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Studying Military Community Health, Well-Being, and Discourse Through the Social Media Lens

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Public Health Intelligence and the Internet

Part of the book series: Lecture Notes in Social Networks ((LNSN))

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Abstract

Social media can provide a resource for characterizing communities and targeted populations through activities and content shared online. For instance, studying the armed forces’ use of social media may provide insights into their health and well-being. 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 geo-tagged tweets at military installations. These military tweets were compared with the tweets from remaining population. Our analysis indicates that military users talk more about military related responsibilities and events, whereas nonmilitary 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.

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Notes

  1. 1.

    http://www.militaryinstallations.dod.mil/.

  2. 2.

    Linguistic Inquiry and Word Count (LIWC): http://www.liwc.net.

References

  1. Ammari, T., Schoenebeck, S.: Understanding and supporting fathers and fatherhood on social media sites. In: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, pp. 1905–1914. ACM (2015)

    Google Scholar 

  2. De Choudhury, M., Counts, S., Horvitz, E.: Predicting postpartum changes in emotion and behavior via social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3267–3276. ACM (2013)

    Google Scholar 

  3. Delgado Valdes, J.M., Eisenstein, J., De Choudhury, M.: Psychological effects of urban crime gleaned from social media. In: Proceedings of ICWSM (Apr 2015). http://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/view/10563

  4. Lin, Y.R.: Assessing sentiment segregation in urban communities. In: Proceedings of the 2014 International Conference on Social Computing. SocialCom ‘14, pp. 9:1–9:8. ACM, New York, NY, USA (2014). http://doi.acm.org/10.1145/2639968.2640066

  5. Harris, J., Mansour, R., Choucair, B., Olson, J., Nissen, C., Bhatt, J., Brown, S.: Health department use of social media to identify foodborne illness-Chicago, Illinois, 2013–2014. MMWR Morb. Mortal. Wkly. Rep. 63(32), 681 (2014)

    Google Scholar 

  6. Paul, M.J., Dredze, M.: Drug extraction from the web: summarizing drug experiences with multi-dimensional topic models. In: Proceedings of HLT-NAACL, pp. 168–178 (2013)

    Google Scholar 

  7. Boehmer, T.K., Boothe, V.L., Flanders, W.D., Barrett, D.H.: Health-related quality of life of U.S. military personnel: a population-based study. Mil. Med 168(11), 941–947 (Nov 2003). http://search.proquest.com/docview/217055081?pq-origsite=gscholar

  8. Segal, D.R., Segal, M.W.: America’s military population, vol. 59. Population Reference Bureau Washington, DC (2004)

    Google Scholar 

  9. Office of the Deputy Assistant Secretary of Defense: Demographics report (2013) http://download.militaryonesource.mil/12038/MOS/Reports/2013-Demographics-Report.pdf

  10. Siebold, G.L.: Core issues and theory in military sociology. Journal of Political and Military Sociology 29(1), 140–159 (2001)

    Google Scholar 

  11. Schoenebeck, S.Y.: The Secret Life of Online Moms: Anonymity and Disinhibition on YouBeMom.com. In: Proceedings of ICWSM (2013)

    Google Scholar 

  12. Cui, A., Zhang, M., Liu, Y., Ma, S., Zhang, K.: Discover breaking events with popular hashtags in twitter. In: Proceedings of CIKM, pp. 1794–1798. ACM, New York, NY, USA (2012). http://doi.acm.org/10.1145/2396761.2398519

  13. Starbird, K., Maddock, J., Orand, M., Achterman, P., Mason, R.M.: Rumors, false flags, and digital vigilantes: misinformation on twitter after the 2013 Boston Marathon Bombing. In: Proceedings of Conference (2014)

    Google Scholar 

  14. Soni, S., Mitra, T., Gilbert, E., Eisenstein, J.: Modeling Factuality Judgments in Social Media Text. In: Proceedings of ACL. Baltimore, MD (2014), http://www.aclweb.org/anthology/P/P14/P14-2068.xhtml

  15. Coppersmith, G., Harman, C., Dredze, M.: Measuring post traumatic stress disorder in twitter. In: Proceedings of ICWSM (May 2014). http://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/paper/view/8079

  16. Dave Lee North America Technology: US military approves Android phones for soldiers (2013) http://www.bbc.com/news/technology-22395602

  17. Powers, R.: Can I use my cell phone during basic training? (Dec 2014). http://usmilitaryabout.com/od/armyjoin/a/basiccellphone.htm

  18. Tausczik, Y.R., Pennebaker, J.W.: The psychological meaning of words: LIWC and computerized text analysis methods. J. Lang Soc Psychol 29(1), 24–54 (2010)

    Article  Google Scholar 

  19. Pennebaker, J.W., Francis, M.E., Booth, R.J.: Linguistic inquiry and word count: LIWC 2001. Mahway: Lawrence Erlbaum Associates 71, 2001 (2001)

    Google Scholar 

  20. Eisenstein, J.: What to do about bad language on the internet. In: Proceedings of NAACL, pp. 359–369. ACL, Stroudsburg, Pennsylvania (2013). http://www.aclweb.org/anthology/N13-1037

  21. Han, B., Baldwin, T.: Lexical normalisation of short text messages: Makn sens a# twitter. In: Proceedings of HLT-Volume 1, pp. 368–378. ACL (2011)

    Google Scholar 

  22. Yang, Y., Eisenstein, J.: A log-linear model for unsupervised text normalization. In: Proceedings of EMNLP, pp. 61–72. ACL (2013)

    Google Scholar 

  23. Eisenstein, J., Ahmed, A., Xing, E.P.: Sparse additive generative models of text. In: Proceedings of ICML, Seattle, WA, pp. 1041–1048 (2011). http://www.icml-2011.org/papers/534_icmlpaper.pdf

  24. Diakopoulos, N.A., Shamma, D.A.: Characterizing debate performance via aggregated twitter sentiment. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1195–1198. ACM (2010)

    Google Scholar 

  25. Mei, Q., Ling, X., Wondra, M., Su, H., Zhai, C.: Topic sentiment mixture: modeling facets and opinions in weblogs. In: Proceedings of the 16th International Conference on World Wide Web, pp. 171–180. ACM (2007)

    Google Scholar 

  26. Corley, C.D., Cook, D.J., Mikler, A.R., Singh, K.P.: Text and structural data mining of influenza mentions in web and social media. Int. J. Environ. Res. Public Health 7(2), 596–615 (2010)

    Article  Google Scholar 

  27. Culotta, A.: Towards detecting influenza epidemics by analyzing twitter messages. In: Proceedings of the First Workshop on Social Media Analytics. pp. 115–122. ACM (2010)

    Google Scholar 

  28. Hutto, C.J., Gilbert, E.: VADER: A parsimonious rule-based model for sentiment analysis of social media text. In: Eighth International AAAI Conference on Weblogs and Social Media (2014). http://www.aaai.org/ocs/index.php/ICWSM/ICWSM14/ paper/view/8109

  29. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J Mach. Learn. Res. 3, 993–1022 (2003)

    MATH  Google Scholar 

  30. Rehurek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: Proceedings of LREC 2010 workshop New Challenges for NLP Frameworks, pp. 46–50. University of Malta, Valletta, Malta (2010). http://www.fi.muni.cz/usr/sojka/presentations/lrec2010-poster-rehurek-sojka.pdf

  31. Gray, G.C., Callahan, J.D., Hawksworth, A.W., Fisher, C.A., Gaydos, J.C.: Respiratory diseases among US military personnel: countering emerging threats. Emerg. Infect. Dis. 5(3), 379 (1999)

    Article  Google Scholar 

  32. Pflanz, S.: Occupational stress and psychiatric illness in the military: investigation of the relationship between occupational stress and mental illness among military mental health patients. Mil. Med. 166(6), 457 (2001)

    Google Scholar 

  33. Russell, K.L., Broderick, M.P., Franklin, S.E., Blyn, L.B., Freed, N.E., Moradi, E., Ecker, D.J., Kammerer, P.E., Osuna, M.A., Kajon, A.E.: others: transmission dynamics and prospective environmental sampling of adenovirus in a military recruit setting. J. Infect. Dis. 194(7), 877–885 (2006)

    Article  Google Scholar 

  34. Dunn, O.J.: Multiple comparisons among means. J. Am. Stat. Assoc. 56(293), 52–64 (1961)

    Article  MathSciNet  MATH  Google Scholar 

  35. Macskassy, S.A.: On the study of social interactions in twitter. In: Proceedings of ICWSM (2012)

    Google Scholar 

  36. Rand, D.G., Kraft-Todd, G., Gruber, J.: The collective benefits of feeling good and letting go: positive emotion and (dis) inhibition interact to predict cooperative behavior. PloS One 10(1), e0117426 (2015)

    Google Scholar 

  37. Pavalanathan, U., Eisenstein, J.: Confounds and consequences in geotagged Twitter data. In: Proceedings of EMNLP, pp. 2138–2148. ACL, Lisbon, Portugal (September 2015). http://aclweb.org/anthology/D15-1256

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Acknowledgements

The authors thank Commander Jean-Paul Chretien, Aaron Kite-Powell, and Vivek Khatri, who were at the Armed Forces Health Surveillance Branch, Defense Health Agency, for helpful discussions on the research and experimental design of this study.

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Authors and Affiliations

  1. Georgia Institute of Technology, Atlanta, GA, USA

    Umashanthi Pavalanathan

  2. Pacific Northwest National Laboratory, Richland, WA, USA

    Vivek Datla, Svitlana Volkova, Lauren Charles-Smith, Meg Pirrung, Josh Harrison, Alan Chappell & Courtney D. Corley

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  1. Umashanthi Pavalanathan
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Correspondence to Umashanthi Pavalanathan .

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Editors and Affiliations

  1. Department of Pediatrics, Center for Biomedical Informatics, The University of Tennessee Health Science Center—Oak-Ridge National Laboratory (UTHSC-ORNL), Memphis, Tennessee, USA

    Arash Shaban-Nejad

  2. Department of Pediatrics, Harvard Medical School, Boston Children’s Hospital, Harvard University, Boston, Massachusetts, USA

    John S. Brownstein

  3. Department of Epidemiology and Biostatistics and Occupational Health, McGill Clinical & Health Informatics, McGill University, Montreal, Québec, Canada

    David L. Buckeridge

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Pavalanathan, U. et al. (2017). Studying Military Community Health, Well-Being, and Discourse Through the Social Media Lens. In: Shaban-Nejad, A., Brownstein, J., Buckeridge, D. (eds) Public Health Intelligence and the Internet. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-68604-2_6

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  • DOI: https://doi.org/10.1007/978-3-319-68604-2_6

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  • Online ISBN: 978-3-319-68604-2

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