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

JMIR Theme Issue: COVID-19 Special Issue (4237) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (2710) Longitudinal and Cohort Studies in Public Health (944) Machine Learning (3305) Infectious Diseases (non-STD/STI) (1945) Data Science (721) Pulmonology, COPD (237) JMH Theme Issue: COVID-19 and Mental Health: Impact and Interventions 2023 (552) Data Sources and Open Data for Infodemiology (40) Patient Generated Health Data (26)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/54543, first published .
Woman in face mask coughing, holding chest, experiencing illness symptoms.

Exploring Psychological Trends in Populations With Chronic Obstructive Pulmonary Disease During COVID-19 and Beyond: Large-Scale Longitudinal Twitter Mining Study

Exploring Psychological Trends in Populations With Chronic Obstructive Pulmonary Disease During COVID-19 and Beyond: Large-Scale Longitudinal Twitter Mining Study

Authors of this article:

Chunyan Zhang1, 2 Author Orcid Image ;   Ting Wang3 Author Orcid Image ;   Caixia Dong2 Author Orcid Image ;   Duwei Dai2 Author Orcid Image ;   Linyun Zhou2 Author Orcid Image ;   Zongfang Li2 Author Orcid Image ;   Songhua Xu2 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

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  1. Zhou X, Chen Y, Qian K, Zhu H. Development and temporal prospective validation of an interpretable machine learning model for identifying depressive symptoms in patients with acute exacerbation of chronic obstructive pulmonary disease. Frontiers in Public Health 2026;14 View

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  1. Vara J, J P. 2025 7th International Conference on Inventive Material Science and Applications (ICIMA). A Review on COPD Prediction using Convolutional Neural Networks: Approaches, Challenges and Future Directions View