Skip to main content
SpringerLink
Log in

Social Media and Psychological Disorder

  • Chapter
  • First Online:
Social Web and Health Research

Abstract

Globally, hundreds of millions of people are estimated living with depression reported by the World Health Organization (WHO). Even though medical technology has improved, a large proportion of sufferers are still receiving improper diagnosis and treatment. Mental illness is endured a burden interfering with emotions, feelings, and various aspects of life. It is a complex disorder that considerably affects physical health particularly severe headache, eating disorder, weakened the immune system, and sleeping disruptions on a constant basis. The traditional procedures for mental illness diagnosis typically rely solely on depression test, self-reported, and family-reported on unusual behaviors. Mental illness is considered taboo to be discussed openly hence the reluctance to seek medical attention. Thus, social media is an ideal alternative for mental illness detection by identifying the symptoms from the users’ activities on social media. In this chapter, the related studies of mental illness on social media are explored and discussed. From previous work, online users with mental health problem have been spotted taking depression screening tests, participation in online forums, and often sharing about themselves on social media. The patterns of linguistic style extracted from selected techniques are used to distinguish mentally ill users from the virtual population. The trained models will assist to classify depressed users and prospective depression users through automated monitoring system on social media.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
eBook
USD 16.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Murray CJ, Lopez AD. Alternative projections of mortality and disability by cause 1990–2020: global burden of disease study. Lancet. 1997;349(9064):1498–504.

    Article  CAS  PubMed  Google Scholar 

  2. Fava M, Labbate LA, Abraham ME, Rosenbaum JF. Hypothyroidism and hyperthyroidism in major depression revisited. J Clin Psychiatry. 1995;56(5):186–92.

    CAS  PubMed  Google Scholar 

  3. Reich M, Lesur A, Perdrizet-Chevallier C. Depression, quality of life and breast cancer: a review of the literature. Breast Cancer Res Treat. 2008;110(1):9–17.

    Article  CAS  PubMed  Google Scholar 

  4. Jiang W, Davidson JR. Antidepressant therapy in patients with ischemic heart disease. Am Heart J. 2005;150(5):871–81.

    Article  CAS  PubMed  Google Scholar 

  5. Holt RI, De Groot M, Golden SH. Diabetes and depression. Curr Diab Rep. 2014;14(6):491.

    Article  PubMed  PubMed Central  Google Scholar 

  6. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 5th ed. Washington, DC: American Psychiatric Publishing; 2013. p. 160–8.

    Book  Google Scholar 

  7. Goetzel RZ, Roemer EC, Holingue C, Fallin MD, McCleary K, Eaton W, et al. Mental health in the workplace: a call to action proceedings from the mental health in the workplace: public health summit. J Occup Environ Med. 2018;60(4):322.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Martin A, Rief W, Klaiberg A, Braehler E. Validity of the brief patient health questionnaire mood scale (PHQ-9) in the general population. Gen Hosp Psychiatry. 2006;28(1):71–7.

    Article  PubMed  Google Scholar 

  9. Kroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606–13.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Howes C, Purver M, McCabe R. Linguistic indicators of severity and progress in online text-based therapy for depression. In: Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. 2014. p. 7–16.

    Google Scholar 

  11. Spence J, Titov N, Dear BF, Johnston L, Solley K, Lorian C, et al. Randomized controlled trial of Internet-delivered cognitive behavioral therapy for posttraumatic stress disorder. Depress Anxiety. 2011;28(7):541–50.

    Article  PubMed  Google Scholar 

  12. Hann D, Winter K, Jacobsen P. Measurement of depressive symptoms in cancer patients: evaluation of the Center for Epidemiological Studies Depression Scale (CES-D). J Psychosom Res. 1999;46(5):437–43.

    Article  CAS  PubMed  Google Scholar 

  13. De Choudhury M, Gamon M, Counts S, Horvitz E. Predicting depression via social media. ICWSM. 2013;13:1–10.

    Google Scholar 

  14. Winzelberg AJ, Classen C, Alpers GW, Roberts H, Koopman C, Adams RE, et al. Evaluation of an internet support group for women with primary breast cancer. Cancer. 2003;97(5):1164–73.

    Article  PubMed  Google Scholar 

  15. Park M, Cha C, Cha M.. Depressive moods of users portrayed in Twitter. In: Proceedings of the ACM SIGKDD Workshop on healthcare informatics (HI-KDD), Vol. 2012. New York, NY: ACM; 2012. p. 1–8.

    Google Scholar 

  16. Beck AT, Steer RA, Brown GK. Beck depression inventory-II. San Antonio. 1996;78(2):490–8.

    Google Scholar 

  17. Richardson LP, McCauley E, Grossman DC, McCarty CA, Richards J, Russo JE, et al. Evaluation of the Patient Health Questionnaire-9 Item for detecting major depression among adolescents. Pediatrics. 2010;126(6):1117–23.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Quiñones AR, Thielke SM, Clark ME, Phillips KM, Elnitsky C, Andresen EM. Validity of Center for Epidemiologic Studies Depression (CES-D) scale in a sample of Iraq and Afghanistan Veterans. SAGE open medicine. 2016;4:2050312116643906.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Mitchell M, Hollingshead K, Coppersmith G. Quantifying the language of schizophrenia in social media. In: Proceedings of the 2nd workshop on Computational linguistics and clinical psychology: From linguistic signal to clinical reality. 2015. p. 11–20.

    Google Scholar 

  20. Coppersmith G, Dredze M, Harman C. Quantifying mental health signals in Twitter. In: Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. 2014. p. 51–60.

    Google Scholar 

  21. Coppersmith G, Dredze M, Harman C, Hollingshead K, Mitchell M. CLPsych 2015 shared task: depression and PTSD on Twitter. In: Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. 2015. p. 31–9.

    Google Scholar 

  22. Preoţiuc-Pietro D, Eichstaedt J, Park G, Sap M, Smith L, Tobolsky V, et al. The role of personality, age, and gender in tweeting about mental illness. In: Proceedings of the 2nd workshop on computational linguistics and clinical psychology: From linguistic signal to clinical reality. 2015. p. 21–30.

    Google Scholar 

  23. Alao AO, Soderberg M, Pohl EL, Alao AL. Cybersuicide: review of the role of the internet on suicide. Cyberpsychol Behav. 2006;9(4):489–93.

    Article  PubMed  Google Scholar 

  24. Bagroy S, Kumaraguru P, De Choudhury M. A social media based index of mental well-being in college campuses. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. ACM; 2017. p. 1634–46.

    Google Scholar 

  25. Hsiung RC. The best of both worlds: an online self-help group hosted by a mental health professional. Cyberpsychol Behav. 2000;3(6):935–50.

    Article  Google Scholar 

  26. Kummervold PE, Gammon D, Bergvik S, Johnsen JAK, Hasvold T, Rosenvinge JH. Social support in a wired world: use of online mental health forums in Norway. Nord J Psychiatry. 2002;56(1):59–65.

    Article  PubMed  Google Scholar 

  27. Webb M, Burns J, Collin P. Providing online support for young people with mental health difficulties: challenges and opportunities explored. Early Interv Psychiatry. 2008;2(2):108–13.

    Article  PubMed  Google Scholar 

  28. Barney LJ, Griffiths KM, Banfield MA. Explicit and implicit information needs of people with depression: a qualitative investigation of problems reported on an online depression support forum. BMC Psychiatry. 2011;11(1):88.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Johnsen JAK, Rosenvinge JH, Gammon D. Online group interaction and mental health: an analysis of three online discussion forums. Scand J Psychol. 2002;43(5):445–9.

    Article  PubMed  Google Scholar 

  30. Hwang JD, Hollingshead K. Crazy mad nutters: the language of mental health. In: Proceedings of the Third Workshop on Computational Lingusitics and Clinical Psychology. 2016. p. 52–62.

    Google Scholar 

  31. Coppersmith G, Dredze M, Harman C, Hollingshead K. From ADHD to SAD: Analyzing the language of mental health on Twitter through self-reported diagnoses. In: Proceedings of the 2nd Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality. 2015. p. 1–10.

    Google Scholar 

  32. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. 4th ed., Text Revision). Washington, DC: Author; 2000.

    Google Scholar 

  33. Saleem S, Prasad R, Vitaladevuni S, Pacula M, Crystal M, Marx B, et al. Automatic detection of psychological distress indicators and severity assessment from online forum posts. In: Proceedings of COLING 2012. 2012. p. 2375–88.

    Google Scholar 

  34. Mowery D, Smith H, Cheney T, Stoddard G, Coppersmith G, Bryan C, Conway M. Understanding depressive symptoms and psychosocial stressors on Twitter: a corpus-based study. J Med Internet Res. 2017;19(2)

    Article  PubMed  PubMed Central  Google Scholar 

  35. Cavazos-Rehg PA, Krauss MJ, Sowles S, Connolly S, Rosas C, Bharadwaj M, Bierut LJ. A content analysis of depression-related tweets. Comput Hum Behav. 2016;54:351–7.

    Article  Google Scholar 

  36. American Psychiatric Association. Diagnostic and statistical manual of mental disorders: DSM-5. Washington, DC: American Psychiatric Association; 2013.

    Book  Google Scholar 

  37. Coppersmith GA, Harman CT, Dredze MH. Measuring post traumatic stress disorder in Twitter. In: ICWSM. 2014.

    Google Scholar 

  38. Benton A, Mitchell M, Hovy D. Multi-task learning for mental health using social media text. arXiv preprint arXiv:1712.03538. 2017.

    Google Scholar 

  39. Cavnar WB, Trenkle JM. N-gram-based text categorization. Ann arbor mi. 1994;48113(2):161–75.

    Google Scholar 

  40. Pavalanathan U, De Choudhury M. Identity management and mental health discourse in social media. In: Proceedings of the 24th International Conference on World Wide Web. ACM; 2015. p. 315–21.

    Google Scholar 

  41. Cook BL, Progovac AM, Chen P, Mullin B, Hou S, Baca-Garcia E. Novel use of natural language processing (NLP) to predict suicidal ideation and psychiatric symptoms in a text-based mental health intervention in Madrid. Comput Math Methods Med. 2016;2016:8708434.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Pennebaker JW, Boyd RL, Jordan K, Blackburn K. The development and psychometric properties of LIWC2015. 2015.

    Google Scholar 

  43. De Choudhury M, De S. Mental health discourse on reddit: self-disclosure, social support, and anonymity. In: ICWSM. 2014.

    Google Scholar 

  44. Hu X, et al. Unsupervised sentiment analysis with emotional signals. In: Proceedings of the 22nd international conference on World Wide Web. ACM; 2013.

    Google Scholar 

  45. Hu X, et al. Exploiting social relations for sentiment analysis in microblogging. In: Proceedings of the sixth ACM international conference on Web search and data mining. ACM; 2013.

    Google Scholar 

  46. Zaydman M. Tweeting about mental health: big data text analysis of twitter for public policy. The Pardee RAND Graduate School; 2017.

    Google Scholar 

  47. Wang X, Zhang C, Ji Y, Sun L, Wu L, Bao Z. A depression detection model based on sentiment analysis in micro-blog social network. In: Pacific-Asia conference on knowledge discovery and data mining. Berlin, Heidelberg: Springer; 2013. p. 201–13.

    Google Scholar 

  48. Olsson V, Lindow M.. How does bipolar and depressive diagnoses reflect in linguistic usage on Twitter: a study using LIWC and other tools. 2018.

    Google Scholar 

  49. Mishra V, Garg T. A systematic study on predicting depression using text analytics. J Fund Appl Sci. 2018;10(2).

    Google Scholar 

  50. Alegría M, Chatterji P, Wells K, Cao Z, Chen CN, Takeuchi D, et al. Disparity in depression treatment among racial and ethnic minority populations in the United States. Psychiatr Serv. 2008;59(11):1264–72.

    Article  PubMed  PubMed Central  Google Scholar 

  51. Hoge CW, Castro CA, Messer SC, McGurk D, Cotting DI, Koffman RL. Combat duty in Iraq and Afghanistan, mental health problems, and barriers to care. N Engl J Med. 2004;351(1):13–22.

    Article  CAS  PubMed  Google Scholar 

  52. Witten IH, Frank E, Hall MA, Pal CJ. Data mining: practical machine learning tools and techniques. San Francisco, CA: Morgan Kaufmann; 2016.

    Google Scholar 

  53. Domingos P. A few useful things to know about machine learning. Commun ACM. 2012;55(10):78–87.

    Article  Google Scholar 

  54. Brodley CE, Rebbapragada U, Small K, Wallace B. Challenges and opportunities in applied machine learning. AI Mag. 2012;33(1):11–24.

    Article  Google Scholar 

  55. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436.

    Article  CAS  PubMed  Google Scholar 

  56. Orabi AH, Buddhitha P, Orabi MH, Inkpen D. Deep learning for depression detection of Twitter users. In: Proceedings of the Fifth Workshop on Computational Linguistics and Clinical Psychology: From Keyboard to Clinic. 2018. p. 88–97.

    Google Scholar 

  57. Joshi DJ, Makhija M, Nabar Y, Nehete N, Patwardhan MS. Mental health analysis using deep learning for feature extraction. In: Proceedings of the ACM India Joint International Conference on Data Science and Management of Data. ACM; 2018. p. 356–9.

    Google Scholar 

  58. Gkotsis G, Oellrich A, Velupillai S, Liakata M, Hubbard TJ, Dobson RJ, Dutta R. Characterisation of mental health conditions in social media using informed deep learning. Sci Rep. 2017;7:45141.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Sadeque F, Xu D, Bethard S. Measuring the latency of depression detection in social media. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining. ACM; 2018. p. 495–503.

    Google Scholar 

  60. Du M, Liu N, Hu X. Techniques for interpretable machine learning. arXiv preprint arXiv:1808.00033. 2018.

    Google Scholar 

  61. Jin H, Song Q, Hu X. Efficient neural architecture search with network Morphism. arXiv preprint arXiv:1806.10282. 2018.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Computer Science and Engineering Department, Texas A&M University, College Station, TX, USA

    Nur Hafieza Ismail, Mengnan Du & Xia Hu

Authors
  1. Nur Hafieza Ismail
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mengnan Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xia Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xia Hu .

Editor information

Editors and Affiliations

  1. Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA

    Jiang Bian

  2. Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, FL, USA

    Yi Guo

  3. School of Information, Florida State University, Tallahassee, FL, USA

    Zhe He

  4. Computer Science and Engineering Department, Texas A&M University, College Station, TX, USA

    Xia Hu

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Ismail, N.H., Du, M., Hu, X. (2019). Social Media and Psychological Disorder. In: Bian, J., Guo, Y., He, Z., Hu, X. (eds) Social Web and Health Research. Springer, Cham. https://doi.org/10.1007/978-3-030-14714-3_9

Download citation

Publish with us

Policies and ethics

Search

Navigation