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Citing this Article

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Published on 09.08.17 in Vol 3, No 3 (2017): Jul-Sept

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

Works citing "Attitudes of Crohn’s Disease Patients: Infodemiology Case Study and Sentiment Analysis of Facebook and Twitter Posts"

Marco Roccetti, Gustavo Marfia, Paola Salomoni, Catia Prandi, Rocco Maurizio Zagari, Faustine Linda Gningaye Kengni, Franco Bazzoli, Marco Montagnani

JMIR Public Health Surveill 2017 (Aug 09); 3(3):e51 

According to Crossref, the following articles are citing this article (DOI 10.2196/publichealth.7004):

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

  1. Mavragani A, Ochoa G. Infoveillance of infectious diseases in USA: STDs, tuberculosis, and hepatitis. Journal of Big Data 2018;5(1)
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  2. Pilozzi A, Huang X. Overcoming Alzheimer’s Disease Stigma by Leveraging Artificial Intelligence and Blockchain Technologies. Brain Sciences 2020;10(3):183
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  3. Lombardo G, Fornacciari P, Mordonini M, Sani L, Tomaiuolo M. A combined approach for the analysis of support groups on Facebook - the case of patients of hidradenitis suppurativa. Multimedia Tools and Applications 2019;78(3):3321
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  4. Sharma C, Whittle S, Haghighi PD, Burstein F, Keen H. Sentiment analysis of social media posts on pharmacotherapy: A scoping review. Pharmacology Research & Perspectives 2020;8(5)
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  5. Mavragani A, Ochoa G. Google Trends in Infodemiology and Infoveillance: Methodology Framework. JMIR Public Health and Surveillance 2019;5(2):e13439
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  6. Salzmann-Erikson M, Eriksson H. A descriptive statistical analysis of volume, visibility and attitudes regarding nursing and care robots in social media. Contemporary Nurse 2018;54(1):88
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  7. Yin Z, Sulieman LM, Malin BA. A systematic literature review of machine learning in online personal health data. Journal of the American Medical Informatics Association 2019;26(6):561
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  8. 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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  9. . Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206
    CrossRef
  10. García-Díaz JA, Cánovas-García M, Valencia-García R. Ontology-driven aspect-based sentiment analysis classification: An infodemiological case study regarding infectious diseases in Latin America. Future Generation Computer Systems 2020;112:641
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  11. Mavragani A, Ochoa G, Tsagarakis KP. Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review. Journal of Medical Internet Research 2018;20(11):e270
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  12. Zunic A, Corcoran P, Spasic I. Sentiment Analysis in Health and Well-Being: Systematic Review. JMIR Medical Informatics 2020;8(1):e16023
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  13. Rovetta A, Bhagavathula AS. COVID-19-Related Web Search Behaviors and Infodemic Attitudes in Italy: Infodemiological Study. JMIR Public Health and Surveillance 2020;6(2):e19374
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  14. Gbashi S, Adebo OA, Doorsamy W, Njobeh PB. Systematic Delineation of Media Polarity on COVID-19 Vaccines in Africa: Computational Linguistic Modeling Study. JMIR Medical Informatics 2021;9(3):e22916
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  15. . The Resurgence of Cyber Racism During the COVID-19 Pandemic and its Aftereffects: Analysis of Sentiments and Emotions in Tweets. JMIR Public Health and Surveillance 2020;6(4):e19833
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  16. Momynaliev KT, Khoperskay LL, Pshenichnaya NY, Abuova GN, Akimkin VG. Infodemiological study of coronavirus epidemic using Google Trends in Central Asian Republics of Kazakhstan, Kyrgyzstan, Uzbekistan, Tajikistan. Medical alphabet 2021;(34):47
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  17. D’Souza RS, Hooten WM, Murad MH. A Proposed Approach for Conducting Studies That Use Data From Social Media Platforms. Mayo Clinic Proceedings 2021;96(8):2218
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  18. Cury GSA, Takamune DM, Herrerias GSP, Rivera-Sequeiros A, de Barros JR, Baima JP, Saad-Hossne R, Sassaki LY. Clinical and Psychological Factors Associated with Addiction and Compensatory Use of Facebook Among Patients with Inflammatory Bowel Disease: A Cross-Sectional Study. International Journal of General Medicine 2022;Volume 15:1447
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  19. Lee K, Song S. Developing insights from the collective voice of target users in Twitter. Journal of Big Data 2022;9(1)
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  20. Stemmer M, Parmet Y, Ravid G. Identifying Patients With Inflammatory Bowel Disease on Twitter and Learning From Their Personal Experience: Retrospective Cohort Study. Journal of Medical Internet Research 2022;24(8):e29186
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  21. Ji M, Xie W, Huang R, Qian X. Automatic Diagnosis of Mental Healthcare Information Actionability: Developing Binary Classifiers. International Journal of Environmental Research and Public Health 2021;18(20):10743
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  22. Galbraith E, Li J, Rio-Vilas VJD, Convertino M. In.To. COVID-19 socio-epidemiological co-causality. Scientific Reports 2022;12(1)
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  23. Chen D, Fulmer C, Gordon IO, Syed S, Stidham RW, Vande Casteele N, Qin Y, Falloon K, Cohen BL, Wyllie R, Rieder F. Application of Artificial Intelligence to Clinical Practice in Inflammatory Bowel Disease – What the Clinician Needs to Know. Journal of Crohn's and Colitis 2022;16(3):460
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  24. Lim LJ, Lim AJW, Fong KK, Lee CG. Sentiments Regarding COVID-19 Vaccination among Graduate Students in Singapore. Vaccines 2021;9(10):1141
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  25. Sharaf M, Hemdan EE, El-Sayed A, El-Bahnasawy NA. An efficient hybrid stock trend prediction system during COVID-19 pandemic based on stacked-LSTM and news sentiment analysis. Multimedia Tools and Applications 2023;82(16):23945
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  26. . SenDemonNet: sentiment analysis for demonetization tweets using heuristic deep neural network. Multimedia Tools and Applications 2022;81(8):11341
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  27. Zhang S, Chu-ke C, Kim H, Jing C, Pandian S. Public View of Public Health Emergencies Based on Artificial Intelligence Data. Journal of Environmental and Public Health 2022;2022:1
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  28. Kinariwala S, Deshmukh S. Short text topic modelling using local and global word-context semantic correlation. Multimedia Tools and Applications 2023;82(17):26411
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  29. Unnikrishnan R, S. SK, V.S. A. Efficient parameter tuning of neural foundation models for drug perspective prediction from unstructured socio-medical data. Engineering Applications of Artificial Intelligence 2023;123:106214
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  30. 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)
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  31. Belagur H, Reddy NS, Krishna PR, Tumuluri R. Cross-modal multi-headed attention for long multimodal conversations. Multimedia Tools and Applications 2023;82(29):45679
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  32. Escobar-Grisales D, Vásquez-Correa JC, Orozco-Arroyave JR. Evaluation of effectiveness in conversations between humans and chatbots using parallel convolutional neural networks with multiple temporal resolutions. Multimedia Tools and Applications 2024;83(2):5473
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  33. Comacchio C, Cesco M, Martinelli R, Garzitto M, Bianchi R, Innocente N, Sozio E, Tascini C, Balestrieri M, Colizzi M. Psychological factors associated with vaccination hesitancy: an observational study of patients hospitalized for COVID-19 in a later phase of the pandemic in Italy. Frontiers in Psychiatry 2023;14
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  34. Al-Qablan TA, Mohd Noor MH, Al-Betar MA, Khader AT. A survey on sentiment analysis and its applications. Neural Computing and Applications 2023;35(29):21567
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  35. Miao X, Zhang X, Zhang H. Low-rank tensor fusion and self-supervised multi-task multimodal sentiment analysis. Multimedia Tools and Applications 2024;
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  36. Wang Y, Wang L, Ma W, Zhao H, Han X, Zhao X. Development of a novel dynamic nosocomial infection risk management method for COVID-19 in outpatient settings. BMC Infectious Diseases 2024;24(1)
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According to Crossref, the following books are citing this article (DOI 10.2196/publichealth.7004):

  1. Apolinario-Arzube , Garcí­a-Dí­az JA, Pinto S, Luna-Aveiga H, Medina-Moreira JJ, Gómez-Berbis JM, Valencia-Garcia R, Estrade-Cabrera JI. Applied Informatics and Cybernetics in Intelligent Systems. 2020. Chapter 15:177
    CrossRef
  2. Stemmer M, Parmet Y, Ravid G. ICT for Health, Accessibility and Wellbeing. 2021. Chapter 18:206
    CrossRef