composed of 710 par- influenced by heterophilous ties to high-status This network thesis has important implica- ticipants, all of whom were recruited from an tions for obese members of a population because online fitness program (28). We constructed two Sloan School, Massachusetts Institute of Technology, the homophilous “clustering” of health character- types of online social networking communities: Cambridge, MA 02142, USA. E-mail: [email protected] istics in social networks (1, 3, 18, 19) can result in (i) homophilously structured populations, in which AB C DE 6 6 6 6 6 5 5 5 5 5 4 4 4 4 4 3 3 3 3 3 2 2 2 2 2 1 1 1 1 1 0 0 0 0 0 Number of Adopters Number of 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 1 2 3 4 5 6 7 Weeks Fig. 1. (A to E) Time series showing the number of adopters in each of the five trials. Adoption levels are shown for all homophilous (solid circles) and unstructured (open triangles) networks. www.sciencemag.org SCIENCE VOL 334 2 DECEMBER 2011 1269 REPORTS individual traits [gender, age, and body mass in- the notification allowed these neighbors to sign of adopting it, Fig. 2 compares adoption levels dex (BMI)] were “clustered” in the social network up. If they completed the sign-up procedure, this among the “obese” (i.e., BMI ≥ 30) and “non- (28, 29), and (ii) unstructured populations, which would result in each of the adopters’ neighbors obese” (BMI < 30) members of the population. had fully “integrated” neighborhoods in which also receiving notifications about the diet diary. Within the homophilous condition, a significantly participants were mixed at random regardless of At the beginning of week 1, we simultaneous- greater number of non-obese than obese individ- their individual characteristics. ly activated seed nodes in each of the 10 networks. uals adopted the behavior (P < 0.05, Mann-Whitney Each participant in the study created an anon- We then observed the spread of adoption for a U test) (Fig. 2A). This is expected because of the ymous online profile, which included the par- total of 7 weeks. high ratio of non-obese to obese individuals in ticipant’s gender, age, BMI, fitness level, diet The results (Fig. 1) show that homophilously the population (6:1) (28) (table S1). However, the preferences, and favorite exercise (28). Subjects structured social networks exhibited significantly fraction of obese adopters was significantly greater were then matched with other participants in more adoption than unstructured networks. De- than the fraction of non-obese adopters (P <0.05) the study—referred to as “health buddies”—as spite low overall levels of adoption, by the third (Fig. 2B), indicating that relative to their popula- members of an online health community. Each week of the study there was already a noticeable tion sizes, homophilous networks promoted greater participant was provided with a personalized difference between conditions: In each of the five uptake of the behavior among obese individuals online “health dashboard” that displayed real-time trials, there were greater numbers of adopters in than among non-obese individuals. By contrast, health information (e.g., daily exercise minutes) the homophilous condition than in the unstruc- in the unstructured condition, both the number as well as basic profile information (as listed tured condition. By week 4, adoption levels were (Fig. 2A) and the fraction (Fig. 2B) of non-obese above) for each participant and his or her health significantly different across experimental con- adopters was significantly greater than that of buddies (28). The health dashboards also dis- ditions (P < 0.05, Mann-Whitney U test). This obese adopters (P < 0.05). Not one obese in- played a record of any health behaviors that the difference increased in week 5 (P < 0.01) and dividual signed up for the diet diary in the un- participants and their health buddies adopted. maintained this level of significance in weeks 6 structured networks, which suggests that obese Participants arriving to the study were random- and 7. At the end of week 7, the total number of members of the population were very reluctant ized to one of the two experimental conditions—a adopters across all homophilous networks was to adopt the behavior. homophilous population condition and an unstruc- more than 3 times the total number of adopters The comparison across conditions shows that tured population condition—that were distinguished in the unstructured networks.