ArticleJMIR public health and surveillance2021
Comparing News Articles and Tweets About COVID-19 in Brazil: Sentiment Analysis and Topic Modeling Approach.
Article in JMIR public health and surveillance, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Drivers of health polarization during the COVID-19 pandemic: A systematic review.Science advances · 2025Pooled it
- Health information dynamics and audience responses to avian influenza on YouTube: Implications for digital public health communication.PNAS nexus · 2026Article
- Exploring Public Sentiment on the Repurposing of Ivermectin for COVID-19 Treatment: Cross-Sectional Study Using Twitter Data.JMIR formative research · 2025Article
- Characterizing Public Sentiments and Drug Interactions in the COVID-19 Pandemic Using Social Media: Natural Language Processing and Network Analysis.Journal of medical Internet research · 2025Article
- An explainable GeoAI approach for the multimodal analysis of urban human dynamics: a case study for the COVID-19 pandemic in Rio de Janeiro.Computational urban science · 2025Article
- Agendas on Nursing in South Korea Media: Natural Language Processing and Network Analysis of News From 2005 to 2022.Journal of medical Internet research · 2024Article
- Attitudes of the public and medical professionals toward nurse prescribing: A text-mining study based on social medias.International journal of nursing sciences · 2024Article
- Mapping Knowledge Landscapes and Emerging Trends for the Spread of Health-Related Misinformation During the COVID-19 on Chinese and English Social Media: A Comparative Bibliometric and Visualization Analysis.Journal of multidisciplinary healthcare · 2024Article
- Analysis of the evolving factors of social media users' emotions and behaviors: a longitudinal study from China's COVID-19 opening policy period.BMC public health · 2023Article
- News Coverage of Face Masks in Australia During the Early COVID-19 Pandemic: Topic Modeling Study.JMIR infodemiology · 2023Article
- News Media Coverage of the Problem of Purchasing Fake Prescription Medicines on the Internet: Thematic Analysis.JMIR formative research · 2023Article
- Evolution of Public Attitudes and Opinions Regarding COVID-19 Vaccination During the Vaccine Campaign in China: Year-Long Infodemiology Study of Weibo Posts.Journal of medical Internet research · 2023Article
- User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis.Journal of medical Internet research · 2023Article
- Agenda-Setting for COVID-19: A Study of Large-Scale Economic News Coverage Using Natural Language Processing.International journal of data science and analytics · 2023Article
- Can the Content of Social Networks Explain Epidemic Outbreaks?Population research and policy review · 2023Article
- Omicron virus emotions understanding system based on deep learning architecture.Journal of ambient intelligence and humanized computing · 2023Article
- Evaluating Voice Assistants' Responses to COVID-19 Vaccination in Portuguese: Quality Assessment.JMIR human factors · 2022Article
- Mass Media Use to Learn About COVID-19 and the Non-intention to Be Vaccinated Against COVID-19 in Latin America and Caribbean Countries.Frontiers in medicine · 2022Article
- Article
- A Hybrid Feature Extraction Method for Nepali COVID-19-Related Tweets Classification.Computational intelligence and neuroscience · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe COVID-19 pandemic is severely affecting people worldwide. Currently, an important approach to understand this phenomenon and its impact on the lives of people consists of monitoring social networks and news on the internet.
objectiveThe purpose of this study is to present a methodology to capture the main subjects and themes under discussion in news media and social media and to apply this methodology to analyze the impact of the COVID-19 pandemic in Brazil.
methodsThis work proposes a methodology based on topic modeling, namely entity recognition, and sentiment analysis of texts to compare Twitter posts and news, followed by visualization of the evolution and impact of the COVID-19 pandemic. We focused our analysis on Brazil, an important epicenter of the pandemic; therefore, we faced the challenge of addressing Brazilian Portuguese texts.
resultsIn this work, we collected and analyzed 18,413 articles from news media and 1,597,934 tweets posted by 1,299,084 users in Brazil. The results show that the proposed methodology improved the topic sentiment analysis over time, enabling better monitoring of internet media. Additionally, with this tool, we extracted some interesting insights about the evolution of the COVID-19 pandemic in Brazil. For instance, we found that Twitter presented similar topic coverage to news media; the main entities were similar, but they differed in theme distribution and entity diversity. Moreover, some aspects represented negative sentiment toward political themes in both media, and a high incidence of mentions of a specific drug denoted high political polarization during the pandemic.
conclusionsThis study identified the main themes under discussion in both news and social media and how their sentiments evolved over time. It is possible to understand the major concerns of the public during the pandemic, and all the obtained information is thus useful for decision-making by authorities.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.