Distinguishing social mechanisms of membership adoption in emerging technology communities.

Digital platforms that enable and foster associations and sharing among entrepreneurs and knowledge workers have become a vital part of the new knowledge economy, yet we know little about the new form of social organization of knowledge. This paper seeks to explore and evaluate two microscopic social mechanisms, namely network effect of recruitment and cultural affinity, that may produce knowledge clustering and differentiation within these communities. To understand the relative effect of mechanisms, we develop a novel estimation procedure that matches individual users based on their historical behavioral patterns. We collected and analyzed a large-scale event dataset from a digital platform for offline in-person meetups in two major U.S. cities, New York City and San Francisco Bay Area. We found that previous methods overestimate network effect in membership adoption decisions by 176%. Our findings show that the network effect is further amplified by varied levels of cultural affinity between individuals and groups, implying a clustering effect whereby individuals tend to gravitate towards groups that are culturally proximate. Implications for understanding social differentiation and the knowledge economy are discussed.

Association Between Degrees of Separation in Physician Networks and Surgeons' Use of Perioperative Breast Magnetic Resonance Imaging.

BACKGROUND: Perioperative magnetic resonance imaging (MRI) is frequently used in breast cancer despite unproven benefits. It is unclear whether surgeons' use of breast MRI is associated with the practices of other surgeons to whom they are connected through shared patients. METHODS: We conducted a retrospective study using Medicare data to identify physicians providing breast cancer care during 2007-2009 and grouped them into patient-sharing networks. Physician pairs were classified according to their "degree of separation" based on patient-sharing (eg, physician pairs that care for the same patients were separated by 1 degree; pairs that both share patients with another physician but not with each other were separated by 2 degrees). We assessed the association between the MRI use of a surgeon and the practice patterns of surgical colleagues by comparing MRI use in the observed networks with networks with randomly shuffled rates of MRI utilization. RESULTS: Of the 15,273 patients who underwent surgery during the study period, 28.8% received perioperative MRI. These patients received care from 1806 surgeons in 60 patient-sharing networks; 55.1% of surgeons used MRI. A surgeon was 24.5% more likely to use MRI if they were directly connected to a surgeon who used MRI. This effect decreased to 16.3% for pairs of surgeons separated by 2 degrees, and 0.8% at the third degree of separation. CONCLUSIONS: Surgeons' use of perioperative breast MRI is associated with the practice of surgeons connected to them through patient-sharing; the strength of this association attenuates as the degree of separation increases.

Measuring structural similarity in large online networks.

Structural similarity based on bipartite graphs can be used to detect meaningful communities, but the networks have been tiny compared to massive online networks. Scalability is important in applications involving tens of millions of individuals with highly skewed degree distributions. Simulation analysis holding underlying similarity constant shows that two widely used measures - Jaccard index and cosine similarity - are biased by the distribution of out-degree in web-scale networks. However, an alternative measure, the Standardized Co-incident Ratio (SCR), is unbiased. We apply SCR to members of Congress, musical artists, and professional sports teams to show how massive co-following on Twitter can be used to map meaningful affiliations among cultural entities, even in the absence of direct connections to one another. Our results show how structural similarity can be used to map cultural alignments and demonstrate the potential usefulness of social media data in the study of culture, politics, and organizations across the social and behavioral sciences.

Innovation or deviation? The relationship between boundary crossing and audience evaluation in the music field.

Prior research in organizations has shown that the spanning of distinct social categories usually leads to an unfavorable reaction from the audience. In the music field, however, a recombination of categories has long been celebrated as a major source of innovation. In this research, we conduct a systematical research on the effect of spanning behavior by musicians with a particular focus on the structural heterogeneity of categorical boundaries. We first ask whether the blending of distinct music genres is penalized in the music field, and then investigate how the outcomes of spanning behavior are differentiated by the structural characteristics of each genres. After collecting a comprehensive dataset of musicians in the United States from diverse sources including AllMusic, iTunes, and MusicBrainz, we construct a two-mode network of musicians and subgenres. In calculating musicians' genre-spanning behavior, we suggest a new diversity metric by incorporating the affinity between genres. Our results suggest that genre-generalist musicians who combine distinct music genres are more likely to be devaluated by listeners compared to genre-specialists who adhere to a single genre. Moreover, we find that musicians tend to be more penalized when they blend genres that have nonporous boundaries rather than penetrable boundaries. This research expands our understanding of the conditions under which boundary crossing leads to negative audience evaluation.

Why Do Liberals Drink Lattes?

Popular accounts of "lifestyle politics" and "culture wars" suggest that political and ideological divisions extend also to leisure activities, consumption, aesthetic taste, and personal morality. Drawing on a total of 22,572 pairwise correlations from the General Social Survey (1972-2010), the authors provide comprehensive empirical support for the anecdotal accounts. Moreover, most ideological differences in lifestyle cannot be explained by demographic covariates alone. The authors propose a surprisingly simplesolution to the puzzle of lifestyle politics. Computational experiments show how the self-reinforcing dynamics of homophily and influence dramatically amplify even very small elective affinities between lifestyle and ideology, producing a stereotypical world of "latte liberals" and "bird-hunting conservatives" much like the one in which we live.