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
  24. Garske S, Elayan S, Sykora M, Edry T, Grabenhenrich L, Galea S, Lowe S, Gruebner O. Space-Time Dependence of Emotions on Twitter after a Natural Disaster. International Journal of Environmental Research and Public Health 2021;18(10):5292 View
  25. Cohrdes C, Yenikent S, Wu J, Ghanem B, Franco-Salvador M, Vogelgesang F. Indications of Depressive Symptoms During the COVID-19 Pandemic in Germany: Comparison of National Survey and Twitter Data. JMIR Mental Health 2021;8(6):e27140 View
  26. Viviani M, Crocamo C, Mazzola M, Bartoli F, Carrà G, Pasi G. Assessing vulnerability to psychological distress during the COVID-19 pandemic through the analysis of microblogging content. Future Generation Computer Systems 2021;125:446 View
  27. Rook L, Mazza M, Lefter I, Brazier F. Toward Linguistic Recognition of Generalized Anxiety Disorder. Frontiers in Digital Health 2022;4 View
  28. Galbraith E, Li J, Rio-Vilas V, Convertino M. In.To. COVID-19 socio-epidemiological co-causality. Scientific Reports 2022;12(1) View
  29. FOWLER J, MADAN A, BRUCE C, FRUEH B, KASH B, JONES S, SASANGOHAR F. Improving Psychiatric Care Through Integrated Digital Technologies. Journal of Psychiatric Practice 2021;27(2):92 View
  30. Pilipiec P, Liwicki M, Bota A. Using Machine Learning for Pharmacovigilance: A Systematic Review. Pharmaceutics 2022;14(2):266 View
  31. Malhotra A, Jindal R. Deep learning techniques for suicide and depression detection from online social media: A scoping review. Applied Soft Computing 2022;130:109713 View
  32. Blanco G, Lourenço A. Optimism and pessimism analysis using deep learning on COVID-19 related twitter conversations. Information Processing & Management 2022;59(3):102918 View
  33. Acuña Caicedo R, Gómez Soriano J, Melgar Sasieta H. Bootstrapping semi-supervised annotation method for potential suicidal messages. Internet Interventions 2022;28:100519 View
  34. Noraset T, Chatrinan K, Tawichsri T, Thaipisutikul T, Tuarob S. Language-agnostic deep learning framework for automatic monitoring of population-level mental health from social networks. Journal of Biomedical Informatics 2022;133:104145 View
  35. Kang J, Kim J, Kim T, Song H, Han J. Experiencing Stress During COVID-19: A Computational Analysis of Stressors and Emotional Responses to Stress. Cyberpsychology, Behavior, and Social Networking 2022;25(9):561 View
  36. Shakeri Hossein Abad Z, Butler G, Thompson W, Lee J. Physical Activity, Sedentary Behavior, and Sleep on Twitter: Multicountry and Fully Labeled Public Data Set for Digital Public Health Surveillance Research. JMIR Public Health and Surveillance 2022;8(2):e32355 View
  37. Gallegos Salazar L, Loyola-González O, Medina-Pérez M. An Explainable Approach Based on Emotion and Sentiment Features for Detecting People with Mental Disorders on Social Networks. Applied Sciences 2021;11(22):10932 View
  38. Pilipiec P, Samsten I, Bota A, Rocha L. Surveillance of communicable diseases using social media: A systematic review. PLOS ONE 2023;18(2):e0282101 View
  39. Zarate D, Stavropoulos V, Ball M, de Sena Collier G, Jacobson N. Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence. BMC Psychiatry 2022;22(1) View
  40. Nanath K, Balasubramanian S, Shukla V, Islam N, Kaitheri S. Developing a mental health index using a machine learning approach: Assessing the impact of mobility and lockdown during the COVID-19 pandemic. Technological Forecasting and Social Change 2022;178:121560 View
  41. Rezaeian A, Agha Akbari L, Amirzadeh F, MalekMohammadi N. The Effectiveness of Acceptance and Commitment Therapy on Distress Tolerance and Depression in Students. Quarterly Journal of Child Mental Health 2021;8(4):94 View
  42. Eaton M, Probst Y, Smith M. Characterizing the Discourse of Popular Diets to Describe Information Dispersal and Identify Leading Voices, Interaction, and Themes of Mental Health: Social Network Analysis. JMIR Infodemiology 2023;3:e38245 View
  43. Norman P, Pickering C. Discourse about national parks on social media: Insights from Twitter. Journal of Outdoor Recreation and Tourism 2023;44:100682 View
  44. Zarate D, Ball M, Prokofieva M, Kostakos V, Stavropoulos V. Identifying self-disclosed anxiety on Twitter: A natural language processing approach. Psychiatry Research 2023;330:115579 View
  45. Zhang J, Xu W, Lei C, Pu Y, Zhang Y, Zhang J, Yu H, Su X, Huang Y, Gong R, Zhang L, Shi Q. Using Clinician-Patient WeChat Group Communication Data to Identify Symptom Burdens in Patients With Uterine Fibroids Under Focused Ultrasound Ablation Surgery Treatment: Qualitative Study. JMIR Formative Research 2023;7:e43995 View
  46. Malhotra A, Jindal R. XAI Transformer based Approach for Interpreting Depressed and Suicidal User Behavior on Online Social Networks. Cognitive Systems Research 2024;84:101186 View
  47. Królak A, Wiktorski T, Żmudzińska A. Automatic analysis of X (Twitter) data for supporting depression diagnosis. Human Technology 2023;19(3):370 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
  4. Dey J, Sarkar A, Karforma S. Recent Trends in Computational Intelligence Enabled Research. View
  5. Ingram W, Khanna R, Weston C. Mental Health Informatics. View
  6. Usharani B, Goyal L. Predictive Analytics of Psychological Disorders in Healthcare. View
  7. Thakur N, Cho H, Cheng H, Lee H. HCI International 2023 – Late Breaking Papers. View