Identifying Protective Health Behaviors on Twitter: Observational Study of Travel Advisories and Zika Virus

Abstract

Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). 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.