Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-17T21:28:23.872Z Has data issue: false hasContentIssue false
  • English
  • Français

A Systematic Review of Techniques Employed for Determining Mental Health Using Social Media in Psychological Surveillance During Disasters

Published online by Cambridge University Press:  05 July 2019

Dhivya Karmegam ,
Thilagavathi Ramamoorthy  and
Bagavandas Mappillairajan
Dhivya Karmegam*
Affiliation:
Research Scholar, School of Public Health, SRM Institute of Science and Technology, Kattankulathur, Chennai
Thilagavathi Ramamoorthy
Affiliation:
Assistant Professor, Division of Biostatistics, School of Public Health, SRM Institute of Science and Technology, Kattankulathur, Chennai
Bagavandas Mappillairajan
Affiliation:
Professor, Division of Biostatistics, School of Public Health, SRM Institute of Science and Technology, Kattankulathur, Chennai
*
Correspondence and reprint requests to Dhivya Karmegam, Research Scholar, School of Public Health, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203 (e-mail: dhivya_karmegam@srmuniv.edu.in).
Get access Rights & Permissions [Opens in a new window]

Abstract

During disasters, people share their thoughts and emotions on social media and also provide information about the event. Mining the social media messages and updates can be helpful in understanding the emotional state of people during such unforeseen events as they are real-time data. The objective of this review is to explore the feasibility of using social media data for mental health surveillance as well as the techniques used for determining mental health using social media data during disasters. PubMed, PsycINFO, and PsycARTICLES databases were searched from 2009 to November 2018 for primary research studies. After screening and analyzing the records, 18 studies were included in this review. Twitter was the widely researched social media platform for understanding the mental health of people during a disaster. Psychological surveillance was done by identifying the sentiments expressed by people or the emotions they displayed in their social media posts. Classification of sentiments and emotions were done using lexicon-based or machine learning methods. It is not possible to conclude that a particular technique is the best performing one, because the performance of any method depends upon factors such as the disaster size, the volume of data, disaster setting, and the disaster web environment.

Keywords

disastersmental healthemotionpsychological surveillancesocial media
Type
Systematic Review
Information
Disaster Medicine and Public Health Preparedness , Volume 14 , Issue 2 , April 2020 , pp. 265 - 272
Copyright
© 2019 Society for Disaster Medicine and Public Health, Inc.

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

WHO. WHO | Mental health in emergencies | Fact Sheet. WHO Website. http://www.who.int/mediacentre/factsheets/fs383/en/ Published 2017. Accessed March 6, 2018.Google Scholar
Satcher, D, Friel, S, Bell, R.Natural and manmade disasters and mental health. JAMA. 2007;298(21):2540–2542. doi: 10.1001/jama.298.21.2540Google ScholarPubMed
Becker, SM.Psychosocial care for women survivors of the tsunami disaster in India. Am J Public Health. 2009;99(4):654–658.Google ScholarPubMed
Goldmann, E, Galea, S.Mental health consequences of disasters. Annu Rev Public Health. 2014;35(1):169–183.Google ScholarPubMed
Kuss, DJ, Griffiths, MD.Online social networking and addiction-A review of the psychological literature. Int J Environ Res Public Health. 2011;8(9):3528–3552.CrossRefGoogle ScholarPubMed
Gugan, I, Gnana, CS.A survey on utilising social and historical data for fast disaster response and recovery. Int J Control Theory Appl. 2017;10(21):1–6.Google Scholar
Imran, M, Castillo, C, Diaz, F, et al. A processing social media messages in mass emergency: a survey. ACM Comput Surv. 2015;47(4):1–38.Google Scholar
Anson, S, Watson, H, Wadhwa, K, et al. Analysing social media data for disaster preparedness: understanding the opportunities and barriers faced by humanitarian actors. Int J Disaster Risk Reduct. 2016;21:131–139. doi: 10.1016/j.ijdrr.2016.11.014Google Scholar
Verma, S, Vieweg, S, Corvey, WJ, et al. Natural language processing to the rescue? Extracting “Situational Awareness’’ tweets during mass emergency. In: Proceedings of the 5th International AAAI Conference on Weblogs and Social Media – Barcelona, Spain. 2011;385–392.Google Scholar
Yin, J, Lampert, A, Cameron, M, et al. Using social media to enhance emergent situation awareness. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence – Buenos Aires, Argentina. 2015;52–59.Google Scholar
Aupetit, M, Imran, M, Aupetit, M. Interactive monitoring of critical situational information on social media. In: Proceedings of the 14th ISCRAM Conference – Albi, France. 2017.Google Scholar
Kaigo, M.Social media usage during disasters and social capital: Twitter and the Great East Japan earthquake. Keio Commun Rev. 2012;34:19–35.Google Scholar
Kaewkitipong, L, Chen, CC, Ractham, P.A community-based approach to sharing knowledge before, during, and after crisis events: a case study from Thailand. Comput Human Behav. 2016;54:653–666.Google Scholar
Vikas. ICT and disaster management. Int J Public Adm Digit Age. 2017;4(3):29–41.Google Scholar
Imran, M, Elbassuoni, S, Castillo, C, et al. Extracting information nuggets from disaster-related messages in social media. In: Proceedings of the 10th International ISCRAM Conference – Baden-Baden, Germany. 2013;791–800.Google Scholar
Imran, M, Castillo, C, Lucas, J, et al. AIDR: artificial intelligence for disaster response. In: WWW ’14 Companion Proceedings of the 23rd International Conference on World Wide Web – Seoul, Korea. 2014;159–162.Google Scholar
Alshareef, HN, Grigoras, D.Using Twitter and the mobile cloud for delivering medical help in emergencies. Concurr Comput. 2017;29(24):e4151.Google Scholar
Alshareef, HN, Grigoras, D. Using social media and the mobile cloud to enhance emergency and risk management. In: 15th International Symposium on Parallel and Distributed Computing (ISPDC) – Fuzhou, China. 2016;92–99.Google Scholar
Okada, A, Ishida, Y, Yamauchi, N.Effectiveness of social media in disaster fundraising. Int J Public Adm Digit Age. 2017;4(1):49–68.Google Scholar
Ashktorab, Z, Brown, C, Nandi, M, et al. Tweedr: mining twitter to inform disaster response. In: Proceedings of the 11th International Conference on Information Systems for Crisis Response and Management – University Park, Pennsylvania. 2014;354–358.Google Scholar
Cervone, G, Schnebele, E, Waters, N, et al. Using social media and satellite data for damage assessment in urban areas during emergencies. In: Thakuriah, P, Tilahun, N, Zellner, M, eds. Seeing Cities Through Big Data. Springer Geography. Springer, Cham. 2017:443–457.CrossRefGoogle Scholar
O’Sullivan, TL, Kuziemsky, CE, Toal-Sullivan, D, et al. Unraveling the complexities of disaster management: a framework for critical social infrastructure to promote population health and resilience. Soc Sci Med. 2013;93:238–246.Google ScholarPubMed
David, CC, Ong, JC, Legara, EFT.Tweeting supertyphoon Haiyan: evolving functions of twitter during and after a disaster event. PLoS One. 2016;11(3):1–19.Google ScholarPubMed
Wongkoblap, A, Vadillo, MA, Curcin, V.Researching mental health disorders in the era of social media: systematic review. J Med Internet Res. 2017;19(6):e228. doi: 10.2196/jmir.7215Google ScholarPubMed
Liberati, A, Altman, DG, Tetzlaff, J, et al. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare interventions: explanation and elaboration. BMJ. 2009;339:b2700.CrossRefGoogle ScholarPubMed
Pluye, P, Robert, E, Cargo, M, Bartlett, G.Proposal: a mixed methods appraisal tool for systematic mixed studies reviews. Montréal: McGill Univ. 2011;(Part I):1–8.Google Scholar
Souto, RQ, Khanassov, V, Hong, QN, et al. Systematic mixed studies reviews: updating results on the reliability and efficiency of the mixed methods appraisal tool. Int J Nurs Stud. 2015;52(1):500–501.Google ScholarPubMed
Khalid, HM, Helander, MG, Hood, NA.Visualizing disaster attitudes resulting from terrorist activities. Appl Ergon. 2012;44(5):671–679. doi: 10.1016/j.apergo.2012.06.005Google ScholarPubMed
Vo, BH, Collier, N.Twitter emotion analysis in earthquake situations. Int J Comput Linguist Appl. 2013;4(1):159–173.Google Scholar
Schulz, A, Thanh, TD, Paulheim, H, Schweizer, I. A fine-grained sentiment analysis approach for detecting crisis related microposts. In: Proceedings of the 10th International ISCRAM Conference – Baden-Baden, Germany. 2013:846–851.Google Scholar
Glasgow, K, Fink, C, Boyd-Graber, J. “Our grief is unspeakable”: automatically measuring the community impact of a tragedy. In: Proceedings of the 8th International AAAI Conference on Weblogs and Social Media – Ann Arbor, Michigan. 2014:161–169.Google Scholar
De Choudhury, M, Monroy-Hernández, A, Gloria, AM. “Narco” emotions: affect and desensitization in social media during the Mexican Drug War. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems – Toronto, Canada. 2014:1–10.Google Scholar
Lu, Y, Hu, X, Wang, F, et al. Visualizing social media sentiment in disaster scenarios. In: WWW ’15 Companion Proceedings of the 24th International Conference on World Wide Web – Florence, Italy. 2015:1211–1215.Google Scholar
Woo, H, Cho, Y, Shim, E, et al. Public trauma after the Sewol ferry disaster: the role of social media in understanding the public mood. Int J Environ Res Public Health. 2015;12(9):10974–10983.Google ScholarPubMed
Doré, B, Ort, L, Braverman, O, et al. Sadness shifts to anxiety over time and distance from the national tragedy in Newtown, Connecticut. Psychol Sci. 2015;26(4):363–373.Google ScholarPubMed
Gaspar, R, Pedro, C, Panagiotopoulos, P, et al. Beyond positive or negative: qualitative sentiment analysis of social media reactions to unexpected stressful events. Comput Human Behav. 2016;56:179–191.Google Scholar
Gruebner, O, Sykora, M, Lowe, SR, et al. Mental health surveillance after the terrorist attacks in Paris. Lancet. 2016;387(10034):2195–2196.Google ScholarPubMed
Jones, NM, Wojcik, SP, Sweeting, J, et al. Tweeting negative emotion: an investigation of twitter data in the aftermath of violence on college campuses. Psychol Methods. 2016;21(4):526–541.Google ScholarPubMed
Hampton, AJ, Shalin, VL.Sentinels of breach: lexical choice as a measure of urgency in social media. Hum Factors. 2017;59(4):505–519.Google ScholarPubMed
Neppalli, VK, Caragea, C, Squicciarini, A, et al. Sentiment analysis during Hurricane Sandy in emergency response. Int J Disaster Risk Reduct. 2017;21(December 2016):213–222.Google Scholar
Lin, YR, Margolin, D, Wen, X.Tracking and analyzing individual distress following terrorist attacks using social media streams. Risk Anal. 2017;37(8):1580–1605.Google ScholarPubMed
Gruebner, O, Lowe, SR, Sykora, M, et al. A novel surveillance approach for disaster mental health. PLoS One. 2017;12(7):e0181233.Google ScholarPubMed
Su, LYF, Cacciatore, MA, Liang, X, et al. Analyzing public sentiments online: combining human- and computer-based content analysis. Inf Commun Soc. 2017;20(3):406–427.CrossRefGoogle Scholar
Van Lent, LGG, Sungur, H, Kunneman, FA, et al. Too far to care? measuring public attention and fear for ebola using twitter. J Med Internet Res. 2017;19(6):e193.Google ScholarPubMed
Gruebner, O, Lowe, S, Sykora, M, et al. Spatio-temporal distribution of negative emotions in New York City after a natural disaster as seen in social media. Int J Environ Res Public Health. 2018;15(10):2275.Google ScholarPubMed
Zimmer, M, Proferes, NJ.A topology of twitter research: disciplines, methods, and ethics. Aslib J Inf Manag. 2014;66(3):250–261.CrossRefGoogle Scholar
Meeder, B, Tam, J, Kelly, PG, et al. RT @IWantPrivacy: widespread violation of privacy settings in the twitter social network. Proceedings of the Web 2.0 Secur Priv. 2010;1–12.Google Scholar
Tausczik, YR, Pennebaker, JW.The psychological meaning of words: LIWC and computerized text analysis methods. J Lang Soc Psychol. 2010;29(1):24–54.Google Scholar
Sykora, MD, Jackson, TW, O’Brien, A, et al. Emotive ontology: extracting fine-grained emotions from terse, informal messages. IADIS Int J Comput Sci Inf Syst. 2013;8(2):106–118.Google Scholar
Duggan, M. Social Media Update 2016. Pew Research Center website. http://www.pewinternet.org/2015/08/19/the-demographics-of-social-media-users/. Published November 11 2016. Accessed June 19, 2018.Google Scholar
Morstatter, F, Pfeffer, J, Liu, H, et al. Is the sample good enough? Comparing data from Twitter’s streaming API with Twitter’s Firehose. In: 7th International AAAI Conference on Weblogs and Social Media – Cambridge, Massachusetts. 2013:400–408.Google Scholar
D’Andrea, W, Chiu, PH, Casas, BR, et al. Linguistic predictors of post-traumatic stress disorder symptoms following 11 September 2001. Appl Cogn Psychol. 2012;26(2):316–323.Google Scholar
Coppersmith, GA, Harman, CT, Dredze, MH. Measuring post traumatic stress disorder in Twitter. In: Proceedings of the 7th International AAAI Conference on Weblogs and Social Media – Cambridge, Massachusetts. 2014:23–45.Google Scholar
De Choudhury, M, Gamon, M, Counts, S, et al. Predicting depression via social media. In: Proceedings of the 7th International AAAI Conference on Weblogs and Social Media – Cambridge, Massachusetts. 2014:128–138.Google Scholar
Mowery, D, Smith, HA, Cheney, T, et al. Identifying depression-related tweets from Twitter for public health monitoring. Online J Public Health Inform. 2016;8(1):2579.Google Scholar
Waterloo, SF, Baumgartner, SE, Peter, J, et al. Norms of online expressions of emotion: comparing Facebook, Twitter, Instagram, and WhatsApp. New Media Soc. 2018;20(5):1813–1831.Google ScholarPubMed
Walker-Springett, K, Butler, C, Adger, WN.Wellbeing in the aftermath of floods. Health Place. 2017;43(October 2016):66–74.Google ScholarPubMed
Woods, C, West, C, Buettner, P, et al. Out of our control: living through Cyclone Yasi. Int J Qual Stud Health Well-being. 2014;9:19821.Google ScholarPubMed
Cerdá, M, Paczkowski, M, Galea, S, et al. Psychopathology in the aftermath of the Haiti earthquake: a population-based study of posttraumatic stress disorder and major depression. Depress Anxiety. 2013;30(5):413–424.Google ScholarPubMed
Reece, AG, Danforth, CM.Instagram photos reveal predictive markers of depression. EPJ Data Science. 2017;6:15. doi: 10.1140/epjds/s13688-017-0110-z.Google Scholar
Kang, K, Yoon, C, Kim, EY. Identifying depressive users in Twitter using multimodal analysis. In: 2016 International Conference on Big Data and Smart Computing (BigComp) – Hong Kong. 2016;231–238.Google Scholar
Lin, H, Jia, J, Guo, Q, et al. User-level psychological stress detection from social media using deep neural network. In: Proceedings of the 14th ACM International Conference on Multimedia – Santa Barbara, California. 2014;507–516.Google Scholar
Michael, V, Silenzio, B, States, NYU.Connecting the invisible dots: network-based methods to reach a hidden population at risk for suicide. Soc Sci Med. 2010;69(3):469–474.Google Scholar
Bollen, J, Gonçalves, B, Ruan, G, et al. Happiness is assortative in online social networks. Artif Life. 2011;17(3):237–251.Google ScholarPubMed
Wang, X, Zhang, C, Sun, L. An improved model for depression detection in micro-blog social network. In: Proceedings of the 2013 IEEE 13th International Conference on Data Mining Workshops – Dallas, Texas. 2013;80–87.Google Scholar
Supplementary material: File

Karmegam et al. supplementary material

Appendix S4

Download Karmegam et al. supplementary material(File)
File 19.1 KB
Supplementary material: File

Karmegam et al. supplementary material

Appendix S2

Download Karmegam et al. supplementary material(File)
File 24 KB
Supplementary material: File

Karmegam et al. supplementary material

Appendix S3

Download Karmegam et al. supplementary material(File)
File 27.2 KB
Supplementary material: File

Karmegam et al. supplementary material

Appendix S1

Download Karmegam et al. supplementary material(File)
File 18.7 KB