Published on in Vol 5, No 4 (2021): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/23593, first published .
Concerns Discussed on Chinese and French Social Media During the COVID-19 Lockdown: Comparative Infodemiology Study Based on Topic Modeling

Concerns Discussed on Chinese and French Social Media During the COVID-19 Lockdown: Comparative Infodemiology Study Based on Topic Modeling

Concerns Discussed on Chinese and French Social Media During the COVID-19 Lockdown: Comparative Infodemiology Study Based on Topic Modeling

Journals

  1. Al-Rawi A, Zemenchik K. Sex Workers’ Lived Experiences With COVID-19 on Social Media: Content Analysis of Twitter Posts. JMIR Formative Research 2022;6(7):e36268 View
  2. Mitera H. Topic-Modeling-Ansätze für Social Media Kommunikation in der Coronapandemie. Information – Wissenschaft & Praxis 2022;73(4):197 View
  3. Kim M, Noh Y, Yamada A, Hong S. Comparison of the Erectile Dysfunction Drugs Sildenafil and Tadalafil Using Patient Medication Reviews: Topic Modeling Study. JMIR Medical Informatics 2022;10(2):e32689 View
  4. Renner S, Loussikian P, Foulquié P, Arnould B, Marrel A, Barbier V, Mebarki A, Schück S, Bharmal M. Perceived Unmet Needs in Patients Living With Advanced Bladder Cancer and Their Caregivers: Infodemiology Study Using Data From Social Media in the United States. JMIR Cancer 2022;8(3):e37518 View
  5. Chen A, Zhang J, Liao W, Luo C, Shen C, Feng B. Multiplicity and dynamics of social representations of the COVID-19 pandemic on Chinese social media from 2019 to 2020. Information Processing & Management 2022;59(4):102990 View
  6. Chin H, Lima G, Shin M, Zhunis A, Cha C, Choi J, Cha M. User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis. Journal of Medical Internet Research 2023;25:e40922 View
  7. Constant A, Conserve D, Gallopel-Morvan K, Raude J. Cognitive Factors Associated With Public Acceptance of COVID-19 Nonpharmaceutical Prevention Measures: Cross-sectional Study. JMIRx Med 2022;3(2):e32859 View
  8. Hu M, Conway M. Perspectives of the COVID-19 Pandemic on Reddit: Comparative Natural Language Processing Study of the United States, the United Kingdom, Canada, and Australia. JMIR Infodemiology 2022;2(2):e36941 View
  9. Faviez C, Talmatkadi M, Foulquié P, Mebarki A, Schück S, Burgun A, Chen X. Assessment of the Early Detection of Anosmia and Ageusia Symptoms in COVID-19 on Twitter: Retrospective Study. JMIR Infodemiology 2023;3:e41863 View
  10. Haupt M, Chiu M, Chang J, Li Z, Cuomo R, Mackey T, Cresci S. Detecting nuance in conspiracy discourse: Advancing methods in infodemiology and communication science with machine learning and qualitative content coding. PLOS ONE 2023;18(12):e0295414 View

Books/Policy Documents

  1. Sarker A. Natural Language Processing in Biomedicine. View