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

Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1015) Infodemiology and Infoveillance (1278)

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

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

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

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

Authors of this article:

Marco Roccetti1 Author Orcid Image ;   Gustavo Marfia2 Author Orcid Image ;   Paola Salomoni1 Author Orcid Image ;   Catia Prandi3 Author Orcid Image ;   Rocco Maurizio Zagari4 Author Orcid Image ;   Faustine Linda Gningaye Kengni4 Author Orcid Image ;   Franco Bazzoli4 Author Orcid Image ;   Marco Montagnani4 Author Orcid Image
Article Authors Cited by (45) Tweetations (12) Metrics

Journals

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  18. Cury G, Takamune D, Herrerias G, Rivera-Sequeiros A, de Barros J, Baima J, Saad-Hossne R, Sassaki L. 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 View
  19. Lee K, Song S. Developing insights from the collective voice of target users in Twitter. Journal of Big Data 2022;9(1) View
  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 View
  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 View
  22. Galbraith E, Li J, Rio-Vilas V, Convertino M. In.To. COVID-19 socio-epidemiological co-causality. Scientific Reports 2022;12(1) View
  23. Chen D, Fulmer C, Gordon I, Syed S, Stidham R, Vande Casteele N, Qin Y, Falloon K, Cohen B, 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 View
  24. Lim L, Lim A, Fong K, Lee C. Sentiments Regarding COVID-19 Vaccination among Graduate Students in Singapore. Vaccines 2021;9(10):1141 View
  25. Sharaf M, Hemdan E, El-Sayed A, El-Bahnasawy N. 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 View
  26. Kayıkçı Ş. SenDemonNet: sentiment analysis for demonetization tweets using heuristic deep neural network. Multimedia Tools and Applications 2022;81(8):11341 View
  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 View
  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 View
  29. Unnikrishnan R, S. S, 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 View
  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) View
  31. Belagur H, Reddy N, Krishna P, Tumuluri R. Cross-modal multi-headed attention for long multimodal conversations. Multimedia Tools and Applications 2023;82(29):45679 View
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Books/Policy Documents

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