What can we learn about the Ebola outbreak from tweets?

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.

Keywords: Data mining; Ebola outbreak; Social media.

Fig 1

Time trends in tweets mentioning Ebola in an early stage of world emergency response.

Fig 2

Geographical location of disseminated tweets mentioning Ebola and Chikungunya (Top). Number of tweets generated on July 30^th, 2014 in Caribbean area and Africa (Bottom).

Fig 3

Time of first Nigerian Ebola case on Twitter, Ministry of Health, and CDC

Fig 4

Four topics of tweets mentioning Ebola. N-gram and frequency broken down by cluster. Color shows details about cluster. Size is normalized by clusters and shows frequency of the n-Gram.

Fig 5

An example of a person’s perception on the day of the Ebola emergency declaration by WHO. (A person received ten alerts and expressed his opinion on Twitter)