Uncovering the relationships between military community health and affects expressed in social media
Data Sciences and Analytics, National Security Directorate, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, 99354, USA
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Accepted: 22 May 2017
Published online: 8 June 2017
Military populations present a small, unique community whose mental and physical health impacts the security of the nation. Recent literature has explored social media’s ability to enhance disease surveillance and characterize distinct communities with encouraging results. We present a novel analysis of the relationships between influenza-like illnesses (ILI) clinical data and affects (i.e., emotions and sentiments) extracted from social media around military facilities. Our analyses examine (1) differences in affects expressed by military and control populations, (2) affect changes over time by users, (3) differences in affects expressed during high and low ILI seasons, and (4) correlations and cross-correlations between ILI clinical visits and affects from an unprecedented scale - 171M geo-tagged tweets across 31 global geolocations. Key findings include: Military and control populations differ in the way they express affects in social media over space and time. Control populations express more positive and less negative sentiments and less sadness, fear, disgust, and anger emotions than military. However, affects expressed in social media by both populations within the same area correlate similarly with ILI visits to military health facilities. We have identified potential responsible cofactors leading to location variability, e.g., region or state locale, military service type and/or the ratio of military to civilian populations. For most locations, ILI proportions positively correlate with sadness and neutral sentiment, which are the affects most often expressed during high ILI season. The ILI proportions negatively correlate with fear, disgust, surprise, and positive sentiment. These results are similar to the low ILI season where anger, surprise, and positive sentiment are highest. Finally, cross-correlation analysis shows that most affects lead ILI clinical visits, i.e. are predictive of ILI data, with affect-ILI leading intervals dependent on geolocation and affect type. Overall, information gained in this study exemplifies a usage of social media data to understand the correlation between psychological behavior and health in the military population and the potential for use of social media affects for prediction of ILI cases.
Key words: social media analytics / machine learning / natural language processing / emotion detection / sentiment analysis / biosurveillance / influenza
© The Author(s), 2017