JMIR Publications

ABSTRACT

Background:

An estimated 3.9 billion individuals live in a location endemic for common mosquito-borne diseases. The emergence of Zika virus in South America in 2015 marked the largest known Zika outbreak and caused hundreds of thousands of infections. Internet data have shown promise in identifying human behaviors relevant for tracking and understanding other diseases.

Objective:

Using Twitter posts regarding the 2015-16 Zika virus outbreak, we seek to identify and describe evidence of travel behavior changes., and additionally identify considerations and self-disclosures of a specific behavior change: travel cancellation. If this type of behavior is identifiable in Twitter, this approach may provide an additional source of data for disease modeling.

Methods:

We combine keyword filtering and machine learning classification to identify first-person reactions to Zika in 29,386 English-language tweets in the context of travel, including considerations and reports of a specific behavior change: travel cancellation. We further explore demographic, timeline, linguistic characteristics of those that change their behavior.

Results:

We explore the characteristics of individuals that report changes to their travel plans in response to the outbreak compared to control groups, finding differences in the demographics, linguistic patterns, and social networks of 1,567 identified individuals. We find significant differences between geographic areas in the United States, significantly more discussion by women than men, and some evidence that this might be explained by additional exposure to Zika-related information.

Conclusions:

Our findings have implications for informing the ways in which public health organizations communicate with the public on social media, and the findings contribute to our understanding of the ways in which the public perceives and acts on risks of emerging infectious diseases.