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

Infoveillance and Social Listening (256) JMIR Theme Issue: COVID-19 Special Issue (2499) Infodemiology and Infoveillance (1463) Depression and Mood Disorders; Suicide Prevention (1527) Outbreak and Pandemic Preparedness and Management (1783) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1089)

Published on in Vol 1, No 1 (2021): Jan-Dec

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26769, first published .
Monitoring Depression Trends on Twitter During the COVID-19 Pandemic: Observational Study

Monitoring Depression Trends on Twitter During the COVID-19 Pandemic: Observational Study

Monitoring Depression Trends on Twitter During the COVID-19 Pandemic: Observational Study

Authors of this article:

Yipeng Zhang1 Author Orcid Image ;   Hanjia Lyu1 Author Orcid Image ;   Yubao Liu1 Author Orcid Image ;   Xiyang Zhang2 Author Orcid Image ;   Yu Wang1 Author Orcid Image ;   Jiebo Luo1 Author Orcid Image
Article Authors Cited by (67) Tweetations (13) Metrics

Journals

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  11. Peng X, Wang-Trexler N, Magagna W, Land S, Peck K. Learning Agility of Learning and Development Professionals in the Life Sciences Field During the COVID-19 Pandemic: Empirical Study. Interactive Journal of Medical Research 2022;11(1):e33360 View
  12. Cai R, Zhang J, Li Z, Zeng C, Qiao S, Li X. Using Twitter Data to Estimate the Prevalence of Symptoms of Mental Disorders in the United States During the COVID-19 Pandemic: Ecological Cohort Study. JMIR Formative Research 2022;6(12):e37582 View
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  20. Yahya N, Abdul Rahim H. Linguistic markers of depression: Insights from english-language tweets before and during the COVID-19 pandemic. Language and Health 2023;1(2):36 View
  21. Theocharopoulos P, Tsoukala A, Georgakopoulos S, Tasoulis S, Plagianakos V. Analysing sentiment change detection of Covid-19 tweets. Neural Computing and Applications 2023;35(29):21433 View
  22. Lotto M, Zakir Hussain I, Kaur J, Butt Z, Cruvinel T, Morita P. Analysis of Fluoride-Free Content on Twitter: Topic Modeling Study. Journal of Medical Internet Research 2023;25:e44586 View
  23. KVTKN P, Ramakrishnudu T. Semi-supervised approach for tweet-level stress detection. Natural Language Processing Journal 2023;4:100019 View
  24. Pananookooln C, Akaranee J, Silpasuwanchai C. Comparing Selective Masking Methods for Depression Detection in Social Media. Computational Linguistics 2023;49(3):525 View
  25. Xu Z, Su C, Xiao Y, Wang F. Artificial intelligence for COVID-19: battling the pandemic with computational intelligence. Intelligent Medicine 2022;2(1):13 View
  26. Thakur N, Patel K, Poon A, Shah R, Azizi N, Han C. A Comprehensive Analysis and Investigation of the Public Discourse on Twitter about Exoskeletons from 2017 to 2023. Future Internet 2023;15(10):346 View
  27. Lyu H, Fan Y, Xiong Z, Komisarchik M, Luo J. Understanding Public Opinion Toward the #StopAsianHate Movement and the Relation With Racially Motivated Hate Crimes in the US. IEEE Transactions on Computational Social Systems 2023;10(1):335 View
  28. Solans Noguero D, Ramírez-Cifuentes D, Ríssola E, Freire A. Gender Bias When Using Artificial Intelligence to Assess Anorexia Nervosa on Social Media: Data-Driven Study. Journal of Medical Internet Research 2023;25:e45184 View
  29. Beierle F, Pryss R, Aizawa A. Sentiments about Mental Health on Twitter—Before and during the COVID-19 Pandemic. Healthcare 2023;11(21):2893 View
  30. Price G, Heinz M, Song S, Nemesure M, Jacobson N. Using digital phenotyping to capture depression symptom variability: detecting naturalistic variability in depression symptoms across one year using passively collected wearable movement and sleep data. Translational Psychiatry 2023;13(1) View
  31. Prashanth K, Ramakrishnudu T. Sarcasm‐based tweet‐level stress detection. Expert Systems 2024;41(4) View
  32. Khoo L, Lim M, Chong C, McNaney R. Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches. Sensors 2024;24(2):348 View
  33. Nerella S, Bandyopadhyay S, Zhang J, Contreras M, Siegel S, Bumin A, Silva B, Sena J, Shickel B, Bihorac A, Khezeli K, Rashidi P. Transformers and large language models in healthcare: A review. Artificial Intelligence in Medicine 2024;154:102900 View
  34. Alshammari M, Al-Mamary Y, Abubakar A. Revolutionizing education: unleashing the power of social media in Saudi Arabian public universities. Humanities and Social Sciences Communications 2024;11(1) View
  35. Wang Y. Large language models for depression prediction. Proceedings of the National Academy of Sciences 2024;121(31) View
  36. Baqir A, Ali M, Jaffar S, Sherazi H, Lee M, Bashir A, Al Dabel M. Identifying COVID-19 survivors living with post-traumatic stress disorder through machine learning on Twitter. Scientific Reports 2024;14(1) View
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  39. Sinha G, Power S, Kursuncu U. Exploring patterns in online discussions into the lingering impact of COVID-19,  two years on. Discover Health Systems 2024;3(1) View
  40. Thamrin S, Chen E, Chen A. Detecting bipolar disorder on social media by post grouping and interpretable deep learning. Journal of Intelligent Information Systems 2025;63(1):161 View
  41. Owen D, Lynham A, Smart S, Pardiñas A, Camacho Collados J. AI for Analyzing Mental Health Disorders Among Social Media Users: Quarter-Century Narrative Review of Progress and Challenges. Journal of Medical Internet Research 2024;26:e59225 View
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  43. Rizwan M, Mushtaq M, Rafiq M, Mehmood A, Diez I, Villar M, Garay H, Ashraf I. Depression Intensity Classification from Tweets Using FastText Based Weighted Soft Voting Ensemble. Computers, Materials & Continua 2024;78(2):2047 View
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Books/Policy Documents

  1. Lefèvre T, Colineaux H, Morgand C, Tournois L, Delpierre C. Artificial Intelligence in Covid-19. View
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Conference Proceedings

  1. Kaseb A, Galal O, Elreedy D. 2022 4th Novel Intelligent and Leading Emerging Sciences Conference (NILES). Analysis on Tweets Towards COVID-19 Pandemic: An Application of Text-Based Depression Detection View
  2. Bankston J, Ma L. 2022 The 6th International Conference on Compute and Data Analysis. A Study on People's Mental Health on Twitter During the COVID-19 Pandemic View
  3. Madanian S, Rasoulipanah H, Yu J. 2023 Australasian Computer Science Week. Stress Detection on Social Network: Public Mental Health Surveillance View
  4. Liaw A, Chua H. 2022 IEEE International Conference on Artificial Intelligence in Engineering and Technology (IICAIET). Depression Detection on Social Media With User Network and Engagement Features Using Machine Learning Methods View
  5. Ahamed S, Shakil S, Lyu H, Zhang X, Luo J. 2022 IEEE International Conference on Big Data (Big Data). Doctors vs. Nurses: Understanding the Great Divide in Vaccine Hesitancy among Healthcare Workers View
  6. Zhou Y, Liew K, Yada S, Wakamiya S, Aramaki E. 2022 10th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW). Music Charts for Approximating Everyday Emotions: A Dataset of Daily Charts with Music Features from 106 Cities View
  7. Bashar M. 2022 7th International Conference on Intelligent Informatics and Biomedical Science (ICIIBMS). Tracking Public Depression from Tweets on COVID-19 and Its Comparison with Pre-pandemic Time View
  8. Bashar M. 2021 6th International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS). Exploration of Public Emotion Dynamics in Japan from Twitter Data during COVID-19 View
  9. Ramasamy T, Jagannathan J. 24TH TOPICAL CONFERENCE ON RADIO-FREQUENCY POWER IN PLASMAS. A systematic study of the machine learning and deep learning methods used in identifying depression among social media users View
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  12. Hossain M, Rahman A, Fazle Rabbi M. 2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT). COVID EmotionNet: A Machine Learning Approach to Unraveling Pandemic Sentiments View
  13. Kalateh S, Cardoso Oliveira I, Estrada-Jimenez L, Nikghadam Hojjati S, Barata J. 2024 16th International Conference on Human System Interaction (HSI). Emotional Elements in Scientific Publications' Abstracts: An Affective-Centric Experiment View
  14. Thamrin S, Chen A. 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI). Detection of Bipolar Disorder on Social Media Data Utilizing Biomedical, Clinical and Mental Health Domain Fine-Tuned Word Embeddings View