ReviewEpidemiology and infection2016
Social media in Ebola outbreak.
Review in Epidemiology and infection, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses 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
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Digital Data Sources and Their Impact on People's Health: A Systematic Review of Systematic Reviews.Frontiers in public health · 2021Pooled it
- Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review.Journal of medical Internet research · 2018Pooled it
- Article
- Advances in Animal Disease Surveillance and Information Systems and Their Role in Disease Control and Prevention: Implications in Ethiopia.Veterinary medicine and science · 2025Review
- Spatio-temporal evaluation of social media as a tool for livestock disease surveillance.One health (Amsterdam, Netherlands) · 2023Article
- People's perceptions on COVID-19 vaccination: an analysis of twitter discourse from four countries.Scientific reports · 2023Article
- Emerging infectious disease outbreaks in Sub-Saharan Africa: Learning from the past and present to be better prepared for future outbreaks.Frontiers in public health · 2023Article
- Global Variations in Event-Based Surveillance for Disease Outbreak Detection: Time Series Analysis.JMIR public health and surveillance · 2022Article
- Mining Social Media Data to Predict COVID-19 Case Counts.Proceedings. IEEE International Conference on Healthcare Informatics · 2022Article
- Crowdsourcing for Machine Learning in Public Health Surveillance: Lessons Learned From Amazon Mechanical Turk.Journal of medical Internet research · 2022Article
- Using a Machine Learning Approach to Monitor COVID-19 Vaccine Adverse Events (VAE) from Twitter Data.Vaccines · 2022Article
- Perceptions of blame on social media during the coronavirus pandemic.Computers in human behavior · 2021Article
- Suitability of Google Trends™ for Digital Surveillance During Ongoing COVID-19 Epidemic: A Case Study from India.Disaster medicine and public health preparedness · 2021Article
- Global Public Interests and Dynamic Trends in Osteoporosis From 2004 to 2019: Infodemiology Study.Journal of medical Internet research · 2021Article
- Detecting Fine-Grained Emotions on Social Media during Major Disease Outbreaks: Health and Well-being before and during the COVID-19 Pandemic.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2021Article
- Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic.Frontiers in digital health · 2021Review
- Reducing medical error during a pandemic.The British journal of oral & maxillofacial surgery · 2020Article
- COVID-19-Related Web Search Behaviors and Infodemic Attitudes in Italy: Infodemiological Study.JMIR public health and surveillance · 2020Article
- Tracking COVID-19 in Europe: Infodemiology Approach.JMIR public health and surveillance · 2020Article
- Ebola and Localized Blame on Social Media: Analysis of Twitter and Facebook Conversations During the 2014-2015 Ebola Epidemic.Culture, medicine and psychiatry · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The West African 2014 Ebola outbreak has highlighted the need for a better information network. Hybrid information networks, an integration of both hierarchical and formalized command control-driven and community-based, or ad hoc emerging networks, could assist in improving public health responses. By filling the missing gaps with social media use, the public health response could be more proactive rather than reactive in responding to such an outbreak of global concern. This article provides a review of the current social media use specifically in this outbreak by systematically collecting data from ProQuest Newsstand, Dow Jones Factiva, Program for Monitoring Emerging Diseases (ProMED) as well as Google Trends. The period studied is from 19 March 2014 (first request for information on ProMED) to 15 October 2014, a total of 31 weeks. The term 'Ebola' was used in the search for media reports. The outcome of the review shows positive results for social media use in effective surveillance response mechanisms - for improving the detection, preparedness and response of the outbreak - as a complement to traditional, filed, work-based surveillance approach.
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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.