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

Infodemiology and Infoveillance (1008) JMIR Theme Issue: COVID-19 Special Issue (2094) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (825) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1276) Infectious Diseases (non-STD/STI) (1225) Mass Media/Social Media Communication and Campaigns (401) Theme Issue: Novel Coronavirus (COVID-19) Outbreak Rapid Reports (1331) Outbreak and Pandemic Preparedness and Management (1458)

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

Authors of this article:

Hyeju Jang 1, 2 Author Orcid Image ;   Emily Rempel 2 Author Orcid Image ;   David Roth 2 Author Orcid Image ;   Giuseppe Carenini 1 Author Orcid Image ;   Naveed Zafar Janjua 2, 3, 4 Author Orcid Image
Article Authors Cited by (61) Tweetations (9) Metrics

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
  2. Cuomo R, Purushothaman V, Li J, Cai M, Mackey T. A longitudinal and geospatial analysis of COVID-19 tweets during the early outbreak period in the United States. BMC Public Health 2021;21(1) View
  3. Zhang X, Yang Q, Albaradei S, Lyu X, Alamro H, Salhi A, Ma C, Alshehri M, Jaber I, Tifratene F, Wang W, Gojobori T, Duarte C, Gao X. Rise and fall of the global conversation and shifting sentiments during the COVID-19 pandemic. Humanities and Social Sciences Communications 2021;8(1) View
  4. Acosta M, Castillo-Sánchez G, Garcia-Zapirain B, de la Torre Díez I, Franco-Martín M. Sentiment Analysis Techniques Applied to Raw-Text Data from a Csq-8 Questionnaire about Mindfulness in Times of COVID-19 to Improve Strategy Generation. International Journal of Environmental Research and Public Health 2021;18(12):6408 View
  5. Urrutia D, Manetti E, Williamson M, Lequy E. Overview of Canada’s Answer to the COVID-19 Pandemic’s First Wave (January–April 2020). International Journal of Environmental Research and Public Health 2021;18(13):7131 View
  6. Hu T, Wang S, Luo W, Zhang M, Huang X, Yan Y, Liu R, Ly K, Kacker V, She B, Li Z. Revealing Public Opinion Towards COVID-19 Vaccines With Twitter Data in the United States: Spatiotemporal Perspective. Journal of Medical Internet Research 2021;23(9):e30854 View
  7. Alshahrani R, Babour A. An Infodemiology and Infoveillance Study on COVID-19: Analysis of Twitter and Google Trends. Sustainability 2021;13(15):8528 View
  8. Marcec R, Likic R. Using Twitter for sentiment analysis towards AstraZeneca/Oxford, Pfizer/BioNTech and Moderna COVID-19 vaccines. Postgraduate Medical Journal 2022;98(1161):544 View
  9. Jiang Q, Xue Y, Hu Y, Li Y. Public Social Media Discussions on Agricultural Product Safety Incidents: Chinese African Swine Fever Debate on Weibo. Frontiers in Psychology 2022;13 View
  10. Jafarzadeh H, Pauleen D, Abedin E, Weerasinghe K, Taskin N, Coskun M, Mehmood R. Making sense of COVID-19 over time in New Zealand: Assessing the public conversation using Twitter. PLOS ONE 2021;16(12):e0259882 View
  11. Cuenca-Zaldívar J, Torrente-Regidor M, Martín-Losada L, Fernández-De-Las-Peñas C, Florencio L, Sousa P, Palacios-Ceña D. Exploring Sentiment and Care Management of Hospitalized Patients During the First Wave of the COVID-19 Pandemic Using Electronic Nursing Health Records: Descriptive Study. JMIR Medical Informatics 2022;10(5):e38308 View
  12. Huangfu L, Mo Y, Zhang P, Zeng D, He S. COVID-19 Vaccine Tweets After Vaccine Rollout: Sentiment–Based Topic Modeling. Journal of Medical Internet Research 2022;24(2):e31726 View
  13. Wang S, Huang X, Hu T, She B, Zhang M, Wang R, Gruebner O, Imran M, Corcoran J, Liu Y, Bao S. A global portrait of expressed mental health signals towards COVID-19 in social media space. International Journal of Applied Earth Observation and Geoinformation 2023;116:103160 View
  14. Singhal A, Baxi M, Mago V. Synergy Between Public and Private Health Care Organizations During COVID-19 on Twitter: Sentiment and Engagement Analysis Using Forecasting Models. JMIR Medical Informatics 2022;10(8):e37829 View
  15. Liu L, Fu Y. Study on the mechanism of public attention to a major event: The outbreak of COVID-19 in China. Sustainable Cities and Society 2022;81:103811 View
  16. Jojoa M, Garcia-Zapirain B, Gonzalez M, Perez-Villa B, Urizar E, Ponce S, Tobar-Blandon M. Analysis of the Effects of Lockdown on Staff and Students at Universities in Spain and Colombia Using Natural Language Processing Techniques. International Journal of Environmental Research and Public Health 2022;19(9):5705 View
  17. Ueda M, Watanabe K, Sueki H. Emotional Distress During COVID-19 due to Mental Health Conditions and Economic Vulnerability: Retrospective Analysis of Survey-Linked Twitter Data With a Semisupervised Machine Learning Algorithm. Journal of Medical Internet Research 2023;25:e44965 View
  18. Golder S, Klein A, Magge A, O’Connor K, Cai H, Weissenbacher D, Gonzalez-Hernandez G. A chronological and geographical analysis of personal reports of COVID-19 on Twitter from the UK. DIGITAL HEALTH 2022;8:205520762210975 View
  19. Ogbuokiri B, Ahmadi A, Bragazzi N, Movahedi Nia Z, Mellado B, Wu J, Orbinski J, Asgary A, Kong J. Public sentiments toward COVID-19 vaccines in South African cities: An analysis of Twitter posts. Frontiers in Public Health 2022;10 View
  20. Grissette H, Nfaoui E. Semisupervised neural biomedical sense disambiguation approach for aspect-based sentiment analysis on social networks. Journal of Biomedical Informatics 2022;135:104229 View
  21. Alamoodi A, Zaidan B, Al-Masawa M, Taresh S, Noman S, Ahmaro I, Garfan S, Chen J, Ahmed M, Zaidan A, Albahri O, Aickelin U, Thamir N, Fadhil J, Salahaldin A. Multi-perspectives systematic review on the applications of sentiment analysis for vaccine hesitancy. Computers in Biology and Medicine 2021;139:104957 View
  22. Al-Garadi M, Yang Y, Sarker A. The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges. Healthcare 2022;10(11):2270 View
  23. Ding Q, Massey D, Huang C, Grady C, Lu Y, Cohen A, Matzner P, Mahajan S, Caraballo C, Kumar N, Xue Y, Dreyer R, Roy B, Krumholz H. Tracking Self-reported Symptoms and Medical Conditions on Social Media During the COVID-19 Pandemic: Infodemiological Study. JMIR Public Health and Surveillance 2021;7(9):e29413 View
  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
  26. Daradkeh M. Analyzing Sentiments and Diffusion Characteristics of COVID-19 Vaccine Misinformation Topics in Social Media. International Journal of Business Analytics 2021;9(3):1 View
  27. 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
  28. Wong-Padoongpatt G, Barrita A, King A, Strong M. The slow violence of racism on Asian Americans during the COVID-19 pandemic. Frontiers in Public Health 2022;10 View
  29. Huang X, Wang S, Zhang M, Hu T, Hohl A, She B, Gong X, Li J, Liu X, Gruebner O, Liu R, Li X, Liu Z, Ye X, Li Z. Social media mining under the COVID-19 context: Progress, challenges, and opportunities. International Journal of Applied Earth Observation and Geoinformation 2022;113:102967 View
  30. Wang Z, Chen Y, Li Y, Kakkar D, Guan W, Ji W, Cain J, Lan H, Sha D, Liu Q, Yang C. Public Opinions on COVID-19 Vaccines—A Spatiotemporal Perspective on Races and Topics Using a Bayesian-Based Method. Vaccines 2022;10(9):1486 View
  31. Chen X, Na J, Tan L, Chong M, Choy M. Exploring how online responses change in response to debunking messages about COVID-19 on WhatsApp. Online Information Review 2022;46(6):1184 View
  32. Aria M, Cuccurullo C, D’Aniello L, Misuraca M, Spano M. Thematic Analysis as a New Culturomic Tool: The Social Media Coverage on COVID-19 Pandemic in Italy. Sustainability 2022;14(6):3643 View
  33. Chiang Y, Chu M, Lin S, Cai X, Chen Q, Wang H, Li A, Rui J, Zhang X, Xie F, Lee C, Chen T. Capturing the Trajectory of Psychological Status and Analyzing Online Public Reactions During the Coronavirus Disease 2019 Pandemic Through Weibo Posts in China. Frontiers in Psychology 2021;12 View
  34. Li J, Pang P, Xiao Y, Wong D. Changes in Doctor–Patient Relationships in China during COVID-19: A Text Mining Analysis. International Journal of Environmental Research and Public Health 2022;19(20):13446 View
  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
  36. Mir A, Rathinam S, Gul S. Public perception of COVID-19 vaccines from the digital footprints left on Twitter: analyzing positive, neutral and negative sentiments of Twitterati. Library Hi Tech 2022;40(2):340 View
  37. Azizi F, Hajiabadi H, Vahdat-Nejad H, Khosravi M. Detecting and analyzing topics of massive COVID-19 related tweets for various countries. Computers and Electrical Engineering 2023;106:108561 View
  38. Shoeibi N, Shoeibi N, Hernández G, Chamoso P, Corchado J. AI-Crime Hunter: An AI Mixture of Experts for Crime Discovery on Twitter. Electronics 2021;10(24):3081 View
  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;9:205520762311583 View

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