This paper is in the following e-collection/theme issue:

Presentations, Abstracts, or Demonstrations (740)

Published on in Vol 8, No 1 (2016):

Identifying Depression-Related Tweets from Twitter for Public Health Monitoring

Identifying Depression-Related Tweets from Twitter for Public Health Monitoring

Identifying Depression-Related Tweets from Twitter for Public Health Monitoring

Authors of this article:

Danielle Mowery ;   Hilary A. Smith ;   Tyler Cheney ;   Craig Bryan ;   Michael Conway
The full text of this article is available as a PDF download by clicking here.
Article Authors Cited by (14) Tweetations Metrics

Journals

  1. DeJohn A, Schulz E, Pearson A, Lachmar E, Wittenborn A. Identifying and Understanding Communities Using Twitter to Connect About Depression: Cross-Sectional Study. JMIR Mental Health 2018;5(4):e61 View
  2. Kjell K, Johnsson P, Sikström S. Freely Generated Word Responses Analyzed With Artificial Intelligence Predict Self-Reported Symptoms of Depression, Anxiety, and Worry. Frontiers in Psychology 2021;12 View
  3. 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
  4. 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
  5. Karmegam D, Ramamoorthy T, Mappillairajan B. A Systematic Review of Techniques Employed for Determining Mental Health Using Social Media in Psychological Surveillance During Disasters. Disaster Medicine and Public Health Preparedness 2020;14(2):265 View
  6. Gruebner O, Lowe S, Sykora M, Shankardass K, Subramanian S, Galea S, Olson D. A novel surveillance approach for disaster mental health. PLOS ONE 2017;12(7):e0181233 View
  7. Hswen Y, Naslund J, Brownstein J, Hawkins J. Online Communication about Depression and Anxiety among Twitter Users with Schizophrenia: Preliminary Findings to Inform a Digital Phenotype Using Social Media. Psychiatric Quarterly 2018;89(3):569 View
  8. Schultebraucks K, Yadav V, Shalev A, Bonanno G, Galatzer-Levy I. Deep learning-based classification of posttraumatic stress disorder and depression following trauma utilizing visual and auditory markers of arousal and mood. Psychological Medicine 2022;52(5):957 View
  9. Bibi M, Aziz W, Almaraashi M, Khan I, Nadeem M, Habib N. A Cooperative Binary-Clustering Framework Based on Majority Voting for Twitter Sentiment Analysis. IEEE Access 2020;8:68580 View
  10. 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. Journal of Medical Internet Research 2017;19(2):e48 View
  11. Anwar M, Khoury D, Aldridge A, Parker S, Conway K. Using Twitter to Surveil the Opioid Epidemic in North Carolina: An Exploratory Study. JMIR Public Health and Surveillance 2020;6(2):e17574 View
  12. Dalal S, Jain S, Dave M. Review of Advancements in Depression Detection Using Social Media Data. IEEE Transactions on Computational Social Systems 2025;12(1):77 View

Books/Policy Documents

  1. Jindal R, Malhotra A. Proceedings of Data Analytics and Management. View
  2. Malhotra A, Jindal R. Recent Advances in Computational Intelligence Applications for Biometrics and Biomedical Devices. View