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

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 ofmore » 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.« less
Authors:
 [1] ;  [1] ;  [1] ;  [1]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Publication Date:
Report Number(s):
PNNL-SA-120565
Journal ID: ISSN 2193-1127
Grant/Contract Number:
AC05-76RL01830
Type:
Accepted Manuscript
Journal Name:
EPJ Data Science
Additional Journal Information:
Journal Volume: 6; Journal Issue: 1; Journal ID: ISSN 2193-1127
Publisher:
Springer
Research Org:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org:
USDOE; Defense Threat Reduction Agency (DTRA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 97 MATHEMATICS AND COMPUTING; social media analytics; machine learning; natural language processing; emotion detection; sentiment analysis; biosurveillance; influenza; opinion analysis; emotion prediction
OSTI Identifier:
1406768
Alternate Identifier(s):
OSTI ID: 1364006