Resumo

Neste artigo, discutimos os resultados do monitoramento das eleições brasileiras de 2022 nas plataformas You Tube, Instagram, Tik Tok, Twitter/X e Facebook, a partir das affordances presentes em cada uma delas. Com base na revisão bibliográfica, propomos uma tipologia de affordances, classificando-as de acordo com sua aplicação em campanhas e fluxos de informações políticas. Em seguida, apresentamos os procedimentos metodológicos da coleta realizada, observando as affordances visíveis na arquitetura algorítmica das plataformas. Ao analisar os dados, identificamos e discutimos as affordances em cada plataforma e as formas como se vinculam aos conteúdos políticos. Concluímos apontando como se configuram as estratégias específicas para cada ambiente, além de evidenciar a construção de uma narrativa mais ampla e tecida por uma maior gama de atores, na qual as plataformas participam de diferentes maneiras da sua composição. 

Palavras-Chaves: Eleições Brasileiras de 2022; Plataformas; Affordances; Métodos Digitais; Comunicação Política.

DOI: https://doi.org/10.22409/rmc.v18i1.59797

Abstract

High-dimensional data, characterized by many features, can be difficult to visualize effectively. Dimensionality reduction techniques, such as PCA, UMAP, and t-SNE, address this challenge by projecting the data into a lower-dimensional space while preserving important relationships. TopoMap is another technique that excels at preserving the underlying structure of the data, leading to interpretable visualizations. In particular, TopoMap maps the high-dimensional data into a visual space, guaranteeing that the 0-dimensional persistence diagram of the Rips filtration of the visual space matches the one from the high-dimensional data. However, the original TopoMap algorithm can be slow and its layout can be too sparse for large and complex datasets. In this paper, we propose three improvements to TopoMap: 1) a more space-efficient layout, 2) a significantly faster implementation, and 3) a novel TreeMap-based representation that makes use of the topological hierarchy to aid the exploration of the projections. These advancements make TopoMap, now referred to as TopoMap++, a more powerful tool for visualizing high-dimensional data which we demonstrate through different use case scenarios.

Keywords: Topological data analysis, Computational topology, High-dimensional data, Projection.

DOI: https://doi.org/10.1109/TVCG.2024.3456365

Abstract

Intelligent applications supported by Machine Learning have achieved remarkable performance rates for a wide range of tasks in many domains. However, understanding why a trained algorithm makes a particular decision remains problematic. Given the growing interest in the application of learning-based models, some concerns arise in the dealing with sensible environments, which may impact users’ lives. The complex nature of those models’ decision mechanisms makes them the so-called ‘‘black boxes,’’ in which the understanding of the logic behind automated decision-making processes by humans is not trivial. Furthermore, the reasoning that leads a model to provide a specific prediction can be more important than performance metrics, which introduces a trade-off between interpretability and model accuracy. Explaining intelligent computer decisions can be regarded as a way to justify their reliability and establish trust. In this sense, explanations are critical tools that verify predictions to discover errors and biases previously hidden within the models’ complex structures, opening up vast possibilities for more responsible applications. In this review, we provide theoretical foundations of Explainable Artificial Intelligence (XAI), clarifying diffuse definitions and identifying research objectives, challenges, and future research lines related to turning opaque machine learning outputs into more transparent decisions. We also present a careful overview of the state-of-the-art explainability approaches, with a particular analysis of methods based on feature importance, such as the well-known LIME and SHAP. As a result, we highlight practical applications of the successful use of XAI.

Keywords: Black-box models, explainability, explainable machine learning, interpretability, interpretable machine learning.

DOI: https://www.doi.org/10.1109/ACCESS.2024.3409843

Abstract

Crime prediction is a critical research area for enhancing public safety and optimizing law enforcement resource allocation, and machine learning techniques have had a significant impact in this field. Traditional machine learning models have long struggled to capture complex crime patterns, primarily due to the intricate interdependence of spatial and temporal data. However, recent advancements in machine learning, particularly with Graph Neural Networks (GNNs), offer a new perspective. GNNs have demonstrated remarkable success in various applications and they can also play a significant role in crime analysis and prediction. Therefore, in this work, we explore such a potential by examining two distinct spatiotemporal GNN architectures, namely Dynamic Self-Attention Network (DySAT) and Evolving Graph Convolutional Network (EvolveGCN), assessing and comparing their effectiveness for crime prediction. Moreover, we propose a data modeling framework that integrates crime, street map graphs, and urban data, which is fundamental to properly train the GNN models. As far as we know, there is no consolidated methodology to integrate those three modalities of data, being a relevant contribution of this work. Our findings underscore the effectiveness of GNNs in crime prediction tasks, offering valuable insights for researchers and practitioners in the field of crime prevention and public safety enhancement.

Keywords: Data modeling · Graph neural networks · Crime prediction

DOI: https://doi.org/10.1007/978-3-031-79035-5_26

Abstract

Spatiotemporal urban data gathered by public security departments holds immense potential for in-depth analysis, enhancing decision-making in areas such as crime prevention and patrolling strategies. However, extracting meaningful patterns from vast and complex spatiotemporal datasets presents a considerable challenge in the field of big data analytics. We introduce a novel visualization-assisted tool tailored for handling massive spatiotemporal urban datasets, with a specific focus on public security data. At the core of this tool are spatial graph vertex ordering algorithms that perform dimensionality reduction on the vertices’ locations. To make this tool practical for handling massive spatiotemporal datasets, we present efficient preprocessing techniques. These techniques are carefully crafted to distill and represent urban spatiotemporal datasets, ensuring efficient data exploration. The effectiveness of the proposed solution is validated through case studies using real datasets from the Military Police of the State of Alagoas - Brazil. We demonstrate the effectiveness of the proposed solutions, showcasing the versatility of the visual tool in accomplishing various relevant analytical tasks.

Keywords: 

DOI: https://doi.org/10.1109/SIBGRAPI62404.2024.10716319

Abstract

Crime in Brazil presents increasing challenges for public management, with impunity being a significant underlying factor. Understanding impunity is crucial for improving the criminal justice system. In this context, the present study aims to investigate and develop inferential and predictive methods for analyzing data on intentional homicides in the state of São Paulo, focusing on impunity as the delay or lack of conversion of police reports into formal investigations. Additionally, the study considers the spatial correlation between municipalities and temporal factors. To achieve this, models such as Intrinsic Conditional Autoregressive (ICAR), Besag-York-Mollié, and Multinomial Regression with a Dirichlet prior in the Bayesian framework are explored. The research aims to contribute to public authorities insights into the patterns of impunity over time and space in São Paulo, offering resources that can enhance state decision-making and ultimately benefit society.

Keywords: Crime data; Urban violence; Multinomial regression.

Abstract

Urban analytics uses extensive datasets with diverse urban information to simulate, predict trends, and uncover complex city patterns. While this data enables advanced analysis, it also presents challenges due to its granularity, heterogeneity, and multimodality. To address these challenges, visual analytic tools have been developed to support the exploration of latent representations of fused heterogeneous/multimodal data, discretized at a street level of detail. However, visualization-assisted tools seldom explore whether analyzing fused data offers deeper insights than examining each data source independently within an integrated visualization framework. In this work, we developed a visualization-assisted framework to analyze whether fused latent data representations are more effective than separate representations in uncovering patterns from dynamic and static urban data. The analysis shows that combined latent representations tend to produce more structured patterns, although the separated representations are beneficial in particular cases.

Keywords: Human-centered computing—Visualization—Visualization techniques—Treemaps; Human-centered computing— Visualization—Visualization design and evaluation methods

Abstract

The emergence of distinct machine learning explanation methods has leveraged a number of new issues to be investigated. The disagreement problem is one such issue, as there may be scenarios where the output of different explanation methods disagree with each other. Although understanding how often, when, and where explanation methods agree or disagree is important to increase confidence in the explanations, few works have been dedicated to investigating such a problem. In this work, we proposed Visagreement, a visualization tool designed to assist practitioners in investigating the disagreement problem. Visagreement builds upon metrics to quantitatively compare and evaluate explanations, enabling visual resources to uncover where and why methods mostly agree or disagree. The tool is tailored for tabular data with binary classification and focuses on local feature importance methods. In the provided use cases, Visagreement turned out to be effective in revealing, among other phenomena, how disagreements relate to the quality of the explanations and machine learning model accuracy, thus assisting users in deciding where and when to trust explanations. To assess the effectiveness and practical utility of Visagreement, we conducted an evaluation involving four experts. These experts assessed the tool’s Effectiveness, Usability, and Impact on Decision-Making. The experts confirm the Visagreement tool’s effectiveness and user-friendliness, making it a valuable asset for analyzing and exploring (dis)agreements.

Keywords: Machine learning, Data models, System applications and experience, Simulation, Modeling, Visualization