Measuring receptivity to misinformation at scale on a social media platform.

Measuring the impact of online misinformation is challenging. Traditional measures, such as user views or shares on social media, are incomplete because not everyone who is exposed to misinformation is equally likely to believe it. To address this issue, we developed a method that combines survey data with observational Twitter data to probabilistically estimate the number of users both exposed to and likely to believe a specific news story. As a proof of concept, we applied this method to 139 viral news articles and find that although false news reaches an audience with diverse political views, users who are both exposed and receptive to believing false news tend to have more extreme ideologies. These receptive users are also more likely to encounter misinformation earlier than those who are unlikely to believe it. This mismatch between overall user exposure and receptive user exposure underscores the limitation of relying solely on exposure or interaction data to measure the impact of misinformation, as well as the challenge of implementing effective interventions. To demonstrate how our approach can address this challenge, we then conducted data-driven simulations of common interventions used by social media platforms. We find that these interventions are only modestly effective at reducing exposure among users likely to believe misinformation, and their effectiveness quickly diminishes unless implemented soon after misinformation's initial spread. Our paper provides a more precise estimate of misinformation's impact by focusing on the exposure of users likely to believe it, offering insights for effective mitigation strategies on social media.

Spatiotemporal-social association predicts immunological similarity in rewilded mice.

Environmental influences on immune phenotypes are well-documented, but our understanding of which elements of the environment affect immune systems, and how, remains vague. Behaviors, including socializing with others, are central to an individual's interaction with its environment. We therefore tracked behavior of rewilded laboratory mice of three inbred strains in outdoor enclosures and examined contributions of behavior, including associations measured from spatiotemporal co-occurrences, to immune phenotypes. We found extensive variation in individual and social behavior among and within mouse strains upon rewilding. In addition, we found that the more associated two individuals were, the more similar their immune phenotypes were. Spatiotemporal association was particularly predictive of similar memory T and B cell profiles and was more influential than sibling relationships or shared infection status. These results highlight the importance of shared spatiotemporal activity patterns and/or social networks for immune phenotype and suggest potential immunological correlates of social life.

Polarized information ecosystems can reorganize social networks via information cascades.

The precise mechanisms by which the information ecosystem polarizes society remain elusive. Focusing on political sorting in networks, we develop a computational model that examines how social network structure changes when individuals participate in information cascades, evaluate their behavior, and potentially rewire their connections to others as a result. Individuals follow proattitudinal information sources but are more likely to first hear and react to news shared by their social ties and only later evaluate these reactions by direct reference to the coverage of their preferred source. Reactions to news spread through the network via a complex contagion. Following a cascade, individuals who determine that their participation was driven by a subjectively "unimportant" story adjust their social ties to avoid being misled in the future. In our model, this dynamic leads social networks to politically sort when news outlets differentially report on the same topic, even when individuals do not know others' political identities. Observational follow network data collected on Twitter support this prediction: We find that individuals in more polarized information ecosystems lose cross-ideology social ties at a rate that is higher than predicted by chance. Importantly, our model reveals that these emergent polarized networks are less efficient at diffusing information: Individuals avoid what they believe to be "unimportant" news at the expense of missing out on subjectively "important" news far more frequently. This suggests that "echo chambers"-to the extent that they exist-may not echo so much as silence.

Response thresholds alone cannot explain empirical patterns of division of labor in social insects.

The effects of heterogeneity in group composition remain a major hurdle to our understanding of collective behavior across disciplines. In social insects, division of labor (DOL) is an emergent, colony-level trait thought to depend on colony composition. Theoretically, behavioral response threshold models have most commonly been employed to investigate the impact of heterogeneity on DOL. However, empirical studies that systematically test their predictions are lacking because they require control over colony composition and the ability to monitor individual behavior in groups, both of which are challenging. Here, we employ automated behavioral tracking in 120 colonies of the clonal raider ant with unparalleled control over genetic, morphological, and demographic composition. We find that each of these sources of variation in colony composition generates a distinct pattern of behavioral organization, ranging from the amplification to the dampening of inherent behavioral differences in heterogeneous colonies. Furthermore, larvae modulate interactions between adults, exacerbating the apparent complexity. Models based on threshold variation alone only partially recapitulate these empirical patterns. However, by incorporating the potential for variability in task efficiency among adults and task demand among larvae, we account for all the observed phenomena. Our findings highlight the significance of previously overlooked parameters pertaining to both larvae and workers, allow the formulation of theoretical predictions for increasing colony complexity, and suggest new avenues of empirical study.

Social influence and interaction bias can drive emergent behavioural specialization and modular social networks across systems.

In social systems ranging from ant colonies to human society, behavioural specialization-consistent individual differences in behaviour-is commonplace: individuals can specialize in the tasks they perform (division of labour (DOL)), the political behaviour they exhibit (political polarization) or the non-task behaviours they exhibit (personalities). Across these contexts, behavioural specialization often co-occurs with modular and assortative social networks, such that individuals tend to associate with others that have the same behavioural specialization. This raises the question of whether a common mechanism could drive co-emergent behavioural specialization and social network structure across contexts. To investigate this question, here we extend a model of self-organized DOL to account for social influence and interaction bias among individuals-social dynamics that have been shown to drive political polarization. We find that these same social dynamics can also drive emergent DOL by forming a feedback loop that reinforces behavioural differences between individuals, a feedback loop that is impacted by group size. Moreover, this feedback loop also results in modular and assortative social network structure, whereby individuals associate strongly with those performing the same task. Our findings suggest that DOL and political polarization-two social phenomena not typically considered together-may actually share a common social mechanism. This mechanism may result in social organization in many contexts beyond task performance and political behaviour.

Corrigendum to "A Survey of Eyespot Sexual Dimorphism across Nymphalid Butterflies".

[This corrects the article DOI: 10.1155/2013/926702.].

A Survey of Eyespot Sexual Dimorphism across Nymphalid Butterflies.

Differences between sexes of the same species are widespread and are variable in nature. While it is often assumed that males are more ornamented than females, in the nymphalid butterfly genus Bicyclus, females have, on average, more eyespot wing color patterns than males. Here we extend these studies by surveying eyespot pattern sexual dimorphism across the Nymphalidae family of butterflies. Eyespot presence or absence was scored from a total of 38 wing compartments for two males and two females of each of 450 nymphalid species belonging to 399 different genera. Differences in eyespot number between sexes of each species were tallied for each wing surface (e.g., dorsal and ventral) of forewings and hindwings. In roughly 44% of the species with eyespots, females had more eyespots than males, in 34%, males had more eyespots than females, and, in the remaining 22% of the species, there was monomorphism in eyespot number. Dorsal and forewing surfaces were less patterned, but proportionally more dimorphic, than ventral and hindwing surfaces, respectively. In addition, wing compartments that frequently displayed eyespots were among the least sexually dimorphic. This survey suggests that dimorphism arises predominantly in "hidden" or "private" surfaces of a butterfly's wing, as previously demonstrated for the genus Bicyclus.