Information-seeking vs. sharing: Which explains regional health? An analysis of Google Search and Twitter trends. (June 2021)

• Record Type: Journal Article
• Authors: Jaidka, Kokil; Eichstaedt, Johannes; Giorgi, Salvatore; Schwartz, H. Andrew; Ungar, Lyle H
• Is Part Of: Telematics and informatics. Volume 59(2021)
• Publication Date: 2021-06
• Subjects: Google Trends -- Twitter -- Topic modeling -- Machine learning -- County health -- Information seeking -- Search behavior; Telecommunication -- Periodicals; Computer networks -- Periodicals; Télécommunications -- Périodiques; Réseaux d'ordinateurs -- Périodiques; 384
• DOI: 10.1016/j.tele.2020.101540
• Languages: English
• ISSNs: 0736-5853

Abstract

Highlights: This is the first study to offer a comparison between the predictive efficacy of public vs. private information behavior on the internet. This study uses the aggregate data from billions of individual signals of behavior to evaluate the predictive efficacy of such signals for a spatial analysis. Google significantly outperforms Twitter at predicting six out of eight chronic diseases and risky health behaviors. Google offers a platform for information seeking without social censoring. Twitter offers a platform for personal disclosures and social buffering. Abstract: Users' information-seeking and information-sharing behavior provide socioeconomic and psychological insights that are useful to understand regional trends in health. We study the spatial variations in aggregate Google Search and Twitter trends across 208 Designated Market Areas (DMAs) in the United States and their association with regional health. We find that information-seeking behavior from Google Trends data is better able to predict the prevalence of non-communicable diseases and impending behavioral risks, with an average gain of 19% over sociodemographics and regional controls, and of 15% over information-sharing behavior on Twitter. Both kinds of digital traces track cultural and socioeconomic contexts; however, information-seeking behavior provides insights into personal habits, while information-sharing provides psychological insights. Our findings can be applied to design online and offline human-centered health interventions that target at-risk populations through a knowledge of their lifestyle and concerns.