Published on in Vol 19, No 2 (2017): February

Understanding Depressive Symptoms and Psychosocial Stressors on Twitter: A Corpus-Based Study

Understanding Depressive Symptoms and Psychosocial Stressors on Twitter: A Corpus-Based Study

Understanding Depressive Symptoms and Psychosocial Stressors on Twitter: A Corpus-Based Study

Journals

  1. Dreisbach C, Koleck T, Bourne P, Bakken S. A systematic review of natural language processing and text mining of symptoms from electronic patient-authored text data. International Journal of Medical Informatics 2019;125:37 View
  2. Todorov G, Mayilvahanan K, Cain C, Cunha C. Context- and Subgroup-Specific Language Changes in Individuals Who Develop PTSD After Trauma. Frontiers in Psychology 2020;11 View
  3. Leis A, Ronzano F, Mayer M, Furlong L, Sanz F. Detecting Signs of Depression in Tweets in Spanish: Behavioral and Linguistic Analysis. Journal of Medical Internet Research 2019;21(6):e14199 View
  4. Gibbons J, Malouf R, Spitzberg B, Martinez L, Appleyard B, Thompson C, Nara A, Tsou M, Danforth C. Twitter-based measures of neighborhood sentiment as predictors of residential population health. PLOS ONE 2019;14(7):e0219550 View
  5. Booth J, Lin Y, Wei K. Neighborhood disadvantage, residents' distress, and online social communication: Harnessing Twitter data to examine neighborhood effects. Journal of Community Psychology 2018;46(7):829 View
  6. Acuña Caicedo R, Gómez Soriano J, Melgar Sasieta H. Assessment of supervised classifiers for the task of detecting messages with suicidal ideation. Heliyon 2020;6(8):e04412 View
  7. Kim S, Marsch L, Hancock J, Das A. Scaling Up Research on Drug Abuse and Addiction Through Social Media Big Data. Journal of Medical Internet Research 2017;19(10):e353 View
  8. Martinez L, Hughes S, Walsh-Buhi E, Tsou M. “Okay, We Get It. You Vape”: An Analysis of Geocoded Content, Context, and Sentiment regarding E-Cigarettes on Twitter. Journal of Health Communication 2018;23(6):550 View
  9. Yin Z, Sulieman L, Malin B. A systematic literature review of machine learning in online personal health data. Journal of the American Medical Informatics Association 2019;26(6):561 View
  10. Calvo R, Dinakar K, Picard R, Christensen H, Torous J. Toward Impactful Collaborations on Computing and Mental Health. Journal of Medical Internet Research 2018;20(2):e49 View
  11. Doan S, Yang E, Tilak S, Li P, Zisook D, Torii M. Extracting health-related causality from twitter messages using natural language processing. BMC Medical Informatics and Decision Making 2019;19(S3) View
  12. Barros J, Duggan J, Rebholz-Schuhmann D. The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review. Journal of Medical Internet Research 2020;22(3):e13680 View
  13. Seabrook E, Kern M, Fulcher B, Rickard N. Predicting Depression From Language-Based Emotion Dynamics: Longitudinal Analysis of Facebook and Twitter Status Updates. Journal of Medical Internet Research 2018;20(5):e168 View
  14. Sampson J, Kettunen J, Vuorinen R. The role of practitioners in helping persons make effective use of information and communication technology in career interventions. International Journal for Educational and Vocational Guidance 2020;20(1):191 View
  15. Hou Y, Liu Y, Liu C, Yan Z, Ma Q, Chen J, Zhang M, Yan Q, Li X, Chen J. Xiaoyaosan regulates depression‐related behaviors with physical symptoms by modulating Orexin A/OxR1 in the hypothalamus. The Anatomical Record 2020;303(8):2144 View
  16. Yeung D. Social Media as a Catalyst for Policy Action and Social Change for Health and Well-Being: Viewpoint. Journal of Medical Internet Research 2018;20(3):e94 View
  17. Velupillai S, Suominen H, Liakata M, Roberts A, Shah A, Morley K, Osborn D, Hayes J, Stewart R, Downs J, Chapman W, Dutta R. Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances. Journal of Biomedical Informatics 2018;88:11 View
  18. Yao X, Yu G, Tang J, Zhang J. Extracting depressive symptoms and their associations from an online depression community. Computers in Human Behavior 2021;120:106734 View
  19. Skaik R, Inkpen D. Using Social Media for Mental Health Surveillance. ACM Computing Surveys 2021;53(6):1 View
  20. Kelly D, Spaderna M, Hodzic V, Nair S, Kitchen C, Werkheiser A, Powell M, Liu F, Coppersmith G, Chen S, Resnik P. Blinded Clinical Ratings of Social Media Data are Correlated with In-Person Clinical Ratings in Participants Diagnosed with Either Depression, Schizophrenia, or Healthy Controls. Psychiatry Research 2020;294:113496 View
  21. Tao X, Chi O, Delaney P, Li L, Huang J. Detecting depression using an ensemble classifier based on Quality of Life scales. Brain Informatics 2021;8(1) View
  22. Athira B, Jones J, Idicula S, Kulanthaivel A, Zhang E. Annotating and detecting topics in social media forum and modelling the annotation to derive directions-a case study. Journal of Big Data 2021;8(1) View
  23. Le Glaz A, Haralambous Y, Kim-Dufor D, Lenca P, Billot R, Ryan T, Marsh J, DeVylder J, Walter M, Berrouiguet S, Lemey C. Machine Learning and Natural Language Processing in Mental Health: Systematic Review. Journal of Medical Internet Research 2021;23(5):e15708 View

Books/Policy Documents

  1. Ebert D, Harrer M, Apolinário-Hagen J, Baumeister H. Frontiers in Psychiatry. View
  2. Ismail N, Du M, Hu X. Social Web and Health Research. View
  3. Razak C, Zulkarnain M, Hamid S, Anuar N, Jali M, Meon H. Computational Science and Technology. View