Polarization in social movements on Twitter

Online social networks have become a parallel universe of socialization from which interactive dynamics are generated until recently unknown. From this framework of online communication, different social movements have reached a greater boom and spread. On Twitter these social movements have as a characteristic feature a hashtag (#) that allows cross-cutting the conversation. Although it has become an instrument that makes it possible to agglutinate the conversation in a massive way, we are also emerging voices that warn of the growing manipulation around these forms of socialization. Social workers must pay attention and investigate these means of digital activism in order to understand and avoid these possible manipulations. In this communication we analyze in a longitudinal way different social movements on Twitter. To this end, social movements that have emerged during the last year about phenomena related to the empowerment of women (#metoo, #yositecreo, etc ...), cooperation against natural disasters (#mallorca) and social demands (# pensions). To identify leaderships, detect communities and measure social distances, social network analysis has been used as well as certain algorithms. For the analysis of the conversations, language analysis techniques have been used to optimize the combination of the most common words, giving rise to conversation patterns around certain force ideas. The results achieved show a high level of emotivity in the way of interacting and a significant pattern of polarization in conversations about these movements that is the seed of conflicts and radicalization. The key lies in the way we congregate and converse in networks around those with whom we share our perspectives and opinions about the world. This tendency towards homophily provokes a biased and monolithic vision of reality. In order to counteract these conflicts we must raise the level of self-awareness on the level of homophilia in the way we congregate and converse on online social networks and intervene to increase tolerance for diversity.Online social networks have become a parallel universe of socialization from which interactive dynamics are generated until recently unknown. From this framework of online communication, different social movements have reached a greater boom and spread. On Twitter these social movements have as a characteristic feature a hashtag (#) that allows cross-cutting the conversation. Although it has become an instrument that makes it possible to agglutinate the conversation in a massive way, we are also emerging voices that warn of the growing manipulation around these forms of socialization. Social workers must pay attention and investigate these means of digital activism in order to understand and avoid these possible manipulations. In this communication we analyze in a longitudinal way different social movements on Twitter. To this end, social movements that have emerged during the last year about phenomena related to the empowerment of women (#metoo, #yositecreo, etc ...), cooperation against natural disasters (#mallorca) and social demands (# pensions). To identify leaderships, detect communities and measure social distances, social network analysis has been used as well as certain algorithms. For the analysis of the conversations, language analysis techniques have been used to optimize the combination of the most common words, giving rise to conversation patterns around certain force ideas.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Big social data to organize the mobilization of citizenship

The cities around the world are concentrating the social life around the world. Nowadays our manner of socialization is changing. The emergence of social networking sites, have enabled greater social connectivity, which in turn has allowed the distances between people to be reduced and interactive dynamics to be generated until recently unpublished. This social phenomenon generates large amounts of data, attracting the interest of researchers and academics and is called Big Social Data. Access to these massive amounts of data makes it possible to detect patterns of behavior that are not visible to the naked eye, simply because they can radiate unknown connections to the naked eye. In addition, it is a type of data that, being more or less accessible, in front of others, does not bother the citizens because they are captured without people feeling observed, granting a very important spontaneity in the collection of data. Despite the many expectations it generates, we must qualify that Big Data does not explain things by itself. The voices that warn that Big Data has a lack have been intensified: explain why, the reasons why users of services do what they do, the emotions, feelings and realities that determine their behaviors and attitudes. To cover this gap, Thick Data is needed, that is, the "dense description" of information as a method to analyze phenomena, cultures and relationships between people. In short, it is about understanding that Thick Data and Big Data are complementary tools that have to be used in a balanced way. In the present communication we will show different examples of research with Big Social Data made so far. In that sense, we have investigated the capacity of organization and mobilization that can help to face social emergencies as disasters and catastrophes. As example we will demonstrate how people organized to find victims facing a terrorism attack happened at Strasbourg (France).Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Parallel 3-D marine controlled-source electromagnetic modelling using high-order tetrahedral Nédélec elements

We present a parallel and high-order Nédélec finite element solution for the marine controlled-source electromagnetic (CSEM) forward problem in 3-D media with isotropic conductivity. Our parallel Python code is implemented on unstructured tetrahedral meshes, which support multiple-scale structures and bathymetry for general marine 3-D CSEM modelling applications. Based on a primary/secondary field approach, we solve the diffusive form of Maxwell’s equations in the low-frequency domain. We investigate the accuracy and performance advantages of our new high-order algorithm against a low-order implementation proposed in our previous work. The numerical precision of our high-order method has been successfully verified by comparisons against previously published results that are relevant in terms of scale and geological properties. A convergence study confirms that high-order polynomials offer a better trade-off between accuracy and computation time. However, the optimum choice of the polynomial order depends on both the input model and the required accuracy as revealed by our tests. Also, we extend our adaptive-meshing strategy to high-order tetrahedral elements. Using adapted meshes to both physical parameters and high-order schemes, we are able to achieve a significant reduction in computational cost without sacrificing accuracy in the modelling. Furthermore, we demonstrate the excellent performance and quasi-linear scaling of our implementation in a state-of-the-art high-performance computing architecture.This project has received funding from the European Union's Horizon 2020 programme under the Marie Sklodowska-Curie grant agreement No. 777778. Furthermore, the research leading to these results has received funding from the European Union's Horizon 2020 programme under the ChEESE Project (https://cheese-coe.eu/ ), grant agreement No. 823844. In addition, the authors would also like to thank the support of the Ministerio de Educación y Ciencia (Spain) under Projects TEC2016-80386-P and TIN2016-80957-P. The authors would like to thank the Editors-in-Chief and to both reviewers, Dr. Martin Cuma and Dr. Raphael Rochlitz, for their valuable comments and suggestions which helped to improve the quality of the manuscript. This work benefited from the valuable suggestions, comments, and proofreading of Dr. Otilio Rojas (BSC). Last but not least, Octavio Castillo-Reyes thanks Natalia Gutierrez (BSC) for her support in CSEM modeling with BSIT.Peer ReviewedPostprint (author's final draft

Improving edge finite element assembly for geophysical electromagnetic modelling on shared-memory architectures

This work presents a set of node-level optimizations to perform the assembly of edge finite element matrices that arise in 3D geophysical electromagnetic modelling on shared-memory architectures. Firstly, we describe the traditional and sequential assembly approach. Secondly, we depict our vectorized and shared-memory strategy which does not require any low level instructions because it is based on an interpreted programming language, namely, Python. As a result, we obtained a simple parallel-vectorized algorithm whose runtime performance is considerably better than sequential version. The set of optimizations have been included to the work-flow of the Parallel Edge-based Tool for Geophysical Electromagnetic Modelling (PETGEM) which is developed as open-source at the Barcelona Supercomputing Center. Finally, we present numerical results for a set of tests in order to illustrate the performance of our strategy.This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 644202. The research leading to these results has received funding from the European Union's Horizon 2020 Programme (2014-2020) and from Brazilian Ministry of Science, Technology and Innovation through Rede Nacional de Pesquisa (RNP) under the HPC4E Project (www.hpc4e.eu), grant agreement No. 689772. Authors gratefully acknowledge the support from the Mexican National Council for Science and Technology (CONACYT). All numerical tests were performed on the MareNostrum supercomputer of the Barcelona Supercomputing Center - Centro Nacional de Supercomputación (www.bsc.es).Peer ReviewedPostprint (author's final draft

Low power hydrogen gas sensors using electrodeposited PdNi-Si Schottky diodes

The use of electrodeposited PdNi-Si Schottky barriers as low power Hydrogen sensors is investigated. The Palladium content of the film causes the Hydrogen molecules to dissociate and be absorbed by the film, changing the metal work function and Schottky barrier current. In this work we show that electrodeposited Pd(Ni)-Si Schottky barriers exhibit very low reverse bias currents compared to evaporated Schottky diodes. The Schottky diodes were fabricated on 0.5-1.5 ohmcm 100 n-type Si by electrodeposition of PdNi followed by evaporation of Aluminium contact pads. Electrical measurements at different Hydrogen pressures were performed on back to back Schottky diodes in a vacuum chamber using pure Nitrogen and a 5% Hydrogen-Nitrogen mixture. Very low currents of 1nA were measured in the absence of Hydrogen. Large increases in the currents, upto a factor of 100, were observed upon exposure to different Hydrogen partial pressures. A back to back configuration forms a device that draws extremely low power when idle. The low idle current, simplicity of the fabrication process and ability to easily integrate with conventional electronics proves the suitability of electrodeposited PdNi-Si Schottky barriers as low power Hydrogen sensors