Recent widespread adoption of electronic and pervasive technologies has enabled the study of human behavior at an unprecedented level, uncovering universal patterns underlying human activity, mobility, and interpersonal communication. In the present work, we investigate whether deviations from these universal patterns may reveal information about the socio-economical status of geographical regions. We quantify the extent to which deviations in diurnal rhythm, mobility patterns, and communication styles across regions relate to their unemployment incidence. For this we examine a country-scale publicly articulated social media dataset, where we quantify individual behavioral features from over 19 million geo-located messages distributed among more than 340 different Spanish economic regions, inferred by computing communities of cohesive mobility fluxes. We find that regions exhibiting more diverse mobility fluxes, earlier diurnal rhythms, and more correct grammatical styles display lower unemployment rates. As a result, we provide a simple model able to produce accurate, easily interpretable reconstruction of regional unemployment incidence from their social-media digital fingerprints alone. Our results show that cost-effective economical indicators can be built based on publicly-available social media datasets.

Recent research has focused on the monitoring of global–scale online data for improved detection of epidemics, mood patterns, movements in the stock market political revolutions, box-office revenues, consumer behaviour and many other important phenomena. However, privacy considerations and the sheer scale of data available online are quickly making global monitoring infeasible, and existing methods do not take full advantage of local network structure to identify key nodes for monitoring. Here, we develop a model of the contagious spread of information in a global-scale, publicly-articulated social network and show that a simple method can yield not just early detection, but advance warning of contagious outbreaks. In this method, we randomly choose a small fraction of nodes in the network and then we randomly choose a friend of each node to include in a group for local monitoring. Using six months of data from most of the full Twittersphere, we show that this friend group is more central in the network and it helps us to detect viral outbreaks of the use of novel hashtags about 7 days earlier than we could with an equal-sized randomly chosen group. Moreover, the method actually works better than expected due to network structure alone because highly central actors are both more active and exhibit increased diversity in the information they transmit to others. These results suggest that local monitoring is not just more efficient, but also more effective, and it may be applied to monitor contagious processes in global–scale networks.

Mapping the distribution of poverty in developing countries is essential for humanitarian organizations and policymakers to formulate targeted programs and aid. However, traditional methods for obtaining socioeconomic data can be time-consuming, expensive, and labor-intensive. Recent studies have demonstrated the effectiveness of combining machine learning and satellite images to estimate wealth in sub-Saharan African countries (Xie et al., 2016, Jean et al., 2016). In this study, we investigate the extent to which this method can be applied in the context of the Philippine archipelago to predict four different socioeconomic indicators: wealth level, years of education, access to electricity, and access to water. We also propose an alternative, cost-effective approach that leverages a combination of volunteered geographic information from OpenStreetMap and nighttime lights satellite imagery for estimating socioeconomic indicators. The best models, which incorporate regional indicators as predictors, explain approximately 63% of the variation in asset-based wealth. Our findings also indicate that models trained on publicly available, volunteer-curated geographic data achieve the same predictive performance as that of models trained using proprietary satellite images.

Probabilistic forecasts play an indispensable role in answering questions about the spread of newly emerged pathogens. However, uncertainties about the epidemiology of emerging pathogens can make it difficult to choose among alternative model structures and assumptions. To assess the potential for uncertainties about emerging pathogens to affect forecasts of their spread, we evaluated the performance 16 forecasting models in the context of the 2015-2016 Zika epidemic in Colombia. Each model featured a different combination of assumptions about human mobility, spatiotemporal variation in transmission potential, and the number of virus introductions. We found that which model assumptions had the most ensemble weight changed through time. We additionally identified a trade-off whereby some individual models outperformed ensemble models early in the epidemic, but on average the ensembles outperformed all individual models. Our results suggest that multiple models spanning uncertainty across alternative assumptions are necessary to obtain robust forecasts for emerging infectious diseases.

Few teachers include information and communication technology in the classroom, despite their potential for increasing attention and motivation for students. Educational authoring tools are intended to turn teachers into designers and deliverers of technology-enhanced educational content, and increasing the adoption of these tools is a key element for speeding up this transformation. This paper emphasizes the importance of learnability for preventing rejection or abandonment by of such an authoring tool, and how acceptance is deeply affected by the interaction paradigm and the creation metaphor used in the tool. We present an analysis comparing two design paradigms: the widespread menu-based and choice-guided interaction paradigm versus a consistent metaphor with direct manipulation. The latter was implemented in DEDOS-Editor, a novel authoring tool that allows the creation of diverse educational activities that can be performed on different devices, such as PCs, digital blackboards, tablets, and multitouch surfaces. An experimental study shows the tremendous impact that interface choices have on the tool's learning curve. The results provide the first mapping of the choice of a direct-manipulation interface and its effect on the learning curve's entry point, as well as a consistent interaction metaphor with smoother and fast-growing learning curves. This allows users to complete more tasks and gain more knowledge through experience, in contrast to menu-based interfaces. The initial use of the tool is thus made easier for users with no experience or information about the tool, and the advantages of experience and expertize in facing new challenges are facilitating. This work also highlights the appropriateness of learning curves as a tool for measuring learnability

Socioeconomic segregation patterns in networks usually evolve gradually, yet they can change abruptly in response to external shocks. The recent COVID-19 pandemic and the subsequent government policies induced several interruptions in societies, potentially disadvantaging the socioeconomically most vulnerable groups. Using large-scale digital behavioral observations as a natural laboratory, here we analyze how lockdown interventions lead to the reorganization of socioeconomic segregation patterns simultaneously in communication and mobility networks in Sierra Leone. We find that while segregation in mobility clearly increased during lockdown, the social communication network reorganized into a less segregated configuration as compared to reference periods. Moreover, due to differences in adaption capacities, the effects of lockdown policies varied across socioeconomic groups, leading to different or even opposite segregation patterns between the lower and higher socioeconomic classes. Such secondary effects of interventions need to be considered for better and more equitable policies.