DiMaggio and Garip (2011) define network externalities (where the value of a practice is a function of network alters that have already adopted the practice) as a mechanism exacerbating social inequality under the condition of homophily (where advantaged individuals poised to be primary adopters are socially connected to other advantaged individuals). The authors use an agent-based model of diffusion on a real-life population for empirical illustration, and thus, do not consider consolidation (correlation between traits), a population parameter that shapes network structure and diffusion (Blau and Schwartz 1984, Centola 2015). Using an agent-based model, this paper shows that prior findings linking homophily to segregated social ties and to differential diffusion outcomes are contingent on high levels of consolidation. Homophily, under low consolidation, is not sufficient to exacerbate existing differences in adoption probabilities across groups, and can even end up alleviating inter-group inequality by facilitating diffusion.
Measures of audience overlap between news sources give us information on the diversity of people’s media diets and the similarity of news outlets in terms of the audiences they share. This provides a way of addressing key questions like whether audiences are increasingly fragmented. In this paper, we use audience overlap estimates to build networks that we then analyze to extract the backbone – that is, the overlapping ties that are statistically significant. We argue that the analysis of this backbone structure offers metrics that can be used to compare news consumption patterns across countries, between groups, and over time. Our analytical approach offers a new way of understanding audience structures that can enable more comparative research and, thus, more empirically grounded theoretical understandings of audience behavior in an increasingly digital media environment.
Historically, social scientists have sought out explanations of human and social phenomena that provide interpretable causal mechanisms, while often ignoring their predictive accuracy. We argue that the increasingly computational nature of social science is beginning to reverse this traditional bias against prediction; however, it has also highlighted three important issues that require resolution. First, current practices for evaluating predictions must be better standardized. Second, theoretical limits to predictive accuracy in complex social systems must be better characterized, thereby setting expectations for what can be predicted or explained. Third, predictive accuracy and interpretability must be recognized as complements, not substitutes, when evaluating explanations. Resolving these three issues will lead to better, more replicable, and more useful social science.
Using global network data, we discovered the existence of social “wormholes” – high bandwidth social ties that bridge vast network distances, enabling rapid diffusion of costly, novel, or controversial innovations whose transmission depends on strong social relationships.