ArticleJournal of medical Internet research2020
Mining the Characteristics of COVID-19 Patients in China: Analysis of Social Media Posts.
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 43 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
43 citing papers in PubMed, 1 synthesis or guideline pooled it, 88 citations in OpenAlex.
- Applications of GIS and geospatial analyses in COVID-19 research: A systematic review.F1000Research · 2020Pooled it
- Inhibitory control and stress biomarkers in response to social media stimuli: a Go/No-Go study using heart rate variability, alpha-amylase and oxytocin.Frontiers in psychiatry · 2026Article
- Outlier analysis for accelerating clinical discovery: An augmented intelligence framework and a systematic review.PLOS digital health · 2024Article
- A multidimensional comparative study of help-seeking messages on Weibo under different stages of COVID-19 pandemic in China.Frontiers in public health · 2024Article
- The manifestation and causes of public panic in the early stage of COVID-19 in China: a framework based on consciousness-attitude-behavior.Frontiers in public health · 2024Article
- Assessment of the Early Detection of Anosmia and Ageusia Symptoms in COVID-19 on Twitter: Retrospective Study.JMIR infodemiology · 2023Article
- Investigating the Role of Nutrition in Enhancing Immunity During the COVID-19 Pandemic: Twitter Text-Mining Analysis.Journal of medical Internet research · 2023Article
- Trend and Co-occurrence Network of COVID-19 Symptoms From Large-Scale Social Media Data: Infoveillance Study.Journal of medical Internet research · 2023Article
- Augmented Intelligence for Clinical Discovery in Hypertensive Disorders of Pregnancy Using Outlier Analysis.Cureus · 2023Article
- Do Case Reports and Case Series Generate Clinical Discoveries About Preeclampsia? A Systematic Review.International journal of women's health · 2023Review
- Increasing public concern on insomnia during the COVID-19 outbreak in China: An info-demiology study.Heliyon · 2022Article
- Transcutaneous Auricular Vagus Nerve Stimulation Improves Inflammation but Does Not Interfere with Cardiac Modulation and Clinical Symptoms of Individuals with COVID-19: A Randomized Clinical Trial.Life (Basel, Switzerland) · 2022Article
- Emergency preparedness during the COVID-19 pandemic: Modelling the roles of social media with fuzzy DEMATEL and analytic network process.Socio-economic planning sciences · 2022Article
- #WuhanDiary and #WuhanLockdown: gendered posting patterns and behaviours on Weibo during the COVID-19 pandemic.BMJ global health · 2022Review
- Effects of local government social media use on citizen compliance during a crisis: Evidence from the COVID-19 crisis in China.Public administration · 2022Article
- The Public Perception of the #GeneEditedBabies Event Across Multiple Social Media Platforms: Observational Study.Journal of medical Internet research · 2022Observational
- The Transmission, Infection Prevention, and Control during the COVID-19 Pandemic in China: A Retrospective Study.International journal of environmental research and public health · 2022Article
- COVID-19 Mask Usage and Social Distancing in Social Media Images: Large-scale Deep Learning Analysis.JMIR public health and surveillance · 2022Article
- Exploring public perceptions of the COVID-19 vaccine online from a cultural perspective: Semantic network analysis of two social media platforms in the United States and China.Telematics and informatics · 2021Article
- A Pipeline to Understand Emerging Illness Via Social Media Data Analysis: Case Study on Breast Implant Illness.JMIR medical informatics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn December 2019, pneumonia cases of unknown origin were reported in Wuhan City, Hubei Province, China. Identified as the coronavirus disease (COVID-19), the number of cases grew rapidly by human-to-human transmission in Wuhan. Social media, especially Sina Weibo (a major Chinese microblogging social media site), has become an important platform for the public to obtain information and seek help.
objectiveThis study aims to analyze the characteristics of suspected or laboratory-confirmed COVID-19 patients who asked for help on Sina Weibo.
methodsWe conducted data mining on Sina Weibo and extracted the data of 485 patients who presented with clinical symptoms and imaging descriptions of suspected or laboratory-confirmed cases of COVID-19. In total, 9878 posts seeking help on Sina Weibo from February 3 to 20, 2020 were analyzed. We used a descriptive research methodology to describe the distribution and other epidemiological characteristics of patients with suspected or laboratory-confirmed SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) infection. The distance between patients' home and the nearest designated hospital was calculated using the geographic information system ArcGIS.
resultsAll patients included in this study who sought help on Sina Weibo lived in Wuhan, with a median age of 63.0 years (IQR 55.0-71.0). Fever (408/485, 84.12%) was the most common symptom. Ground-glass opacity (237/314, 75.48%) was the most common pattern on chest computed tomography; 39.67% (167/421) of families had suspected and/or laboratory-confirmed family members; 36.58% (154/421) of families had 1 or 2 suspected and/or laboratory-confirmed members; and 70.52% (232/329) of patients needed to rely on their relatives for help. The median time from illness onset to real-time reverse transcription-polymerase chain reaction (RT-PCR) testing was 8 days (IQR 5.0-10.0), and the median time from illness onset to online help was 10 days (IQR 6.0-12.0). Of 481 patients, 32.22% (n=155) lived more than 3 kilometers away from the nearest designated hospital.
conclusionsOur findings show that patients seeking help on Sina Weibo lived in Wuhan and most were elderly. Most patients had fever symptoms, and ground-glass opacities were noted in chest computed tomography. The onset of the disease was characterized by family clustering and most families lived far from the designated hospital. Therefore, we recommend the following: (1) the most stringent centralized medical observation measures should be taken to avoid transmission in family clusters; and (2) social media can help these patients get early attention during Wuhan's lockdown. These findings can help the government and the health department identify high-risk patients and accelerate emergency responses following public demands for help.
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.