Modeling and Assessing the Temporal Behavior of Emotional and Depressive User Interactions on Social Networks

Giuntini, Felipe T.; Moraes, Kaue L. de; Cazzolato, Mirela T.; Kirchner, Luziane de Fátima; Reis, Maria de Jesus Dutra dos; Traina, Agma J. M.; Campbell, Andrew T.; Ueyama, Jó

doi:10.1109/access.2021.3091801

Cited by 12 publications

(5 citation statements)

References 47 publications

(47 reference statements)

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“…Giuntini et al [59,60] assessed and modeled the temporal behavior of emotional and depressive user interactions on social networks. They modeled user interactions using complex networks and grouped them using the Clauset-Newman-Moore greedy modularity maximization.…”

Section: Related Workmentioning

confidence: 99%

Emotion Recognition System Based on Two-Level Ensemble of Deep-Convolutional Neural Network Models

et al. 2023

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Emotions play a crucial role in human interaction and healthcare. This study introduces an automatic emotion recognition system based on deep learning using electroencephalogram signals. A lightweight pyramidal one-dimensional convolutional neural network model is proposed that involves a small number of learnable parameters. Using the model, a two-level ensemble classifier is designed. Each channel is scanned incrementally in the first level to generate predictions, which are fused using the majority vote. The second level fuses the predictions of all the channels of a signal using a majority vote to predict the emotional state. The method was validated using the public domain challenging benchmark DEAP dataset. The electroencephalogram signals over five brain regions were analyzed. The results indicate that the frontal brain region plays a dominant role, achieving accuracies of 98.43% and 97.65% for two emotion recognition problems (distinguishing high valence vs. low valence and high arousal vs. low arousal states).

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Section: Related Workmentioning

confidence: 99%

Emotion Recognition System Based on Two-Level Ensemble of Deep-Convolutional Neural Network Models

et al. 2023

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“…Primero, respetar a los demás en las redes sociales incluye tratar a todos con cortesía y respeto, independientemente de sus opiniones, creencias o antecedentes. La sociedad ecuatoriana, como cualquier otra sociedad, es diversa y las redes sociales pueden ser un espacio para celebrar esta diversidad en lugar de fomentar la división y el odio (Giuntini et al, 2021;Sampedro Guamán et al, 2021). Además, una buena gestión de las redes sociales promueve el diálogo constructivo en lugar de la confrontación dañina.…”

Section: Introductionunclassified

Forecasting con Python, caso de estudio: visitas a las redes sociales en Ecuador con machine learning

Marcillo Vera,

Rosado,

Zambrano

et al. 2024

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En este artículo, se enfatiza la importancia del respeto en el uso de las redes sociales en la sociedad ecuatoriana, subrayando su diversidad y la necesidad de fomentar un diálogo constructivo en lugar de la confrontación dañina en línea. Se proporciona una metodología que emplea técnicas de aprendizaje automático y modelado estadístico, como el modelo ARIMA, para predecir el tráfico web en las redes sociales de Ecuador. Además, se discuten diversas estrategias de backtesting para evaluar y mejorar la precisión del modelo a lo largo del tiempo. Los resultados indican un crecimiento significativo en el número de usuarios de redes sociales en Ecuador, con un enfoque en el modelo ARIMA como eficaz para la predicción de series temporales, aunque se sugiere la exploración de enfoques adicionales y mejoras continuas en futuras investigaciones. Este estudio contribuye a comprender mejor el impacto de las redes sociales en la sociedad ecuatoriana y proporciona una base metodológica para pronosticar el tráfico web en estas plataformas en el futuro.

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“…Shortest paths, network diameter, and average path lengths indicate how easily nodes can reach each other [53,54]. 7.…”

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confidence: 99%

Application of Graph Theory for Blockchain Technologies

Jayabalasamy,

Pujol,

Latha Bhaskaran

2024

Mathematics

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Blockchain technology, serving as the backbone for decentralized systems, facilitates secure and transparent transactional data storage across a distributed network of nodes. Blockchain platforms rely on distributed ledgers to enable secure peer-to-peer transactions without central oversight. As these systems grow in complexity, analyzing their topological structure and vulnerabilities requires robust mathematical frameworks. This paper explores applications of graph theory for modeling blockchain networks to evaluate decentralization, security, privacy, scalability and NFT Mapping. We use graph metrics like degree distribution and betweenness centrality to quantify node connectivity, identify network bottlenecks, trace asset flows and detect communities. Attack vectors are assessed by simulating adversarial scenarios within graph models of blockchain systems. Overall, translating blockchain ecosystems into graph representations allows comprehensive analytical insights to guide the development of efficient, resilient decentralized infrastructures.

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Modeling and Assessing the Temporal Behavior of Emotional and Depressive User Interactions on Social Networks

Cited by 12 publications

References 47 publications

Emotion Recognition System Based on Two-Level Ensemble of Deep-Convolutional Neural Network Models

Emotion Recognition System Based on Two-Level Ensemble of Deep-Convolutional Neural Network Models

Forecasting con Python, caso de estudio: visitas a las redes sociales en Ecuador con machine learning

Application of Graph Theory for Blockchain Technologies

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