19 research outputs found
The Impact of Social Curiosity on Information Spreading on Networks
Most information spreading models consider that all individuals are identical
psychologically. They ignore, for instance, the curiosity level of people,
which may indicate that they can be influenced to seek for information given
their interest. For example, the game Pok\'emon GO spread rapidly because of
the aroused curiosity among users. This paper proposes an information
propagation model considering the curiosity level of each individual, which is
a dynamical parameter that evolves over time. We evaluate the efficiency of our
model in contrast to traditional information propagation models, like SIR or
IC, and perform analysis on different types of artificial and real-world
networks, like Google+, Facebook, and the United States roads map. We present a
mean-field approach that reproduces with a good accuracy the evolution of
macroscopic quantities, such as the density of stiflers, for the system's
behavior with the curiosity. We also obtain an analytical solution of the
mean-field equations that allows to predicts a transition from a phase where
the information remains confined to a small number of users to a phase where it
spreads over a large fraction of the population. The results indicate that the
curiosity increases the information spreading in all networks as compared with
the spreading without curiosity, and that this increase is larger in spatial
networks than in social networks. When the curiosity is taken into account, the
maximum number of informed individuals is reached close to the transition
point. Since curious people are more open to a new product, concepts, and
ideas, this is an important factor to be considered in propagation modeling.
Our results contribute to the understanding of the interplay between diffusion
process and dynamical heterogeneous transmission in social networks.Comment: 8 pages, 5 figure
Automatização no fact-checking: : pensando a prática de verificação a partir de agrupamentos semânticos de frases verificadas pelo Projeto Comprova
O artigo promove uma reflexão teórica sobre os limites da prática de checagem de fatos (fact-checking) frente a um cenário de desinformação, tanto pela perspectiva computacional quanto pelo princípio da objetividade jornalística. O estudo analisa 2031 mensagens originais de usuários enviadas ao Projeto Comprova, coalizão entre dezenas de veículos de mídia brasileiros, utilizando técnicas de aprendizado de máquina, mineração de textos e modelagem de redes complexas. O modelo computacional investiga padrões linguísticos que possam auxiliar no desenvolvimento de ferramentas de automatização para identificar conteúdos potencialmente enganosos na rede a partir de agrupamentos semânticos. Os achados servem à problematização de mudanças estruturais do jornalismo em um ecossistema midiático que exige respostas multidisciplinares.The paper promotes a theoretical reflection on the limits of the fact-checking practice in a disinformation context, both from the computational perspective and the journalistic principle of objectivity. The study analyzes 2031 original messages sent from users to the Comprova Project, a coalition of dozens of Brazilian media outlets, using machine learning techniques, text mining and complex network modeling. The computational model investigates linguistic patterns that can help in the development of automation tools to identify potentially misleading content on the web based on semantic groupings. The findings serve to problematize structural changes in journalism in a media ecosystem that requires multidisciplinary responses
The Impact of Social Curiosity on Information Spreading on Networks
Most information spreading models consider that all individuals are identical psychologically. They ignore, for instance, the curiosity level of people, which may indicate that they can be influenced to seek for information given their interest. For example, the game Pokemon GO spread rapidly because of the aroused curiosity among users. This paper proposes an information propagation model considering the curiosity level of each individual, which is a dynamical parameter that evolves over time. We evaluate the efficiency of our model in contrast to traditional information propagation models, like SIR or IC, and perform analysis on different types of artificial and real-world networks, like Google+, Facebook, and the United States roads map. We present a mean-field approach that reproduces with a good accuracy the evolution of macroscopic quantities, such as the density of stiflers, for the system's behavior with the curiosity. We also obtain an analytical solution of the mean-field equations that allows to predicts a transition from a phase where the information remains confined to a small number of users to a phase where it spreads over a large fraction of the population. The results indicate that the curiosity increases the information spreading in all networks as compared with the spreading without curiosity, and that this increase is larger in spatial networks than in social networks. When the curiosity is taken into account, the maximum number of informed individuals is reached close to the transition point. Since curious people are more open to a new product, concepts, and ideas, this is an important factor to be considered in propagation modeling. Our results contribute to the understanding of the interplay between diffusion process and dynamical heterogeneous transmission in social networks.Instituto de Física de Líquidos y Sistemas Biológico
Global Fire Season Severity Analysis and Forecasting
Global fire activity has a huge impact on human lives. In recent years, many
fire models have been developed to forecast fire activity. They present good
results for some regions but require complex parametrizations and input
variables that are not easily obtained or estimated. In this paper, we evaluate
the possibility of using historical data from 2003 to 2017 of active fire
detections (NASA's MODIS MCD14ML C6) and time series forecasting methods to
estimate global fire season severity (FSS), here defined as the accumulated
fire detections in a season. We used a hexagonal grid to divide the globe, and
we extracted time series of daily fire counts from each cell. We propose a
straightforward method to estimate the fire season lengths. Our results show
that in 99% of the cells, the fire seasons have lengths shorter than seven
months. Given this result, we extracted the fire seasons defined as time
windows of seven months centered in the months with the highest fire
occurrence. We define fire season severity (FSS) as the accumulated fire
detections in a season. A trend analysis suggests a global decrease in length
and severity. Since FSS time series are concise, we used the
monthly-accumulated fire counts (MA-FC) to train and test the seven forecasting
models. Results show low forecasting errors in some areas. Therefore we
conclude that many regions present predictable variations in the FSS
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Spatiotemporal data analysis with chronological networks
The number of spatiotemporal data sets has increased rapidly in the last years, which demands robust and fast methods to extract information from this kind of data. Here, we propose a network-based model, called Chronnet, for spatiotemporal data analysis. The network construction process consists of dividing a geometric space into grid cells represented by nodes connected chronologically. Strong links in the network represent consecutive recurrent events between cells. The chronnet construction process is fast, making the model suitable to process large data sets. Using artificial and real data sets, we show how chronnets can capture data properties beyond simple statistics, like frequent patterns, spatial changes, outliers, and spatiotemporal clusters. Therefore, we conclude that chronnets represent a robust tool for the analysis of spatiotemporal data sets
Spatiotemporal data analysis with chronological networks
The amount and size of spatiotemporal data sets from different domains have
been rapidly increasing in the last years, which demands the development of
robust and fast methods to analyze and extract information from them. In this
paper, we propose a network-based model for spatiotemporal data analysis called
chronnet. It consists of dividing a geometrical space into grid cells
represented by nodes connected chronologically. The main goal of this model is
to represent consecutive recurrent events between cells with strong links in
the network. This representation permits the use of network science and
graphing mining tools to extract information from spatiotemporal data. The
chronnet construction process is fast, which makes it suitable for large data
sets. In this paper, we describe how to use our model considering artificial
and real data. For this purpose, we propose an artificial spatiotemporal data
set generator to show how chronnets capture not just simple statistics, but
also frequent patterns, spatial changes, outliers, and spatiotemporal clusters.
Additionally, we analyze a real-world data set composed of global fire
detections, in which we describe the frequency of fire events, outlier fire
detections, and the seasonal activity, using a single chronnet
A Inteligência Artificial e os desafios da Ciência Forense Digital no século XXI
Digital Forensics emerged from the need to perform forensic tasks in the digital age. Its most recent challenges are related to the popularization of social media and were intensified by the advance of Artificial Intelligence. The generation of massive social media data made forensic analyses more complex, mainly due to improvements in computational models able to artificially create highly realistic content. Because of this, Artificial Intelligence techniques have been studied and used to process the massive volume of information. This paper discusses the challenges and opportunities associated with such methods and provides real case examples, as well as the problems that arise when using these approaches in sensitive contexts and how the scientific community has approached these topics. Finally, it draws future research paths to be explored.A Ciência Forense Digital surgiu da necessidade de tratar problemas forenses na era digital. Seu mais recente desafio está relacionado ao surgimento das mídias sociais, intensificado pelos avanços da Inteligência Artificial. A produção massiva de dados nas mídias sociais tornou a análise forense mais complexa, especialmente pelo aperfeiçoamento de modelos computacionais capazes de gerar conteúdo artificial com alto realismo. Assim, tem-se a necessidade da aplicação de técnicas de Inteligência Artificial para tratar esse imenso volume de informação. Neste artigo, apresentamos desafios e oportunidades associados à aplicação dessas técnicas, além de fornecer exemplos de seu uso em situações reais. Discutimos os problemas que surgem em contextos sensíveis e como a comunidade científica tem abordado esses tópicos. Por fim, delineamos futuros caminhos de pesquisa a serem explorados