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

Infoveillance and Social Listening (214) JMIR Theme Issue: COVID-19 Special Issue (2461) Infodemiology and Infoveillance (1369) Depression and Mood Disorders; Suicide Prevention (1392) Outbreak and Pandemic Preparedness and Management (1743) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1050)

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 (61) Tweetations (13) Metrics

Journals

  1. Xiong Z, Li P, Lyu H, Luo J. Social Media Opinions on Working From Home in the United States During the COVID-19 Pandemic: Observational Study. JMIR Medical Informatics 2021;9(7):e29195 View
  2. Elyashar A, Plochotnikov I, Cohen I, Puzis R, Cohen O. The State of Mind of Health Care Professionals in Light of the COVID-19 Pandemic: Text Analysis Study of Twitter Discourses. Journal of Medical Internet Research 2021;23(10):e30217 View
  3. Zhang X, Lyu H, Luo J. What Contributes to a Crowdfunding Campaign's Success? Evidence and Analyses from GoFundMe Data. Journal of Social Computing 2021;2(2):183 View
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  5. Li M, Hua Y, Liao Y, Zhou L, Li X, Wang L, Yang J. Tracking the Impact of COVID-19 and Lockdown Policies on Public Mental Health Using Social Media: Infoveillance Study. Journal of Medical Internet Research 2022;24(10):e39676 View
  6. Feng S, Kirkley A. Integrating online and offline data for crisis management: Online geolocalized emotion, policy response, and local mobility during the COVID crisis. Scientific Reports 2021;11(1) View
  7. Ali M, Baqir A, Husnain Raza Sherazi H, Hussain A, Hassan Alshehri A, Ali Imran M. Machine Learning Based Psychotic Behaviors Prediction from Facebook Status Updates. Computers, Materials & Continua 2022;72(2):2411 View
  8. Liu Y, Yin Z, Ni C, Yan C, Wan Z, Malin B. Examining Rural and Urban Sentiment Difference in COVID-19–Related Topics on Twitter: Word Embedding–Based Retrospective Study. Journal of Medical Internet Research 2023;25:e42985 View
  9. Weger R, Lossio-Ventura J, Rose-McCandlish M, Shaw J, Sinclair S, Pereira F, Chung J, Atlas L. Trends in Language Use During the COVID-19 Pandemic and Relationship Between Language Use and Mental Health: Text Analysis Based on Free Responses From a Longitudinal Study. JMIR Mental Health 2023;10:e40899 View
  10. Zhang S, Sun L, Zhang D, Li P, Liu Y, Anand A, Xie Z, Li D. The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States. Health Data Science 2022;2022 View
  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
  13. Rosato C, Moore R, Carter M, Heap J, Harris J, Storopoli J, Maskell S. Extracting Self-Reported COVID-19 Symptom Tweets and Twitter Movement Mobility Origin/Destination Matrices to Inform Disease Models. Information 2023;14(3):170 View
  14. Davidson P, Muniandy T, Karmegam D. Perception of COVID-19 vaccination among Indian Twitter users: computational approach. Journal of Computational Social Science 2023;6(2):541 View
  15. Lyu H, Imtiaz A, Zhao Y, Luo J. Human behavior in the time of COVID-19: Learning from big data. Frontiers in Big Data 2023;6 View
  16. Alswedani S, Mehmood R, Katib I, Altowaijri S. Psychological Health and Drugs: Data-Driven Discovery of Causes, Treatments, Effects, and Abuses. Toxics 2023;11(3):287 View
  17. Massell J, Lieb R, Meyer A, Mayor E, Cheong S. Fluctuations of psychological states on Twitter before and during COVID-19. PLOS ONE 2022;17(12):e0278018 View
  18. Stemmer M, Parmet Y, Ravid G. What are IBD Patients Talking About on Twitter? Using Natural Language Understanding to Investigate Patients’ Tweets. SN Computer Science 2023;4(4) View
  19. García-Noguez L, Tovar-Arriaga S, Paredes-García W, Ramos-Arreguín J, Aceves-Fernandez M. Automatic classification of depressive users on Twitter including temporal analysis. Network Modeling Analysis in Health Informatics and Bioinformatics 2023;12(1) View
  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
  37. Abdalla S, Galea S. Key considerations for the future of mental health epidemiology. American Journal of Epidemiology 2024;193(10):1307 View
  38. Zhang Z, Hua Y, Zhou P, Lin S, Li M, Zhang Y, Zhou L, Liao Y, Yang J. Sexual and Gender-Diverse Individuals Face More Health Challenges during COVID-19: A Large-Scale Social Media Analysis with Natural Language Processing. Health Data Science 2024;4 View
  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 2024 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
  42. Guo Z, Lai A, Thygesen J, Farrington J, Keen T, Li K. Large Language Models for Mental Health Applications: Systematic Review. JMIR Mental Health 2024;11:e57400 View

Books/Policy Documents

  1. Lefèvre T, Colineaux H, Morgand C, Tournois L, Delpierre C. Artificial Intelligence in Covid-19. View
  2. Klutse E, Nuamah-Amoabeng S, Lyu H, Luo J. Social, Cultural, and Behavioral Modeling. View
  3. Thakur N, Cho H, Cheng H, Lee H. HCI International 2023 – Late Breaking Papers. View
  4. Shwetha C, Pushpalatha K. ICT for Intelligent Systems. View
  5. Mendoza Palechor F, De la Hoz Manotas A, Neira-Rodado D. HCI International 2024 – Late Breaking Papers. View