Published on in Vol 7, No 2 (2019): February

Preprints (earlier versions) of this paper are available at, first published .
Using Twitter to Detect Psychological Characteristics of Self-Identified Persons With Autism Spectrum Disorder: A Feasibility Study

Using Twitter to Detect Psychological Characteristics of Self-Identified Persons With Autism Spectrum Disorder: A Feasibility Study

Using Twitter to Detect Psychological Characteristics of Self-Identified Persons With Autism Spectrum Disorder: A Feasibility Study


  1. Potier R. The Digital Phenotyping Project: A Psychoanalytical and Network Theory Perspective. Frontiers in Psychology 2020;11 View
  2. Mavragani A, Ochoa G. Google Trends in Infodemiology and Infoveillance: Methodology Framework. JMIR Public Health and Surveillance 2019;5(2):e13439 View
  3. Moura I, Teles A, Silva F, Viana D, Coutinho L, Barros F, Endler M. Mental health ubiquitous monitoring supported by social situation awareness: A systematic review. Journal of Biomedical Informatics 2020;107:103454 View
  4. Stens O, Weisman M, Simard J, Reuter K. Insights From Twitter Conversations on Lupus and Reproductive Health: Protocol for a Content Analysis. JMIR Research Protocols 2020;9(8):e15623 View
  5. Bellon-Harn M, Ni J, Manchaiah V. Twitter usage about autism spectrum disorder. Autism 2020;24(7):1805 View
  6. Hswen Y, Hawkins J, Sewalk K, Tuli G, Williams D, Viswanath K, Subramanian S, Brownstein J. Racial and Ethnic Disparities in Patient Experiences in the United States: 4-Year Content Analysis of Twitter. Journal of Medical Internet Research 2020;22(8):e17048 View
  7. Reuter K, Danve A, Deodhar A. Harnessing the power of social media: how can it help in axial spondyloarthritis research?. Current Opinion in Rheumatology 2019;31(4):321 View
  8. Reuter K, Lee D. Perspectives Toward Seeking Treatment Among Patients With Psoriasis: Protocol for a Twitter Content Analysis. JMIR Research Protocols 2021;10(2):e13731 View
  9. Hassrick E, Holmes L, Sosnowy C, Walton J, Carley K. Benefits and Risks: A Systematic Review of Information and Communication Technology Use by Autistic People. Autism in Adulthood 2021;3(1):72 View
  10. Reuter K, Deodhar A, Makri S, Zimmer M, Berenbaum F, Nikiphorou E. The impact of the COVID-19 pandemic on people with rheumatic and musculoskeletal diseases: insights from patient-generated data on social media. Rheumatology 2021;60(SI):SI77 View
  11. Bunyan A, Venuturupalli S, Reuter K. Expressed Symptoms and Attitudes Toward Using Twitter for Health Care Engagement Among Patients With Lupus on Social Media: Protocol for a Mixed Methods Study. JMIR Research Protocols 2021;10(5):e15716 View
  12. Thorpe Huerta D, Hawkins J, Brownstein J, Hswen Y. Exploring discussions of health and risk and public sentiment in Massachusetts during COVID-19 pandemic mandate implementation: A Twitter analysis. SSM - Population Health 2021;15:100851 View
  13. 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
  14. Wesson P, Hswen Y, Valdes G, Stojanovski K, Handley M. Risks and Opportunities to Ensure Equity in the Application of Big Data Research in Public Health. Annual Review of Public Health 2022;43(1):59 View
  15. Quiroga Gutierrez A, Lindegger D, Taji Heravi A, Stojanov T, Sykora M, Elayan S, Mooney S, Naslund J, Fadda M, Gruebner O. Reproducibility and Scientific Integrity of Big Data Research in Urban Public Health and Digital Epidemiology: A Call to Action. International Journal of Environmental Research and Public Health 2023;20(2):1473 View
  16. Gauld C, Maquet J, Micoulaud-Franchi J, Dumas G. Popular and Scientific Discourse on Autism: Representational Cross-Cultural Analysis of Epistemic Communities to Inform Policy and Practice. Journal of Medical Internet Research 2022;24(6):e32912 View
  17. Harvey P, Depp C, Rizzo A, Strauss G, Spelber D, Carpenter L, Kalin N, Krystal J, McDonald W, Nemeroff C, Rodriguez C, Widge A, Torous J. Technology and Mental Health: State of the Art for Assessment and Treatment. American Journal of Psychiatry 2022;179(12):897 View
  18. Prakash J, Chaudhury S, Chatterjee K. Digital phenotyping in psychiatry: When mental health goes binary. Industrial Psychiatry Journal 2021;30(2):191 View
  19. Potier R. Revue critique sur le potentiel du numérique dans la recherche en psychopathologie : un point de vue psychanalytique. L'Évolution Psychiatrique 2022;87(4):729 View
  20. Reuter K, Angyan P, Le N, Buchanan T. Using Patient-Generated Health Data From Twitter to Identify, Engage, and Recruit Cancer Survivors in Clinical Trials in Los Angeles County: Evaluation of a Feasibility Study. JMIR Formative Research 2021;5(11):e29958 View
  21. Mo C, Yin J, Fung I, Tse Z. Aggregating Twitter Text through Generalized Linear Regression Models for Tweet Popularity Prediction and Automatic Topic Classification. European Journal of Investigation in Health, Psychology and Education 2021;11(4):1537 View
  22. Yu H, Yang C, Yu P, Liu K, Patel S. Emotion diffusion effect: Negative sentiment COVID-19 tweets of public organizations attract more responses from followers. PLOS ONE 2022;17(3):e0264794 View
  23. Kelley S, Mhaonaigh C, Burke L, Whelan R, Gillan C. Machine learning of language use on Twitter reveals weak and non-specific predictions. npj Digital Medicine 2022;5(1) View
  24. Koss J, Rheinlaender A, Truebel H, Bohnet-Joschko S. Social media mining in drug development—Fundamentals and use cases. Drug Discovery Today 2021;26(12):2871 View
  25. Macenski C, Hamel M, McDougle C, Thom R. Challenges and Strategies to Mitigate Problematic Social Media Use in Psychiatric Disorders. Harvard Review of Psychiatry 2021;29(6):409 View
  26. Liang Y, Liu L, Ji Y, Huangfu L, Zeng D. Identifying emotional causes of mental disorders from social media for effective intervention. Information Processing & Management 2023;60(4):103407 View
  27. Khorasani M, Kahani M, Yazdi S, Hajiaghaei-Keshteli M. Towards finding the lost generation of autistic adults: A deep and multi-view learning approach on social media. Knowledge-Based Systems 2023;276:110724 View
  28. Thakur N. Investigating and Analyzing Self-Reporting of Long COVID on Twitter: Findings from Sentiment Analysis. Applied System Innovation 2023;6(5):92 View

Books/Policy Documents

  1. Ingram W, Khanna R, Weston C. Mental Health Informatics. View
  2. Sheridan K, Allen K, Vine Foggo R, Hurem A, Leif E, Freeman N. Research and Teaching in a Pandemic World. View