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Citing this Article

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Published on 12.09.17 in Vol 19, No 9 (2017): September

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

Works citing "Enhancing Seasonal Influenza Surveillance: Topic Analysis of Widely Used Medicinal Drugs Using Twitter Data"

Ireneus Kagashe, Zhijun Yan, Imran Suheryani

J Med Internet Res 2017 (Sep 12); 19(9):e315 

According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.7393):

(note that this is only a small subset of citations)

  1. Wang J, Deng H, Liu B, Hu A, Liang J, Fan L, Zheng X, Wang T, Lei J. Systematic Evaluation of Research Progress on Natural Language Processing in Medicine Over the Past 20 Years: Bibliometric Study on PubMed. Journal of Medical Internet Research 2020;22(1):e16816
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  2. . Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206
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  3. Alessa A, Faezipour M. Flu Outbreak Prediction Using Twitter Posts Classification and Linear Regression With Historical Centers for Disease Control and Prevention Reports: Prediction Framework Study. JMIR Public Health and Surveillance 2019;5(2):e12383
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  4. Puri N, Coomes EA, Haghbayan H, Gunaratne K. Social media and vaccine hesitancy: new updates for the era of COVID-19 and globalized infectious diseases. Human Vaccines & Immunotherapeutics 2020;16(11):2586
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  5. Mavragani A, Sampri A, Sypsa K, Tsagarakis KP. Integrating Smart Health in the US Health Care System: Infodemiology Study of Asthma Monitoring in the Google Era. JMIR Public Health and Surveillance 2018;4(1):e24
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  6. . The Role of Pharmacist in the Health Care System: Current Scenario in India. Borneo Journal of Pharmacy 2020;3(2):84
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  7. Gao J, Zhang Y, Zhou T. Computational socioeconomics. Physics Reports 2019;817:1
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  8. Mahroum N, Adawi M, Sharif K, Waknin R, Mahagna H, Bisharat B, Mahamid M, Abu-Much A, Amital H, Luigi Bragazzi N, Watad A, Manogaran G. Public reaction to Chikungunya outbreaks in Italy—Insights from an extensive novel data streams-based structural equation modeling analysis. PLOS ONE 2018;13(5):e0197337
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  9. Weissenbacher D, Sarker A, Klein A, O’Connor K, Magge A, Gonzalez-Hernandez G. Deep neural networks ensemble for detecting medication mentions in tweets. Journal of the American Medical Informatics Association 2019;26(12):1618
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  10. Logghe HJ, Selby LV, Boeck MA, Stamp NL, Chuen J, Jones C. The academic tweet: Twitter as a tool to advance academic surgery. Journal of Surgical Research 2018;226:viii
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  11. Huang M, ElTayeby O, Zolnoori M, Yao L. Public Opinions Toward Diseases: Infodemiological Study on News Media Data. Journal of Medical Internet Research 2018;20(5):e10047
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  12. Liang F, Guan P, Wu W, Huang D. Forecasting influenza epidemics by integrating internet search queries and traditional surveillance data with the support vector machine regression model in Liaoning, from 2011 to 2015. PeerJ 2018;6:e5134
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  13. Amith M, Cohen T, Cunningham R, Savas LS, Smith N, Cuccaro P, Gabay E, Boom J, Schvaneveldt R, Tao C. Mining HPV Vaccine Knowledge Structures of Young Adults From Reddit Using Distributional Semantics and Pathfinder Networks. Cancer Control 2020;27(1):107327481989144
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  14. Junwei K, Yang H, Junjiang L, Zhijun Y. Dynamic prediction of cardiovascular disease using improved LSTM. International Journal of Crowd Science 2019;3(1):14
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  15. Du J, Cunningham RM, Xiang Y, Li F, Jia Y, Boom JA, Myneni S, Bian J, Luo C, Chen Y, Tao C. Leveraging deep learning to understand health beliefs about the Human Papillomavirus Vaccine from social media. npj Digital Medicine 2019;2(1)
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  16. Zhu B, Zheng X, Liu H, Li J, Wang P. Analysis of spatiotemporal characteristics of big data on social media sentiment with COVID-19 epidemic topics. Chaos, Solitons & Fractals 2020;140:110123
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  17. Yang H, Gao H. Toward Sustainable Virtualized Healthcare: Extracting Medical Entities from Chinese Online Health Consultations Using Deep Neural Networks. Sustainability 2018;10(9):3292
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  18. Cai M, Shah N, Li J, Chen W, Cuomo RE, Obradovich N, Mackey TK, Lavorgna L. Identification and characterization of tweets related to the 2015 Indiana HIV outbreak: A retrospective infoveillance study. PLOS ONE 2020;15(8):e0235150
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  19. Hanna A, Hanna L. Topic Analysis of UK Fitness to Practise Cases: What Lessons Can Be Learnt?. Pharmacy 2019;7(3):130
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  20. Barros JM, Duggan J, Rebholz-Schuhmann D. The Application of Internet-Based Sources for Public Health Surveillance (Infoveillance): Systematic Review. Journal of Medical Internet Research 2020;22(3):e13680
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  21. Edo-Osagie O, De La Iglesia B, Lake I, Edeghere O. A scoping review of the use of Twitter for public health research. Computers in Biology and Medicine 2020;122:103770
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  22. Mutanga MB, Abayomi A. Tweeting on COVID-19 pandemic in South Africa: LDA-based topic modelling approach. African Journal of Science, Technology, Innovation and Development 2022;14(1):163
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  23. Gabarron E, Rivera-Romero O, Miron-Shatz T, Grainger R, Denecke K. Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review. Yearbook of Medical Informatics 2021;30(01):200
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  24. Huang N, Yan Z, Yin H. Effects of Online-Offline Service Integration on e-Healthcare Providers: A Quasi-Natural Experiment. SSRN Electronic Journal 2021;
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  25. Huang N, Yan Z, Yin H. Effects of Online–Offline Service Integration on e‐Healthcare Providers: A Quasi‐Natural Experiment. Production and Operations Management 2021;30(8):2359
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  26. Li L, Novillo-Ortiz D, Azzopardi-Muscat N, Kostkova P. Digital Data Sources and Their Impact on People's Health: A Systematic Review of Systematic Reviews. Frontiers in Public Health 2021;9
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  27. Palomares I, Martínez-Cámara E, Montes R, García-Moral P, Chiachio M, Chiachio J, Alonso S, Melero FJ, Molina D, Fernández B, Moral C, Marchena R, de Vargas JP, Herrera F. A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects. Applied Intelligence 2021;51(9):6497
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  28. Wu J, Sivaraman V, Kumar D, Banda JM, Sontag D. Pulse of the pandemic: Iterative topic filtering for clinical information extraction from social media. Journal of Biomedical Informatics 2021;120:103844
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  29. Wahid J, Shi L, Gao Y, Yang B, Tao Y, Wei L, Hussain S. Identifying and Characterizing the Propagation Scale of COVID-19 Situational Information on Twitter: A Hybrid Text Analytic Approach. Applied Sciences 2021;11(14):6526
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  30. Kostkova P, Saigí-Rubió F, Eguia H, Borbolla D, Verschuuren M, Hamilton C, Azzopardi-Muscat N, Novillo-Ortiz D. Data and Digital Solutions to Support Surveillance Strategies in the Context of the COVID-19 Pandemic. Frontiers in Digital Health 2021;3
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  31. Hagg LJ, Merkouris SS, O’Dea GA, Francis LM, Greenwood CJ, Fuller-Tyszkiewicz M, Westrupp EM, Macdonald JA, Youssef GJ. Examining Analytic Practices in Latent Dirichlet Allocation Within Psychological Science: Scoping Review. Journal of Medical Internet Research 2022;24(11):e33166
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  32. . Arabic Twitter Conversation Dataset about the COVID-19 Vaccine. Data 2022;7(11):152
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  33. Azizi F, Hajiabadi H, Vahdat-Nejad H, Khosravi MH. Detecting and analyzing topics of massive COVID-19 related tweets for various countries. Computers and Electrical Engineering 2023;106:108561
    CrossRef
  34. Pu X, Jiang Q, Fan B. Chinese public opinion on Japan's nuclear wastewater discharge: A case study of Weibo comments based on a thematic model. Ocean & Coastal Management 2022;225:106188
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  35. Pilipiec P, Samsten I, Bota A, Rocha LM. Surveillance of communicable diseases using social media: A systematic review. PLOS ONE 2023;18(2):e0282101
    CrossRef
  36. Noble PM, Appleton C, Radford AD, Nenadic G, Dórea FC. Using topic modelling for unsupervised annotation of electronic health records to identify an outbreak of disease in UK dogs. PLOS ONE 2021;16(12):e0260402
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  37. Alves VM, Korn D, Pervitsky V, Thieme A, Capuzzi SJ, Baker N, Chirkova R, Ekins S, Muratov EN, Hickey A, Tropsha A. Knowledge-based approaches to drug discovery for rare diseases. Drug Discovery Today 2022;27(2):490
    CrossRef
  38. Kostarella I, Kotsakis R. The Effects of the COVID-19 “Infodemic” on Journalistic Content and News Feed in Online and Offline Communication Spaces. Journalism and Media 2022;3(3):471
    CrossRef
  39. Sarker A, Gonzalez-Hernandez G. An unsupervised and customizable misspelling generator for mining noisy health-related text sources. Journal of Biomedical Informatics 2018;88:98
    CrossRef
  40. Fu J, Li C, Zhou C, Li W, Lai J, Deng S, Zhang Y, Guo Z, Wu Y. Methods for Analyzing the Contents of Social Media for Health Care: Scoping Review. Journal of Medical Internet Research 2023;25:e43349
    CrossRef
  41. Butt MJ, Malik AK, Qamar N, Yar S, Malik AJ, Rauf U. A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media. Sensors 2023;23(12):5543
    CrossRef
  42. Huang L, Eiden AL, He L, Annan A, Wang S, Wang J, Manion FJ, Wang X, Du J, Yao L. Vaccine sentiments and hesitancy on social media: a natural language processing-powered real-time monitoring system (Preprint). JMIR Medical Informatics 2024;
    CrossRef

According to Crossref, the following books are citing this article (DOI 10.2196/jmir.7393):

  1. Alves VM, Capuzzi SJ, Baker N, Muratov EN, Trospsha A, Hickey AJ. Approaching Complex Diseases. 2020. Chapter 4:77
    CrossRef
  2. Wang K, He C, Wang L, Wu J. Knowledge and Systems Sciences. 2018. Chapter 4:45
    CrossRef