Published on in Vol 23, No 2 (2021): February

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/25431, first published .
Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis

Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis

Tracking COVID-19 Discourse on Twitter in North America: Infodemiology Study Using Topic Modeling and Aspect-Based Sentiment Analysis

Journals

  1. Liu J, Nie H, Li S, Chen X, Cao H, Ren J, Lee I, Xia F. Tracing the Pace of COVID-19 Research: Topic Modeling and Evolution. Big Data Research 2021;25:100236 View
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  24. Mitera H. Topic-Modeling-Ansätze für Social Media Kommunikation in der Coronapandemie. Information – Wissenschaft & Praxis 2022;73(4):197 View
  25. Beliga S, Martinčić-Ipšić S, Matešić M, Petrijevčanin Vuksanović I, Meštrović A. Infoveillance of the Croatian Online Media During the COVID-19 Pandemic: One-Year Longitudinal Study Using Natural Language Processing. JMIR Public Health and Surveillance 2021;7(12):e31540 View
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  35. Alabrah A, Alawadh H, Okon O, Meraj T, Rauf H. Gulf Countries’ Citizens’ Acceptance of COVID-19 Vaccines—A Machine Learning Approach. Mathematics 2022;10(3):467 View
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  39. Lloret-Pineda A, He Y, Haro J, Cristóbal-Narváez P. Types of Racism and Twitter Users’ Responses Amid the COVID-19 Outbreak: Content Analysis. JMIR Formative Research 2022;6(5):e29183 View
  40. Politis I, Georgiadis G, Kopsacheilis A, Nikolaidou A, Papaioannou P. Capturing Twitter Negativity Pre- vs. Mid-COVID-19 Pandemic: An LDA Application on London Public Transport System. Sustainability 2021;13(23):13356 View
  41. Kandasamy V, Trojovský P, Machot F, Kyamakya K, Bacanin N, Askar S, Abouhawwash M. Sentimental Analysis of COVID-19 Related Messages in Social Networks by Involving an N-Gram Stacked Autoencoder Integrated in an Ensemble Learning Scheme. Sensors 2021;21(22):7582 View
  42. Vyas P, Reisslein M, Rimal B, Vyas G, Basyal G, Muzumdar P. Automated Classification of Societal Sentiments on Twitter With Machine Learning. IEEE Transactions on Technology and Society 2022;3(2):100 View
  43. Ligo V, Chang C, Yi H. Contested solidarity and vulnerability in social media-based public responses to COVID-19 policies of mobility restrictions in Singapore: a qualitative analysis of temporal evolution. BMC Public Health 2021;21(1) View
  44. Kaiser T, Mögling I, Feldmann M, Hamm A, Brakemeier E. Fostering compliance with physical distancing by interactive feedback in the context of the COVID-19 pandemic: A web-based randomized controlled trial. Internet Interventions 2022;28:100545 View
  45. Basch C, Fera J, Pellicane A, Basch C. Handwashing videos on TikTok during the COVID-19 pandemic: Potential for disease prevention and health promotion. Infection, Disease & Health 2022;27(1):31 View
  46. Tsao S, MacLean A, Chen H, Li L, Yang Y, Butt Z. Public Attitudes During the Second Lockdown: Sentiment and Topic Analyses Using Tweets From Ontario, Canada. International Journal of Public Health 2022;67 View
  47. Lin J, Chien T, Yeh Y, Ho S, Chou W. Using sentiment analysis to identify similarities and differences in research topics and medical subject headings (MeSH terms) between Medicine (Baltimore) and the Journal of the Formosan Medical Association (JFMA) in 2020. Medicine 2022;101(11) View
  48. Bogdanowicz A, Guan C, Sasahara K. Dynamic topic modeling of twitter data during the COVID-19 pandemic. PLOS ONE 2022;17(5):e0268669 View
  49. Li T, Zeng Z, Sun J, Sun S. Using data mining technology to analyse the spatiotemporal public opinion of COVID-19 vaccine on social media. The Electronic Library 2022;40(4):435 View
  50. Lieneck C, Heinemann K, Patel J, Huynh H, Leafblad A, Moreno E, Wingfield C. Facilitators and Barriers of COVID-19 Vaccine Promotion on Social Media in the United States: A Systematic Review. Healthcare 2022;10(2):321 View
  51. Leung Y, Khalvati F. Exploring COVID-19–Related Stressors: Topic Modeling Study. Journal of Medical Internet Research 2022;24(7):e37142 View
  52. 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
  53. Sussman K, Bouchacourt L, Bright L, Wilcox G, Mackert M, Norwood A, Allport Altillo B. COVID-19 topics and emotional frames in vaccine hesitation: A social media text and sentiment analysis. DIGITAL HEALTH 2023;9 View
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  55. Lu J, Liu J. Communicating Concerns, Emotional Expressions, and Disparities on Ethnic Communities on Social Media During the COVID-19 Pandemic: A Structural Topic Modeling Approach. American Behavioral Scientist 2023:000276422311640 View
  56. Ng Q, Teo Y, Kiew C, Lim B, Lim Y, Liew T. Examining the Prevailing Negative Sentiments Surrounding Measles Vaccination: Unsupervised Deep Learning of Twitter Posts from 2017 to 2022. Cyberpsychology, Behavior, and Social Networking 2023;26(8):621 View
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  58. Oleschuk M, Maniotes C. “We Can ‘Break Bread’ Virtually:” Routinized and Ritualized Aspects of Family Food Provisioning in the United States During Lockdown. Journal of Family Communication 2023;23(3-4):310 View
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  60. Tian Y, Zhang W, Duan L, McDonald W, Osgood N. Comparison of pretrained transformer-based models for influenza and COVID-19 detection using social media text data in Saskatchewan, Canada. Frontiers in Digital Health 2023;5 View
  61. Saleh S, McDonald S, Basit M, Kumar S, Arasaratnam R, Perl T, Lehmann C, Medford R. Public perception of COVID-19 vaccines through analysis of Twitter content and users. Vaccine 2023;41(33):4844 View
  62. Neely S, Hao F. Diagnosis Disclosure and Peer-to-Peer Information Seeking Among COVID-19–Infected Social Media Users: Survey of US-Based Adults. JMIR Formative Research 2023;7:e48581 View
  63. Pillai M, Griffin A, Kronk C, McCall T. Toward Community-Based Natural Language Processing (CBNLP): Cocreating With Communities. Journal of Medical Internet Research 2023;25:e48498 View
  64. KÖÇERİ K. Yapay Zekânın Siyasi, Etik ve Toplumsal Açıdan Dezenformasyon Tehdidi. İletişim ve Diplomasi 2023;(11):247 View
  65. C. P, P. M. D. An Efficient CSPK-FCM Explainable Artificial Intelligence Model on COVID-19 Data to Predict the Emotion Using Topic Modeling. Journal of Advances in Information Technology 2023;14(6):1390 View
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  67. Guo F, Liu Z, Lu Q, Ji S, Zhang C. Public Opinion About COVID-19 on a Microblog Platform in China: Topic Modeling and Multidimensional Sentiment Analysis of Social Media. Journal of Medical Internet Research 2024;26:e47508 View
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Books/Policy Documents

  1. Küçük D, Arıcı N. Handbook of Research on Opinion Mining and Text Analytics on Literary Works and Social Media. View
  2. Liew X. Advances in Information Retrieval. View
  3. Tibbels N, Dosso A, Kwizera A, Benie W, Massingue F, Nana M, Naugle D. Communicating COVID-19. View
  4. Zhang W, Yang N, Li C, Li J. E-Business. New Challenges and Opportunities for Digital-Enabled Intelligent Future. View
  5. Wan Q, Wang F, Deng S. Information Management. View
  6. Strong M, Constantine M, Donovan A, Wong-Padoongpatt G. The COVID-19 Aftermath. View
  7. Lien Y, Wu W. Innovative Design and Engineering Applications of Intelligent Systems Under the Framework of Industry 4.0. View