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Published on in Vol 2 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49531, first published .
Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach

Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach

Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach

Journals

  1. Azizi M, Jamali A, Spiteri R. Identifying X (Formerly Twitter) Posts Relevant to Dementia and COVID-19: Machine Learning Approach. JMIR Formative Research 2024;8:e49562 View
  2. Jamali A, Berger C, Spiteri R. Identification of depression predictors from standard health surveys using machine learning. Current Research in Behavioral Sciences 2024;7:100157 View
  3. Shah S, Gillani S, Baig M, Saleem M, Siddiqui M. Advancing depression detection on social media platforms through fine-tuned large language models. Online Social Networks and Media 2025;46:100311 View
  4. Chen H, Alfred M, Cohen E. Efficient Detection of Stigmatizing Language in Electronic Health Records via In-Context Learning: Comparative Analysis and Validation Study. JMIR Medical Informatics 2025;13:e68955 View

Books/Policy Documents

  1. Sarkar A, Bhattacharjee A, Priya A. Advanced Computational and Communication Paradigms. View

Conference Proceedings

  1. Chen Z, Lin J, Jiang J, Jing D. 2024 4th International Signal Processing, Communications and Engineering Management Conference (ISPCEM). A Method to Classify Emotions Based on BERT View