Distrust in scientific expertise^{1,2,3,4,5,6,7,8,9,10,11,12,13,14} is dangerous. Opposition to vaccination with a future vaccine against SARS-CoV-2, the causal agent of COVID-19, for example, could amplify outbreaks^2,3,4, as happened for measles in 2019^5,6. Homemade remedies^7,8 and falsehoods are being shared widely on the Internet, as well as dismissals of expert advice^9,10,11. There is a lack of understanding about how this distrust evolves at the system level^13,14. Here we provide a map of the contention surrounding vaccines that has emerged from the global pool of around three billion Facebook users. Its core reveals a multi-sided landscape of unprecedented intricacy that involves nearly 100 million individuals partitioned into highly dynamic, interconnected clusters across cities, countries, continents and languages. Although smaller in overall size, anti-vaccination clusters manage to become highly entangled with undecided clusters in the main online network, whereas pro-vaccination clusters are more peripheral. Our theoretical framework reproduces the recent explosive growth in anti-vaccination views, and predicts that these views will dominate in a decade. Insights provided by this framework can inform new policies and approaches to interrupt this shift to negative views. Our results challenge the conventional thinking about undecided individuals in issues of contention surrounding health, shed light on other issues of contention such as climate change¹¹, and highlight the key role of network cluster dynamics in multi-species ecologies¹⁵.

Tackling the advance of online threats: Online support for adversarial groups such as Islamic State (ISIS) can turn local into global threats and attract new recruits and funding. Johnson et al. analyzed data collected on ISIS-related websites involving 108,086 individual followers between 1 January 1 and 31 August 2015. They developed a statistical model aimed at identifying behavioral patterns among online supporters of ISIS and used this information to predict the onset of major violent events. Sudden escalation in the number of ISIS-supporting ad hoc web groups (“aggregates”) preceded the onset of violence in a way that would not have been detected by looking at social media references to ISIS alone. The model suggests how the development and evolution of such aggregates can be blocked. Science , this issue p. 1459

Temporal behavior is an essential aspect of all biological systems. Time series have been previously represented as networks. Such representations must address two fundamental problems on how to: (1) Create appropriate networks to reflect the characteristics of biological time series. (2) Detect characteristic dynamic patterns or events as network temporal communities. General community detection methods use metrics comparing the connectivity within a community to random models, or are based on the betweenness centrality of edges or nodes. However, such methods were not designed for network representations of time series. We introduce a visibility-graph-based method to build networks from time series and detect temporal communities within these networks. To characterize unevenly sampled time series (typical of biological experiments), and simultaneously capture events associated to peaks and troughs, we introduce the Weighted Dual-Perspective Visibility Graph (WDPVG). To detect temporal communities in individual signals, we first find the shortest path of the network between start and end nodes, identifying high intensity nodes as the main stem of our community detection algorithm that act as hubs for each community. Then, we aggregate nodes outside the shortest path to the closest nodes found on the main stem based on the closest path length, thereby assigning every node to a temporal community based on proximity to the stem nodes/hubs. We demonstrate the validity and effectiveness of our method through simulation and biological applications.

Human observations can only capture a portion of ongoing classroom social activity, and are not ideal for understanding how children’s interactions are spatially structured. Here we demonstrate how social interaction can be investigated by modeling automated continuous measurements of children’s location and movement using a commercial system based on radio frequency identification. Continuous location data were obtained from 16 five-year-olds observed during three 1-h classroom free play observations. Illustrative coordinate mapping indicated that boys and girls tended to cluster in different physical locations in the classroom, but there was no suggestion of gender differences in children’s velocity (i.e., speed of movement). To detect social interaction, we present the radial distribution function, an index of when children were in social contact at greater than chance levels. Rank-order plots indicated that children were in social contact tens to hundreds of times more with some peers than others. We illustrate the use of social ties (higher than average levels of social contact) to visualize the classroom network. Analysis of the network suggests that transitivity is a potential lens through which to examine male, female, and mixed-sex cliques. The illustrative findings suggest the validity of the new measurement approach by re-examining well-established gender segregation findings from a new perspective.

Gain and oil are important compounds in global food supplies, and ensuring the quality and safety of grains and oil is a critical issue in the food supply chain security. Data traceability is the key factor in quality and safety management. Currently, it is a big challenge to ensure the reliability of data and guarantee the efficient exchange of data in various highly heterogeneous systems. To address this challenge, we develop a reliable traceability model applied to the grain and oil industry. In this paper, we first analyze the characteristics of the whole chain traceability information flow, and then we propose the concept that the connector for blockchain and industrial internet is suitable for data traceability in the grain and oil industry. Based on this concept, a reliable traceability model of grain and oil quality and safety is constructed. Finally, a reliable traceability prototype system for wheat quality and safety was designed, and the system implementation of the model was validated. The overall advantage of the proposed model is that the traceability information is safe and credible, the interaction is concise and efficient, and the security and full-process traceability of cross-chain information interaction are guaranteed. This paper fills the gap in the application of research chain network in the field of grain and oil traceability. Reference to this model can also be used to implement and adjust the traceability system, which is adaptable to stakeholders in the grain and oil industry. The model and techniques in this paper not only demonstrate value in real-world applications but also inspire further research in the field.