ArticleChaos, solitons, and fractals2020
Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics.
Article in Chaos, solitons, and fractals, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 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
41 citing papers in PubMed, 1 synthesis or guideline pooled it, 145 citations in OpenAlex.
- When Infodemic Meets Epidemic: Systematic Literature Review.JMIR public health and surveillance · 2025Pooled it
- Game-based inoculation versus graphic-based inoculation to combat misinformation: a randomized controlled trial.Cognitive research: principles and implications · 2023Trial
- Article
- Context-based sentiment analysis using a BiGRU DistilBERT fusion model for COVID-19 tweets.Scientific reports · 2025Article
- Understanding Public Emotions: Spatiotemporal Dynamics in the Post-Pandemic Era Through Weibo Data.Behavioral sciences (Basel, Switzerland) · 2025Article
- Public Opinion About COVID-19 on a Microblog Platform in China: Topic Modeling and Multidimensional Sentiment Analysis of Social Media.Journal of medical Internet research · 2024Article
- Online rumors during the COVID-19 pandemic: co-evolution of themes and emotions.Frontiers in public health · 2024Article
- Scenario-based assessment of emergency management of urban infectious disease outbreaks.Frontiers in public health · 2024Article
- Article
- News Waves: Hard News, Soft News, Fake News, Rumors, News Wavetrains.Entropy (Basel, Switzerland) · 2023Article
- Temporal and Emotional Variations in People's Perceptions of Mass Epidemic Infectious Disease After the COVID-19 Pandemic Using Influenza A as an Example: Topic Modeling and Sentiment Analysis Based on Weibo Data.Journal of medical Internet research · 2023Article
- Impact of the COVID-19 Pandemic on the Level of Anxiety and Depression in Caregivers of Children Benefiting from Neurorehabilitation Services.International journal of environmental research and public health · 2023Article
- Using geospatial social media data for infectious disease studies: a systematic review.International journal of digital earth · 2023Article
- A data-driven framework to assess population dynamics during novel coronavirus outbreaks: A case study on Xiamen Island, China.PloS one · 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
- What We Ask about When We Ask about Quarantine? Content and Sentiment Analysis on Online Help-Seeking Posts during COVID-19 on a Q&A Platform in China.International journal of environmental research and public health · 2022Article
- An Advanced Study of Urban Emergency Medical Equipment Logistics Distribution for Different Levels of Urgency Demand.International journal of environmental research and public health · 2022Article
- Public Opinions on COVID-19 Vaccines-A Spatiotemporal Perspective on Races and Topics Using a Bayesian-Based Method.Vaccines · 2022Article
- A bi-objective optimization model for the medical supplies' simultaneous pickup and delivery with drones.Computers & industrial engineering · 2022Article
- Spatiotemporal disparities in regional public risk perception of COVID-19 using Bayesian Spatiotemporally Varying Coefficients (STVC) series models across Chinese cities.International journal of disaster risk reduction : IJDRR · 2022Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 blocked Wuhan in China, which was sealed off on Chinese New Year's Eve. During this period, the research on the relevant topics of COVID-19 and emotional expressions published on social media can provide decision support for the management and control of large-scale public health events. The research assisted the analysis of microblog text topics with the help of the LDA model, and obtained 8 topics ("origin", "host", "organization", "quarantine measures", "role models", "education", "economic", "rumor") and 28 interactive topics. Obtain data through crawler tools, with the help of big data technology, social media topics and emotional change characteristics are analyzed from spatiotemporal perspectives. The results show that: (1) "Double peaks" feature appears in the epidemic topic search curve. Weibo on the topic of the epidemic gradually reduced after January 24. However, the proportion of epidemic topic searches has gradually increased, and a "double peaks" phenomenon appeared within a week; (2) The topic changes with time and the fluctuation of the topic discussion rate gradually weakens. The number of texts on different topics and interactive topics changes with time. At the same time, the discussion rate of epidemic topics gradually weakens; (3) The political and economic center is an area where social media is highly concerned. The areas formed by Beijing, Shanghai, Guangdong, Sichuan and Hubei have published more microblog texts. The spatial division of the number of Weibo social media texts has a high correlation with the economic zone division; (4) The existence of the topic of "rumor" will enable people to have more communication and discussion. The interactive topics of "rumors" always have higher topic popularity and low emotion text expressions. Through the analysis of media information, it helps relevant decision makers to grasp social media topics from spatiotemporal characteristics, so that relevant departments can accurately grasp the public's subjective ideas and emotional expressions, and provide decision support for macro-control response strategies and measures and risk communication.
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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.