Fake News and Post-truth: Numerical Simulations of Information Diffusion in Social Networks

The year 2016 was crucial in terms of the use of social networks as tools for disseminating information for political purposes. The election of candidate Donald Trump for the presidency of the United States of America lit a warning signal about the influence that the information disseminated through social networks exerted in the choice of candidates by the American people. Since then, researchers from different areas have focused on the topic, which involves different aspects: computing, social sciences, mathematics, among others. Therefore, the object of study of this work is the phenomenon of behavioral changes brought about by the new social relationships established by digital social networks, under the scope of the spread of fake news through them. To guarantee the intended study, the general objective was to adapt mathematical models consisting of ordinary differential equations for the dissemination of information on social networks for the spread of fake news. As specific objectives, the contribution of the mathematical models proposed in the mitigation, through algorithms, of the spread of false or distorted information was discussed, as well as the discussion on the concepts of fake news and post-truth from a social point of view, in a way that individuals can also distinguish true information from disinformation through individual interpretation tools. As a research methodology, bibliographic research was chosen and a systematic literature review was carried out, to consider published works on the proposed research object. For the numerical simulations, a numerical code was developed in MATLAB, which was able to carry out the desirable experiments. It is concluded that innovation diffusion models can adapt to fake news dissemination models. However, such models are not able to robustly simulate the mitigation of fake news

The free boundary problem describing information diffusion in online social networks

In this paper we consider a free boundary problem for a reactiondiffusion logistic equation with a time-dependent growth rate. Such a problem arises in the modeling of information diffusion in online social networks, with the free boundary representing the spreading front of news among users. We present several sharp thresholds for information diffusion that either lasts forever or suspends in finite time. In the former case, we give the asymptotic spreading speed which is determined by a corresponding elliptic equation

Homophily and Contagion Are Generically Confounded in Observational Social Network Studies

We consider processes on social networks that can potentially involve three factors: homophily, or the formation of social ties due to matching individual traits; social contagion, also known as social influence; and the causal effect of an individual's covariates on their behavior or other measurable responses. We show that, generically, all of these are confounded with each other. Distinguishing them from one another requires strong assumptions on the parametrization of the social process or on the adequacy of the covariates used (or both). In particular we demonstrate, with simple examples, that asymmetries in regression coefficients cannot identify causal effects, and that very simple models of imitation (a form of social contagion) can produce substantial correlations between an individual's enduring traits and their choices, even when there is no intrinsic affinity between them. We also suggest some possible constructive responses to these results.Comment: 27 pages, 9 figures. V2: Revised in response to referees. V3: Ditt

Development of a GPGPU accelerated tool to simulate advection-reaction-diffusion phenomena in 2D

Computational models are powerful tools to the study of environmental systems, playing a fundamental role in several fields of research (hydrological sciences, biomathematics, atmospheric sciences, geosciences, among others). Most of these models require high computational capacity, especially when one considers high spatial resolution and the application to large areas. In this context, the exponential increase in computational power brought by General Purpose Graphics Processing Units (GPGPU) has drawn the attention of scientists and engineers to the development of low cost and high performance parallel implementations of environmental models. In this research, we apply GPGPU computing for the development of a model that describes the physical processes of advection, reaction and diffusion. This presentation is held in the form of three self-contained articles. In the first one, we present a GPGPU implementation for the solution of the 2D groundwater flow equation in unconfined aquifers for heterogenous and anisotropic media. We implement a finite difference solution scheme based on the Crank- Nicolson method and show that the GPGPU accelerated solution implemented using CUDA C/C++ (Compute Unified Device Architecture) greatly outperforms the corresponding serial solution implemented in C/C++. The results show that accelerated GPGPU implementation is capable of delivering up to 56 times acceleration in the solution process using an ordinary office computer. In the second article, we study the application of a diffusive-logistic growth (DLG) model to the problem of forest growth and regeneration. The study focuses on vegetation belonging to preservation areas, such as riparian buffer zones. The study was developed in two stages: (i) a methodology based on Artificial Neural Network Ensembles (ANNE) was applied to evaluate the width of riparian buffer required to filter 90% of the residual nitrogen; (ii) the DLG model was calibrated and validated to generate a prognostic of forest regeneration in riparian protection bands considering the minimum widths indicated by the ANNE. The solution was implemented in GPGPU and it was applied to simulate the forest regeneration process for forty years on the riparian protection bands along the Ligeiro river, in Brazil. The results from calibration and validation showed that the DLG model provides fairly accurate results for the modelling of forest regeneration. In the third manuscript, we present a GPGPU implementation of the solution of the advection-reaction-diffusion equation in 2D. The implementation is designed to be general and flexible to allow the modeling of a wide range of processes, including those with heterogeneity and anisotropy. We show that simulations performed in GPGPU allow the use of mesh grids containing more than 20 million points, corresponding to an area of 18,000 km? in a standard Landsat image resolution.Os modelos computacionais s?o ferramentas poderosas para o estudo de sistemas ambientais, desempenhando um papel fundamental em v?rios campos de pesquisa (ci?ncias hidrol?gicas, biomatem?tica, ci?ncias atmosf?ricas, geoci?ncias, entre outros). A maioria desses modelos requer alta capacidade computacional, especialmente quando se considera uma alta resolu??o espacial e a aplica??o em grandes ?reas. Neste contexto, o aumento exponencial do poder computacional trazido pelas Unidades de Processamento de Gr?ficos de Prop?sito Geral (GPGPU) chamou a aten??o de cientistas e engenheiros para o desenvolvimento de implementa??es paralelas de baixo custo e alto desempenho para modelos ambientais. Neste trabalho, aplicamos computa??o em GPGPU para o desenvolvimento de um modelo que descreve os processos f?sicos de advec??o, rea??o e difus?o. Esta disserta??o ? apresentada sob a forma de tr?s artigos. No primeiro, apresentamos uma implementa??o em GPGPU para a solu??o da equa??o de fluxo de ?guas subterr?neas 2D em aqu?feros n?o confinados para meios heterog?neos e anisotr?picos. Foi implementado um esquema de solu??o de diferen?as finitas com base no m?todo Crank- Nicolson e mostramos que a solu??o acelerada GPGPU implementada usando CUDA C / C ++ supera a solu??o serial correspondente implementada em C / C ++. Os resultados mostram que a implementa??o acelerada por GPGPU ? capaz de fornecer acelera??o de at? 56 vezes no processo da solu??o usando um computador de escrit?rio comum. No segundo artigo estudamos a aplica??o de um modelo de crescimento log?stico difusivo (DLG) ao problema de crescimento e regenera??o florestal. O estudo foi desenvolvido em duas etapas: (i) Aplicou-se uma metodologia baseada em Comites de Rede Neural Artificial (ANNE) para avaliar a largura da faixa de prote??o rip?ria necess?ria para filtrar 90% do nitrog?nio residual; (ii) O modelo DLG foi calibrado e validado para gerar um progn?stico de regenera??o florestal em faixas de prote??o rip?rias considerando as larguras m?nimas indicadas pela ANNE. A solu??o foi implementada em GPGPU e aplicada para simular o processo de regenera??o florestal para um per?odo de quarenta anos na faixa de prote??o rip?ria ao longo do rio Ligeiro, no Brasil. Os resultados da calibra??o e valida??o mostraram que o modelo DLG fornece resultados bastante precisos para a modelagem de regenera??o florestal. No terceiro artigo, apresenta-se uma implementa??o em GPGPU para solu??o da equa??o advec??o-rea??o-difus?o em 2D. A implementa??o ? projetada para ser geral e flex?vel para permitir a modelagem de uma ampla gama de processos, incluindo caracter?sticas como heterogeneidade e anisotropia do meio. Neste trabalho mostra-se que as simula??es realizadas em GPGPU permitem o uso de malhas contendo mais de 20 milh?es de pontos (vari?veis), correspondendo a uma ?rea de 18.000 km? em resolu??o de 30m padr?o das imagens Landsat

Complex and Adaptive Dynamical Systems: A Primer

An thorough introduction is given at an introductory level to the field of quantitative complex system science, with special emphasis on emergence in dynamical systems based on network topologies. Subjects treated include graph theory and small-world networks, a generic introduction to the concepts of dynamical system theory, random Boolean networks, cellular automata and self-organized criticality, the statistical modeling of Darwinian evolution, synchronization phenomena and an introduction to the theory of cognitive systems. It inludes chapter on Graph Theory and Small-World Networks, Chaos, Bifurcations and Diffusion, Complexity and Information Theory, Random Boolean Networks, Cellular Automata and Self-Organized Criticality, Darwinian evolution, Hypercycles and Game Theory, Synchronization Phenomena and Elements of Cognitive System Theory.Comment: unformatted version of the textbook; published in Springer, Complexity Series (2008, second edition 2010