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

Abstract

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:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE; Defense Threat Reduction Agency (DTRA)
OSTI Identifier:
1406768
Alternate Identifier(s):
OSTI ID: 1364006
Report Number(s):
PNNL-SA-120565
Journal ID: ISSN 2193-1127
Grant/Contract Number:
AC05-76RL01830
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
EPJ Data Science
Additional Journal Information:
Journal Volume: 6; Journal Issue: 1; Journal ID: ISSN 2193-1127
Publisher:
Springer
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

Citation Formats

Volkova, Svitlana, Charles, Lauren E., Harrison, Josh, and Corley, Courtney D. Uncovering the relationships between military community health and affects expressed in social media. United States: N. p., 2017. Web. doi:10.1140/epjds/s13688-017-0102-z.
Volkova, Svitlana, Charles, Lauren E., Harrison, Josh, & Corley, Courtney D. Uncovering the relationships between military community health and affects expressed in social media. United States. doi:10.1140/epjds/s13688-017-0102-z.
Volkova, Svitlana, Charles, Lauren E., Harrison, Josh, and Corley, Courtney D. Thu . "Uncovering the relationships between military community health and affects expressed in social media". United States. doi:10.1140/epjds/s13688-017-0102-z. https://www.osti.gov/servlets/purl/1406768.
@article{osti_1406768,
title = {Uncovering the relationships between military community health and affects expressed in social media},
author = {Volkova, Svitlana and Charles, Lauren E. and Harrison, Josh and Corley, Courtney D.},
abstractNote = {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.},
doi = {10.1140/epjds/s13688-017-0102-z},
journal = {EPJ Data Science},
number = 1,
volume = 6,
place = {United States},
year = {Thu Jun 08 00:00:00 EDT 2017},
month = {Thu Jun 08 00:00:00 EDT 2017}
}

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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 betweenmore » 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.« less
  • Social media can provide a resource for characterizing communities and targeted populations through activities and content shared online. For instance, studying the armed forces‚Äô use of social media may provide insights into their health and wellbeing. In this paper, we address three broad research questions: (1) How do military populations use social media? (2) What topics do military users discuss in social media? (3) Do military users talk about health and well-being differently than civilians? Military Twitter users were identified through keywords in the profile description of users who posted geotagged tweets at military installations. These military tweets were comparedmore » with the tweets from remaining population. Our analysis indicate that military users talk more about military related responsibilities and events, whereas non-military users talk more about school, work, and leisure activities. A significant difference in online content generated by both populations was identified, involving sentiment, health, language, and social media features.« less
  • This work is the first to take advantage of recurrent neural networks to predict influenza-like-illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data [1, 2] and the state-of-the-art machine learning models [3, 4], we build and evaluate the predictive power of Long Short Term Memory (LSTMs) architectures capable of nowcasting (predicting in \real-time") and forecasting (predicting the future) ILI dynamics in the 2011 { 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, stylistic and syntactic patterns,more » emotions and opinions, and communication behavior. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks. Finally, we combine ILI and social media signals to build joint neural network models for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance [1], specifically for military rather than general populations [3] in 26 U.S. and six international locations. Our approach demonstrates several advantages: (a) Neural network models learned from social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than syntactic and stylistic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns.« less
  • Social media can provide a resource for characterizing communities and small populations through activities and content shared online. For instance, studying the language use in social media within military populations may provide insights into their health and wellbeing. In this paper, we address three research questions: (1) How do military populations use social media? (2) What do military users discuss in social media? And (3) Do military users talk about health and well-being differently than civilians? Military Twitter users were identified through keywords in the profile description of users who posted geo-tagged tweets at military installations. The data was anonymizedmore » for the analysis. User profiles that belong to military population were compared to the nonmilitary population. Our results indicate that military users talk more about events in their military life, whereas nonmilitary users talk more about school, work, and leisure activities. We also found that the online content generated by both populations is significantly different, including health-related language and communication behavior.« less
  • Here, community resilience results from the collective output of a set of elements within contributing systems. Obvious ones are the physical elements of the supporting infrastructures; the rules and regulations under which they operate; and the economic mechanics by which they are developed, operated, and maintained.