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Uncovering the relationships between military community health and affects expressed in social media

Journal Article · · EPJ Data Science
 [1];  [1];  [1];  [1]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
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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 dier 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 co-factors 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 geo-location and affect type. Altogether, 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.
Research Organization:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Organization:
Defense Threat Reduction Agency (DTRA); USDOE
Grant/Contract Number:
AC05-76RL01830
OSTI ID:
1364006
Alternate ID(s):
OSTI ID: 1406768
Report Number(s):
PNNL-SA--120565
Journal Information:
EPJ Data Science, Journal Name: EPJ Data Science Journal Issue: 1 Vol. 6; ISSN 2193-1127
Publisher:
SpringerCopyright Statement
Country of Publication:
United States
Language:
English

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Cited By (3)

Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations text January 2018
Forecasting influenza-like illness dynamics for military populations using neural networks and social media journal December 2017
Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations journal January 2019

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Related Subjects

60 APPLIED LIFE SCIENCES
97 MATHEMATICS AND COMPUTING
biosurveillance
emotion detection
emotion prediction
influenza
machine learning
natural language processing
opinion analysis
sentiment analysis
social media analytics