ArticleApplied soft computing2021
Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA.
Article in Applied soft computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
63 citing papers in PubMed, 1 synthesis or guideline pooled it, 372 citations in OpenAlex.
- When Infodemic Meets Epidemic: Systematic Literature Review.JMIR public health and surveillance · 2025Pooled it
- Linking social media data with geospatial information to analyse changes in human sentiments in and along surface water environments.MethodsX · 2025Article
- Event-driven dynamics, regional disparities, and netizen engagement: characterizing and exploring malaria discourse on Sina Weibo for enhanced health communication.Malaria journal · 2025Article
- Exploring Public Sentiment on the Repurposing of Ivermectin for COVID-19 Treatment: Cross-Sectional Study Using Twitter Data.JMIR formative research · 2025Article
- Application of the LDA model to identify topics in telemedicine conversations on the X social network.BMC health services research · 2025Article
- Understanding Challenges and Emotions of Informal Caregivers of General Older Adults and People With Alzheimer Disease and Related Dementia: Comparative Study.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
- Chinese Public Attitudes and Opinions on Health Policies During Public Health Emergencies: Sentiment and Topic Analysis.Journal of medical Internet research · 2024Article
- Digital epidemiology: harnessing big data for early detection and monitoring of viral outbreaks.Infection prevention in practice · 2024Review
- Annotated dataset for sentiment analysis and sarcasm detection: Bilingual code-mixed English-Malay social media data in the public security domain.Data in brief · 2024Article
- Analysis of Pharmaceutical Companies' Social Media Activity during the COVID-19 Pandemic and Its Impact on the Public.Behavioral sciences (Basel, Switzerland) · 2024Article
- A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets.JMIR formative research · 2024Article
- Social mood during the Covid-19 vaccination process in Spain. A sentiment analysis of tweets and social network leaders.Heliyon · 2024Article
- Understanding the determinants of vaccine hesitancy in the United States: A comparison of social surveys and social media.PloS one · 2024Article
- Analyzing information sharing behaviors during stance formation on COVID-19 vaccination among Japanese Twitter users.PloS one · 2024Article
- Online rumors during the COVID-19 pandemic: co-evolution of themes and emotions.Frontiers in public health · 2024Article
- Breaking news: Unveiling a new dataset for Portuguese news classification and comparative analysis of approaches.PloS one · 2024Article
- Article
- Sentiment analysis of COVID-19 cases in Greece using Twitter data.Expert systems with applications · 2023Article
- Topic sentiment analysis based on deep neural network using document embedding technique.The Journal of supercomputing · 2023Article
3 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Twitter is a social media platform with more than 500 million users worldwide. It has become a tool for spreading the news, discussing ideas and comments on world events. Twitter is also an important source of health-related information, given the amount of news, opinions and information that is shared by both citizens and official sources. It is a challenge identifying interesting and useful content from large text-streams in different languages, few works have explored languages other than English. In this paper, we use topic identification and sentiment analysis to explore a large number of tweets in both countries with a high number of spreading and deaths by COVID-19, Brazil, and the USA. We employ 3,332,565 tweets in English and 3,155,277 tweets in Portuguese to compare and discuss the effectiveness of topic identification and sentiment analysis in both languages. We ranked ten topics and analyzed the content discussed on Twitter for four months providing an assessment of the discourse evolution over time. The topics we identified were representative of the news outlets during April and August in both countries. We contribute to the study of the Portuguese language, to the analysis of sentiment trends over a long period and their relation to announced news, and the comparison of the human behavior in two different geographical locations affected by this pandemic. It is important to understand public reactions, information dissemination and consensus building in all major forms, including social media in different countries.
Indexed as
Identifiers
What OpenQuestion holds
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.