ArticleJournal of medical Internet research2020
Social Network Analysis of COVID-19 Sentiments: Application of Artificial Intelligence.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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
78 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Social Media Use for Health Purposes: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Analyzing Social Media to Infer Mental Health Status and Affective States for Crisis and Disaster Management: Scoping Review.Journal of medical Internet research · 2026Article
- Digital epidemiology and public health surveillance: scientometric mapping of emerging technologies and challenges (2000-2025).Frontiers in digital health · 2026Review
- Group cognitive space of online rumors in public health emergencies: a theoretical and empirical study.Frontiers in public health · 2026Article
- Dangling centrality highlights critical nodes by evaluating network stability through link removal.Scientific reports · 2025Article
- Vaccination Coverage Covid-19 and Self-Reported Adverse Post-Vaccination Events in Mbuji-Mayi, DR Congo.Infection and drug resistance · 2025Article
- Exploring the Early Adoption of Open AI among Laypeople and Technical Professionals: An Analysis of Twitter Conversations on #ChatGPT and #GPT3.International journal of human-computer interaction · 2025Article
- Unmasking the psychological impact of the early COVID-19 pandemic in young adults: results from a cross-sectional online survey.Frontiers in psychiatry · 2025Article
- Spatial structure and organization of the medical device industry urban network in China: evidence from specialized, refined, distinctive, and innovative firms.Frontiers in public health · 2025Article
- Task-Specific Transformer-Based Language Models in Health Care: Scoping Review.JMIR medical informatics · 2024Article
- Chinese Public Attitudes and Opinions on Health Policies During Public Health Emergencies: Sentiment and Topic Analysis.Journal of medical Internet research · 2024Article
- Bridging the Gap: Integrating Knowledge from the Study of Social Network Analysis and Infectious Disease Dynamics in Human and Nonhuman Primates.Annual review of anthropology · 2024Article
- Evaluation via simulation of statistical corrections for network nonindependence.Health services & outcomes research methodology · 2024Article
- Impact of the COVID-19 Pandemic on People Living With Rare Diseases and Their Families: Results of a National Survey.JMIR public health and surveillance · 2024Article
- Utility and Utilization of Patient-Reported Experience Measures for the Supplementary COVID-19 Protective Actions at the Ovidius Clinical Hospital in Romania.Healthcare (Basel, Switzerland) · 2024Article
- Research on the evolution of cross-platform online public opinion for public health emergencies considering stakeholders.PloS one · 2024Article
- Utilizing AI for extracting insights on post WHO's COVID-19 vaccination declaration from X (Twitter) social network.AIMS public health · 2024Article
- Understanding the progress of COVID-19 transmission in a rural district: a social network approach.PeerJ · 2024Article
- Machine Learning-Guided Protein Engineering.ACS catalysis · 2023Review
- Article
18 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
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe coronavirus disease (COVID-19) pandemic led to substantial public discussion. Understanding these discussions can help institutions, governments, and individuals navigate the pandemic.
objectiveThe aim of this study is to analyze discussions on Twitter related to COVID-19 and to investigate the sentiments toward COVID-19.
methodsThis study applied machine learning methods in the field of artificial intelligence to analyze data collected from Twitter. Using tweets originating exclusively in the United States and written in English during the 1-month period from March 20 to April 19, 2020, the study examined COVID-19-related discussions. Social network and sentiment analyses were also conducted to determine the social network of dominant topics and whether the tweets expressed positive, neutral, or negative sentiments. Geographic analysis of the tweets was also conducted.
resultsThere were a total of 14,180,603 likes, 863,411 replies, 3,087,812 retweets, and 641,381 mentions in tweets during the study timeframe. Out of 902,138 tweets analyzed, sentiment analysis classified 434,254 (48.2%) tweets as having a positive sentiment, 187,042 (20.7%) as neutral, and 280,842 (31.1%) as negative. The study identified 5 dominant themes among COVID-19-related tweets: health care environment, emotional support, business economy, social change, and psychological stress. Alaska, Wyoming, New Mexico, Pennsylvania, and Florida were the states expressing the most negative sentiment while Vermont, North Dakota, Utah, Colorado, Tennessee, and North Carolina conveyed the most positive sentiment.
conclusionsThis study identified 5 prevalent themes of COVID-19 discussion with sentiments ranging from positive to negative. These themes and sentiments can clarify the public's response to COVID-19 and help officials navigate the pandemic.
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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.