The backstage of the reality and the reality of the backstage: effects of real in JN Especial website

O objetivo deste estudo é analisar os recursos utilizados pelo telejornalismo de forma a aproximar público e produção televisiva, na articulação entre telejornalismo e internet. Partimos da análise do site JN Especial, que está ligado ao site do Jornal Nacional e hospedado pelo Globo.com, a fim de entender como se dá a produção discursiva de sentidos e de efeitos de real a partir do conteúdo disponibilizado e da possibilidade de interação do público. Nossa intenção é observar como os textos, vinculados a outros recursos como fotografias, vídeos e os comentários do público, constroem discursos a respeito da produção jornalística, que deve estar pautada na apresentação do real.The objective of this study is to analyze the resources used by the television news in order to get together public and television production, in the articulation between the television news and the internet. We start with the analysis of the website JN Especial3, which is linked to Jornal Nacional’s website and hosted by Globo.com, in order to understand how do the discursive production of meanings and effects of real work, from the contents made available and the possibility of interaction with the public. We intend to observe how those texts, linked to other resources such as photographs, videos and comments by the public, build speeches about the journalistic production, which should be based on the presentation of the real

Os bastidores da realidade e a realidade dos bastidores: efeitos de real no site JN Especial

O objetivo deste estudo é analisar os recursos utilizados pelo telejornalismo de forma a aproximar público e produção televisiva, na articulação entre telejornalismo e internet. Partimos da análise do site JN Especial, que está ligado ao site do Jornal Nacional e hospedado pelo Globo.com, a fim de entender como se dá a produção discursiva de sentidos e de efeitos de real a partir do conteúdo disponibilizado e da possibilidade de interação do público. Nossa intenção é observar como os textos, vinculados a outros recursos como fotografias, vídeos e os comentários do público, constroem discursos a respeito da produção jornalística, que deve estar pautada na apresentação do real

Automatic BI-RADS Classification of Breast Magnetic Resonance Medical Records Using Transformer-Based Models for Brazilian Portuguese

This chapter aims to present a classification model for categorizing textual clinical records of breast magnetic resonance imaging, based on lexical, syntactic and semantic analysis of clinical reports according to the Breast Imaging-Reporting and Data System (BI-RADS) classification, using Deep Learning and Natural Language Processing (NLP). The model was developed from transfer learning based on the pre-trained BERTimbau model, BERT model (Bidirectional Encoder Representations from Transformers) trained in Brazilian Portuguese. The dataset is composed of medical reports in Brazilian Portuguese classified into six categories: Inconclusive; Normal or Negative; Certainly Benign Findings; Probably Benign Findings; Suspicious Findings; High Risk of Cancer; Previously Known Malignant Injury. The following models were implemented and compared: Random Forest, SVM, Naïve Bayes, BERTimbau with and without finetuning. The BERTimbau model presented better results, with better performance after finetuning

Television experience and political discussion on Twitter : exploring online conversations during the 2014 Brazilian presidential elections

This empirical study aims to investigate the characteristics of the conversation and political discussion on Twitter during the last televised presidential debate in the first round of the Brazilian elections in 2014. Television experience, political participation, and Twitter dynamics were considered for the examination of different kinds of engagement in 100,000 messages collected during the debate. The aim was to identify peaks of user engagement, linking these specific moments and evaluating how the characteristics of the communicative environment may have affected the discourses built around the debate.Esta pesquisa empírica pretendeinvestigar as características da conversação e da discussão política no Twitter durante o últimodebate presidencial televisionado no primeiro turno da eleições de 2014. A experiênciatelevisiva, a participação política e a dinâmica do Twitter foram consideradas para o examede diferentes tipos de engajamento em 100 mil mensagens coletadas durante o debate.O objetivo foi identificar picos de engajamento de uso, conectando esses momentosespecíficos e avaliando como as características do ambiente comunicacional podem terafetado os discursos construídos ao redor do debate

Ciência e Covid-19 no Brasil: a repercussão das decisões da OMS no Twitter

This work objective is to analyze the discussion on science, its role and functioning, in the context of the Covid-19 pandemic in Brazil. A study of the repercussion on Twitter of the decisions of the World Health Organization (WHO) on the tests of chloroquine and/or hydroxychloroquine for the Covid-19 treatment was carried out in three moments: (i) temporary suspension, (ii) resumption, and (iii) interruption. 501,123 tweets were analyzed. The results indicate an incomprehension about scientific research functioning and the presence of politically interested discourses against scienceO objetivo deste trabalho é analisar a discussão sobre a ciência, seu papel e funcionamento, no contexto da pandemia de Covid-19 no Brasil. Foi realizado um estudo da repercussão no Twitter das decisões da Organização Mundial da Saúde (OMS) sobre os testes com a cloroquina e/ou hidroxicloroquina para o tratamento da Covid-19 em três momentos: (i) suspensão temporária; (ii) retomada; e (iii) interrupção. Foram analisados 501.123 tweets. Os resultados indicam uma incompreensão quanto ao funcionamento da pesquisa científica e a presença de discursos politicamente interessados contrários à ciênciaEl objetivo de este trabajo es analizar la discusión sobre la ciencia, su rol y su funcionamiento, en el contexto de la pandemia Covid-19 en Brasil. Se realizó un estudio de la repercusión en Twitter de las decisiones de la Organización Mundial de la Salud (OMS) sobre las pruebas de cloroquina y/o hidroxicloroquina para el tratamiento de Covid-19 en tres momentos: (i) suspensión temporal, (ii) reanudación, y (iii) interrupción. Se analizaron 501,123 tweets. Los resultados indican una incomprensión sobre el funcionamiento de la investigación científica y la presencia de discursos contra la ciencia políticamente interesados

Using discrete wavelet transform for optimizing COVID-19 new cases and deaths prediction worldwide with deep neural networks.

This work aims to compare deep learning models designed to predict daily number of cases and deaths caused by COVID-19 for 183 countries, using a daily basis time series, in addition to a feature augmentation strategy based on Discrete Wavelet Transform (DWT). The following deep learning architectures were compared using two different feature sets with and without DWT: (1) a homogeneous architecture containing multiple LSTM (Long-Short Term Memory) layers and (2) a hybrid architecture combining multiple CNN (Convolutional Neural Network) layers and multiple LSTM layers. Therefore, four deep learning models were evaluated: (1) LSTM, (2) CNN + LSTM, (3) DWT + LSTM and (4) DWT + CNN + LSTM. Their performances were quantitatively assessed using the metrics: Mean Absolute Error (MAE), Normalized Mean Squared Error (NMSE), Pearson R, and Factor of 2. The models were designed to predict the daily evolution of the two main epidemic variables up to 30 days ahead. After a fine-tuning procedure for hyperparameters optimization of each model, the results show a statistically significant difference between the models' performances both for the prediction of deaths and confirmed cases (p-value<0.001). Based on NMSE values, significant differences were observed between LSTM and CNN+LSTM, indicating that convolutional layers added to LSTM networks made the model more accurate. The use of wavelet coefficients as additional features (DWT+CNN+LSTM) achieved equivalent results to CNN+LSTM model, which demonstrates the potential of wavelets application for optimizing models, since this allows training with a smaller time series data

Using discrete wavelet transform for optimizing COVID-19 new cases and deaths prediction worldwide with deep neural networks

This work aims to compare deep learning models designed to predict daily number of cases and deaths caused by COVID-19 for 183 countries, using a daily basis time series, in addition to a feature augmentation strategy based on Discrete Wavelet Transform (DWT). The following deep learning architectures were compared using two different feature sets with and without DWT: (1) a homogeneous architecture containing multiple LSTM (Long-Short Term Memory) layers and (2) a hybrid architecture combining multiple CNN (Convolutional Neural Network) layers and multiple LSTM layers. Therefore, four deep learning models were evaluated: (1) LSTM, (2) CNN + LSTM, (3) DWT + LSTM and (4) DWT + CNN + LSTM. Their performances were quantitatively assessed using the metrics: Mean Absolute Error (MAE), Normalized Mean Squared Error (NMSE), Pearson R, and Factor of 2. The models were designed to predict the daily evolution of the two main epidemic variables up to 30 days ahead. After a fine-tuning procedure for hyperparameters optimization of each model, the results show a statistically significant difference between the models’ performances both for the prediction of deaths and confirmed cases (p-value<0.001). Based on NMSE values, significant differences were observed between LSTM and CNN+LSTM, indicating that convolutional layers added to LSTM networks made the model more accurate. The use of wavelet coefficients as additional features (DWT+CNN+LSTM) achieved equivalent results to CNN+LSTM model, which demonstrates the potential of wavelets application for optimizing models, since this allows training with a smaller time series data