Infodemia: excesso de quantidade em detrimento da qualidade das informações sobre a COVID-19

García, Leila Posenato; Duarte, Elisete

doi:10.1590/s1679-49742020000400019

Cited by 67 publications

(62 citation statements)

References 7 publications

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“…Similarly, because of this virus outbreak, plenty of fake news and criticism on movements is continuously appearing on social media platforms. It disturbs communication among public health authorities and provokes high tension in the general public [ 9 ]. The study on social network analysis of novel virus sentimental analysis resulted in five relevant themes that range from positive to negative [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Sentimental Analysis of COVID-19 Tweets Using Deep Learning Models

Chintalapudi

Battineni

Amenta

2021

Infectious Disease Reports

113

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The novel coronavirus disease (COVID-19) is an ongoing pandemic with large global attention. However, spreading false news on social media sites like Twitter is creating unnecessary anxiety towards this disease. The motto behind this study is to analyses tweets by Indian netizens during the COVID-19 lockdown. The data included tweets collected on the dates between 23 March 2020 and 15 July 2020 and the text has been labelled as fear, sad, anger, and joy. Data analysis was conducted by Bidirectional Encoder Representations from Transformers (BERT) model, which is a new deep-learning model for text analysis and performance and was compared with three other models such as logistic regression (LR), support vector machines (SVM), and long-short term memory (LSTM). Accuracy for every sentiment was separately calculated. The BERT model produced 89% accuracy and the other three models produced 75%, 74.75%, and 65%, respectively. Each sentiment classification has accuracy ranging from 75.88–87.33% with a median accuracy of 79.34%, which is a relatively considerable value in text mining algorithms. Our findings present the high prevalence of keywords and associated terms among Indian tweets during COVID-19. Further, this work clarifies public opinion on pandemics and lead public health authorities for a better society.

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Section: Introductionmentioning

confidence: 99%

Sentimental Analysis of COVID-19 Tweets Using Deep Learning Models

Chintalapudi

Battineni

Amenta

2021

Infectious Disease Reports

113

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“…Para obter uma descrição completa da licença, visite 7 . E para a versão beta, disponibilizamos o endereço do código do painel web 8 com as instruções, APK 9 do aplicativo e um vídeo 10 prático.…”

Section: Privacidade Licença E Acessounclassified

“…9 https://github.com/ribaslucian/rechat-apk/blob/master/Rechat.apk. 10 https://youtu.be/rByIa6ugMJ8. Demonstramos a união de dois modelos de estudo de amostragem, bem como a proposta em diversificar a típica coleta por formulários para um aplicativo de bate-papo similar ao real.…”

Section: Considerações Finaisunclassified

“…Segundo Garcia et. al [10], no contexto da pandemia de COVID-19, o excesso de informações compartilhadas a respeito desse fenômeno gerou dificuldade em filtrar o que é fake news (notícia falsa).…”

Section: Introductionunclassified

“…Um grande aumento no volume de informações associadas a um assunto específico, que podem se multiplicar exponencialmente em pouco tempo[10] 4. https://www.forbes.com/sites/johnkoetsier/2019/12/17/top-apps-of-the-decadewhere-facebook-google-tiktok-and-twitter-rank/#28ba6b934dff.…”

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Rechat: Ferramenta para Estudo do Comportamento de Usuários em Sistemas de Bate-papo do Estilo WhatsApp

Ribas

Gomes-Jr

Silva

2020

Anais Estendidos Do XXVI Simpósio Brasileiro De Sistemas Multimídia E Web (WebMedia 2020)

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The spread of misinformation, hate speech, or sexist discourse had become a significant problem, especially on chat platforms. This article describes a data collection and processing tool to support research on users’ behavior exposed to these contents. Our motivation is the recent need to study profiles that propagate this type of information and the need to understand these new dissemination mechanisms. This tool provides a web configuration panel and a mobile application for data collection. Using a chatbot mechanism, the panel allows researchers to perform personalized experiments to analyze specific issues. The application collects the user’s basic profile, allows interaction with the chatbots sent by the researcher, and runs on the volunteers’ devices, similar to a real messaging application such as WhatsApp or Telegram. The data generated regarding the user’s interactions with the application can be exported for analysis through the panel. Also, we describe the insertion of optional codes that can be executed in parallel to the conversations, helping in sophisticated personalizations. We believe that the proposed open-source tool will help researchers from different areas, even without computer programming skills, to understand fundamental mechanisms of user behavior in mobile chat systems.

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Improving the review classification of Google apps using combined feature embedding and deep convolutional neural network model

Aslam

Alzamzami

Xia

et al. 2023

J Ambient Intell Human Comput

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Infodemia: excesso de quantidade em detrimento da qualidade das informações sobre a COVID-19

Abstract: Infodemic: excess quantity to the detriment of quality of information about COVID-19

Cited by 67 publications

References 7 publications

Sentimental Analysis of COVID-19 Tweets Using Deep Learning Models

Sentimental Analysis of COVID-19 Tweets Using Deep Learning Models

Rechat: Ferramenta para Estudo do Comportamento de Usuários em Sistemas de Bate-papo do Estilo WhatsApp

Improving the review classification of Google apps using combined feature embedding and deep convolutional neural network model

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