Currently submitted to: JMIR Formative Research
Date Submitted: Mar 30, 2026
Open Peer Review Period: Apr 17, 2026 - Jun 12, 2026
(currently open for review)
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Integrating Social Media Sentiment and Mobility Data for Public Health Surveillance: The Role of Emotion in COVID-19 Policy Compliance
ABSTRACT
Background:
Background:
Digital data streams, including social media and mobility tracking, offer new opportunities for real-time public health surveillance during rapidly evolving crises such as the COVID-19 pandemic. Public emotional responses, captured through social media platforms, may influence compliance with public health interventions, yet their interaction with policy measures remains insufficiently understood.
Objective:
Objective:
This study examines the relationship between population-level stress measured by negative emotions, public health policy implementation, and mobility behavior during the early stages of the COVID-19 pandemic in the United States, with a focus on how emotional responses interact with stay-at-home orders to shape behavioral outcomes.
Methods:
Methods:
We conducted a county-level longitudinal ecological analysis using publicly available data from February to April 2020. Public stress levels were measured using geolocated Twitter data on negative emotions toward COVID-19, while mobility behavior was assessed using SafeGraph social distancing metrics as a proxy for staying at home. Public health policy exposure was operationalized as the number of days stay-at-home orders were in effect. Random-effects regression models were used to evaluate associations between emotional signals, policy duration, and mobility, including interaction effects. Models controlled for demographic, socioeconomic, epidemiological, and political factors.
Results:
Results:
Higher levels of stress, characterized by negative emotions, were significantly linked to greater reductions in mobility (β=18.83, p<0.001). The duration of stay-at-home orders also positively correlated with decreased mobility and notably moderated the relationship between stress levels and mobility (β=0.82, p<0.001), suggesting that emotional responses intensified the impact of policy measures. Other interventions, such as school and business closures, demonstrated less consistent associations with mobility.
Conclusions:
Conclusions:
This study demonstrates the value of integrating social media-derived emotional signals with mobility data for public health surveillance. Emotional responses appear to play a critical role in shaping behavioral compliance, particularly when reinforced by clear policy interventions. These findings suggest that incorporating real-time emotional monitoring into public health strategy may improve the effectiveness of policy communication and implementation during future health emergencies.
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Copyright
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