We analyze information diffusion using empirical data that tracks online communication around two instances of mass political mobilization that took place in Spain in 2011 and 2012. We also analyze protest-related communications during the year that elapsed between those protests. We compare the global properties of the topological and dynamic networks through which communication took place, as well as local changes in network composition. We show that changes in network structure underlie aggregated differences on how information diffused: an increase in network hierarchy is accompanied by a reduction in the average size of cascades. The increasing hierarchy affects not only the underlying communication topology but also the more dynamic structure of information exchange ; the increase is especially noticeable amongst certain categories of nodes (or users). Our findings suggest that the relationship between the structure of networks and their function in diffusing information is not as straightforward as some theoretical models of diffusion in networks imply.
AbstractEvaluating the effectiveness of nonpharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic is crucial to maximize the epidemic containment while minimizing the social and economic impact of these measures. However, this endeavor crucially relies on surveillance data publicly released by health authorities that can hide several limitations. In this article, we quantify the impact of inaccurate data on the estimation of the time-varying reproduction number$ R(t) $, a pivotal quantity to gauge the variation of the transmissibility originated by the implementation of different NPIs. We focus on Italy and Spain, two European countries among the most severely hit by the COVID-19 pandemic. For these two countries, we highlight several biases of case-based surveillance data and temporal and spatial limitations in the data regarding the implementation of NPIs. We also demonstrate that a nonbiased estimation of$ R(t) $could have had direct consequences on the decisions taken by the Spanish and Italian governments during the first wave of the pandemic. Our study shows that extreme care should be taken when evaluating intervention policies through publicly available epidemiological data and call for an improvement in the process of COVID-19 data collection, management, storage, and release. Better data policies will allow a more precise evaluation of the effects of containment measures, empowering public health authorities to take more informed decisions.
Evaluating the effectiveness of nonpharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic is crucial to maximize the epidemic containment while minimizing the social and economic impact of these measures. However, this endeavor crucially relies on surveillance data publicly released by health authorities that can hide several limitations. In this article, we quantify the impact of inaccurate data on the estimation of the time-varying reproduction number R(t), a pivotal quantity to gauge the variation of the transmissibility originated by the implementation of different NPIs. We focus on Italy and Spain, two European countries among the most severely hit by the COVID-19 pandemic. For these two countries, we highlight several biases of case-based surveillance data and temporal and spatial limitations in the data regarding the implementation of NPIs. We also demonstrate that a nonbiased estimation of R(t) could have had direct consequences on the decisions taken by the Spanish and Italian governments during the first wave of the pandemic. Our study shows that extreme care should be taken when evaluating intervention policies through publicly available epidemiological data and call for an improvement in the process of COVID-19 data collection, management, storage, and release. Better data policies will allow a more precise evaluation of the effects of containment measures, empowering public health authorities to take more informed decisions. ; Peer Reviewed ; Postprint (published version)
In the book The Essential Tension (1979) Thomas Kuhn described the conflict between tradition and innovation in scientific research—i.e., the desire to explore new promising areas, counterposed to the need to capitalize on the work done in the past. While it is probable that along their careers many scientists felt this tension, only few works have tried to quantify it. Here, we address this question by analyzing a large-scale dataset, containing all the papers published by the American Physical Society (APS) in 26 years, which allows for a better understanding of scientists' careers evolution in Physics. We employ the Physics and Astronomy Classification Scheme (PACS) present in each paper to map the scientific interests of 103,246 authors and their evolution along the years. Our results indeed confirm the existence of the "essential tension" with scientists balancing between exploring the boundaries of their area and exploiting previous work. In particular, we found that although the majority of physicists change the topics of their research, they stay within the same broader area thus exploring with caution new scientific endeavors. Furthermore, we quantify the flows of authors moving between different subfields and pinpoint which areas are more likely to attract or donate researchers to the other ones. Overall, our results depict a very distinctive portrait of the evolution of research interests in Physics and can help in designing specific policies for the future. ; This material is based upon work supported by, or in part by, the U.S. Army Research Laboratory and the U.S. Army Research Office under contract/grant number W911NF-18-1-0376. A.A. acknowledges support from Santander via the "Universities International Mobility Awards "program and of the FPI doctoral fellowship program from MINECO, Spain (grant FIS2014-55867-P). S.M. acknowledges partial financial support from the Agencia Estatal de Investigacion (AEI, Spain) and Fondo Europeo de Desarrollo Regional under Project PACSS Project No. RTI2018-093732-B-C22 (MCIU, AEI/FEDER,UE) and through the María de Maeztu Program for units of Excellence in R&D (MDM-2017-0711). Y.M. acknowledges partial support from the Government of Aragón, Spain through a grant to the group FENOL (E36-17R), by MINECO and FEDER funds (grant FIS2017-87519-P) and by Intesa Sanpaolo Innovation Center.
This article explores the growth of online mobilizations using data from the indignados (outraged) movement in Spain, which emerged under the influence of the revolution in Egypt and as a precursor to the global Occupy mobilizations. The data track Twitter activity around the protests that took place in May 2011, which led to the formation of camp sites in dozens of cities all over the country and massive daily demonstrations during the week prior to the elections of May 22. We reconstruct the network of tens of thousands of users and monitor their message activity for a month (April 25, 2011, to May 25, 2011). Using both the structure of the network and levels of activity in message exchange, we identify four types of users and analyze their role in the growth of the protest. Drawing from theories of online activism and research on information diffusion in networks, this article centers on the following two questions: How does protest information spread in online networks? And how do different actors contribute to the growth of activity? The article aims to inform the theoretical debate on whether digital technologies are changing the logic of collective action and to provide evidence of how new media facilitates the emergence of massive offline mobilizations.
Recently, the study of evolutionary games on networks has attracted great interest, focused mainly on the problem of the emergence of cooperation. A well studied framework for this problem is the Prisoner's Dilemma game on fixed, evolving or growing networks. In this paper we present a complete picture of the behavior of another important social dilemma, the Stag Hunt game, under an evolutionary preferential attachment model, in which the network grows according to the dynamical states of the elements of the system. We observe the emergence of a scale-free and hierarchical organization of the strategies according to connectivity classes as a by-product of the diffusion of cooperation in the network. Depending on the parametrization of the game dynamics, we find a smooth transition from cooperation to defection and a polymorphic state with simultaneous presence of cooperator and defector hubs, which is very unusual in coordination games. ; AS was supported in part by grants MOSAICO and Complexity-NET RESINEE (Ministerio de Ciencia e Innovación, Spain) and MODELICO-CM (Comunidad de Madrid, Spain). YM is supported by the Spanish MICINN through projects FIS2008-01240 and FIS2009-13364-C02-01 and by the Government of Arag´on (DGA) through a grant to the FENOL group. ; Publicado
Understanding the dynamics of social interactions is crucial to comprehend human behavior. The emergence of online social media has enabled access to data regarding people relationships at a large scale. Twitter, specifically, is an information oriented network, with users sharing and consuming information. In this work, we study whether users tend to be in contact with people interested in similar topics, i.e., if they are topically aligned. To do so, we propose an approach based on the use of hashtags to extract information topics from Twitter messages and model users' interests. Our results show that, on average, users are connected with other users similar to them. Furthermore, we show that topical alignment provides interesting information that can eventually allow inferring users' connectivity. Our work, besides providing a way to assess the topical similarity of users, quantifies topical alignment among individuals, contributing to a better understanding of how complex social systems are structured. ; FC and AS acknowledges support from Microsoft, Santander, CAPES, CNPq, and FAPESP Project 2015/01587-0. SM acknowledges support from the Ramón y Cajal Program by MINECO, Spain. YM and SM acknowledge support from the Government of Aragón, Spain through a grant to the group FENOL, by MINECO and FEDER funds (grant FIS2017-87519-P) and by the European Commission FET-Proactive Project Multiplex (grant 317532). SM also acknowledge the Spanish State Research Agency, through the María de Maeztu Program for Units of Excellence in R&D (MDM-2017-0711). ; Peer reviewed
Over the past 60 years, the Mycobacterium bovis bacille Calmette-Guérin (BCG) has been used worldwide to prevent tuberculosis (TB). However, BCG has shown a very variable efficacy in different trials, offering a wide range of protection in adults against pulmonary TB. One of the most accepted hypotheses to explain these inconsistencies points to the existence of a pre-existing immune response to antigens that are common to environmental sources of mycobacterial antigens and Mycobacterium tuberculosis. Specifically, two different mechanisms have been hypothesized to explain this phenomenon: the masking and the blocking effects. According to masking hypothesis, previous sensitization confers some level of protection against TB that masks vaccine's effects. In turn, the blocking hypothesis postulates that previous immune response prevents vaccine taking of a new TB vaccine. In this work we introduce a series of models to discriminate between masking and blocking mechanisms and address their relative likelihood. We apply our methodology to the data reported by BCG-REVAC clinical trials, which were specifically designed for studying BCG efficacy variability. Our results yield estimates that are consistent with high levels of blocking (41% in Manaus -95% CI [14-68]- and 96% in Salvador -95% CI [52-100]-). Moreover, we also show that masking does not play any relevant role in modifying vaccine's efficacy either alone or in addition to blocking. The quantification of these effects around a plausible model constitutes a relevant step towards impact evaluation of novel anti-tuberculosis vaccines, which are susceptible of being affected by similar effects, especially if applied on individuals previously exposed to mycobacterial antigens. ; SA was supported by the FPI program of the Government of Aragón, Spain. JS was supported by the program of Postdoctoral Scholarships for Excellence of the Sainte-Justine UHC Foundation and by the Merit scholarship program for foreign students (PBEEE) of the Fonds de Recherche of Quebec, ...
Nestedness has traditionally been used to detect assembly patterns in meta-communities and networks of interacting species. Attempts have also been made to uncover nested structures in international trade, typically represented as bipartite networks in which connections can be established between countries (exporters or importers) and industries. A bipartite representation of trade, however, inevitably neglects transactions between industries. To fully capture the organization of the global value chain, we draw on the World Input-Output Database and construct a multi-layer network in which the nodes are the countries, the layers are the industries, and links can be established from sellers to buyers within and across industries. We defne the buyers' and sellers' participation matrices in which the rows are the countries and the columns are all possible pairs of industries, and then compute nestedness based on buyers' and sellers' involvement in transactions between and within industries. Drawing on appropriate null models that preserve the countries' or layers' degree distributions in the original multi-layer network, we uncover variations of country- and transaction-based nestedness over time, and identify the countries and industries that most contributed to nestedness. We discuss the implications of our fndings for the study of the international production network and other real-world systems. ; L.G.A.A. acknowledges FAPESP (Grant No. 2016/16987-7) for fnancial support. F.A.R. acknowledges CNPq (Grant No. 305940/2010-4) and FAPESP (Grants No. 2016/25682-5 and grants 2013/07375-0). Y.M. acknowledges support from the Government of Aragón, Spain through grant (E36-17R, FENOL), by MINECO and FEDER funds (grant FIS2014-55867-P) and by the European Commission FET-Proactive Project Dolfns (grant 640772).
The vast amount of research devoted to public goods games has shown that contributions may be dramatically affected by varying framing conditions. This is particularly relevant in the context of donations to charities and non-governmental organizations. Here, we design a multiple public goods experiment by introducing five types of funds, each differing in the fraction of the contribution that is donated to a charity. We found that people contribute more to public goods when the associated social donations are presented as indirect rather than as direct donations. At the same time, the fraction of the donations devoted to charity is not affected by the framing. We have also found that, on average, women contribute to public goods and donate to charity significantly more than men. These findings are of potential interest to the design of social investment tools, in particular for charities to ask for better institutional designs from policy makers. ; This work was partially supported by MINECO (Spain) and FEDER funds through grant no. FIS2017-87519-P (Y.M.) and FJCI-2016-28276 (A.A.); by Ministerio de Ciencia, Innovación y Universidades/FEDER (Spain/UE) through grant no. PGC2018-098186-B-I00 (BASIC) (A.S. and J.C.); by Comunidad de Madrid under grant no. PRACTICO-CM and by Comunidad de Madrid/Universidad Carlos III de Madrid under grant no. CAVTIONS-CM-UC3M (A.S.); by Comunidad de Aragón (Spain) through grant no. E36-20R to FENOL (C.G.L. and Y.M.); by the EU through FET-Proactive Project MULTIPLEX (contract no. 317532, Y.M.) and FET-Proactive Project DOLFINS (contract no. 640772, C.G.L., Y.M. and A.S.), and by the Spanish State Research Agency and FEDER funds, through the María de Maeztu Program for Units of Excellence in R&D (MDM-2017-0711, S.M.) and under the PACSS grant no. (RTI2018-093732-B-C22, S.M.). ; Peer reviewed
