Cited by 234Data-Driven Disease SurveillanceSocial Media in Health EducationViral Infections and Outbreaks Research

What can we learn about the Ebola outbreak from tweets?

Open access
Odlum, Michelle, Yoon, Sunmoo·2015·American Journal of Infection Control

Abstract

Background Twitter can address the challenges of the current Ebola outbreak surveillance. The aims of this study are to demonstrate the use of Twitter as a real-time method of Ebola outbreak surveillance to monitor information spread, capture early epidemic detection, and examine content of public knowledge and attitudes.Methods We collected tweets mentioning Ebola in English during the early stage of the current Ebola outbreak from July 24-August 1, 2014. Our analysis for this observational study includes time series analysis with geologic visualization to observe information dissemination and content analysis using natural language processing to examine public knowledge and attitudes.Results A total of 42,236 tweets (16,499 unique and 25,737 retweets) mentioning Ebola were posted and disseminated to 9,362,267,048 people, 63 times higher than the initial number. Tweets started to rise in Nigeria 3-7 days prior to the official announcement of the first probable Ebola case. The topics discussed in tweets include risk factors, prevention education, disease trends, and compassion.Conclusion Because of the analysis of a unique Twitter dataset captured in the early stage of the current Ebola outbreak, our results provide insight into the intersection of social media and public health outbreak surveillance. Findings demonstrate the usefulness of Twitter mining to inform public health education.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.ajic.2015.02.023

Cites

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

The citation lists haven’t been fetched yet

References 33 · Cited by 234

Research works printed but not linked: 1

  1. [33]Havard School of Public Health Poll finds many in U.S. lack knowledge about Ebola and its transmission News Press Release 2014

Authors
Odlum, M., & Yoon, S.
Affiliations
Columbia University Irving Medical Center (US)
Corresponding author
Yoon, Sunmoo
Venue
American Journal of Infection Control
Publisher
Elsevier BV
Published
2015-06
Volume
43
Issue
6
Pages
563-571
ISSN
0196-6553 (ISSN-L)
OpenAlex
W1512104599
Cited by
234
References
33
Citations by year
2026: 3 · 2025: 5 · 2024: 12 · 2023: 19 · 2022: 39 · 2021: 38 · 2020: 35 · 2019: 19 · 2018: 26 · 2017: 16 · 2016: 19 · 2015: 3
FWCI
16.73
Type
journal article
Language
en
Open access
Locations
  • American Journal of Infection Control (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Institute of Nursing Research (P30NR010677)
Topics
  • Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
  • Social Sciences › Health › Social Media in Health Education
  • Health Sciences › Medicine › Infectious Diseases › Viral Infections and Outbreaks Research
Keywords
Ebola outbreak, Twitter, social media surveillance, information dissemination, public knowledge and attitudes, 2014 West Africa Ebola outbreak, Nigeria, public health education, retweets
MeSH terms
Disease Outbreaks, Humans, Health Knowledge, Attitudes, Practice, Hemorrhagic Fever, Ebola, Information Dissemination, Social Media, Geographic Mapping, Public Health Surveillance

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review