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

Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1641) Infodemiology and Infoveillance (1349) Infoveillance, Infodemiology, Digital Disease Surveillance, Infodemic Management (1041)

Published on in Vol 19, No 12 (2017): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/9266, first published .
Using Social Media Data to Understand the Impact of Promotional Information on Laypeople’s Discussions: A Case Study of Lynch Syndrome

Using Social Media Data to Understand the Impact of Promotional Information on Laypeople’s Discussions: A Case Study of Lynch Syndrome

Using Social Media Data to Understand the Impact of Promotional Information on Laypeople’s Discussions: A Case Study of Lynch Syndrome

Authors of this article:

Jiang Bian1 Author Orcid Image ;   Yunpeng Zhao1 Author Orcid Image ;   Ramzi G Salloum1 Author Orcid Image ;   Yi Guo1 Author Orcid Image ;   Mo Wang2 Author Orcid Image ;   Mattia Prosperi3, 4 Author Orcid Image ;   Hansi Zhang1 Author Orcid Image ;   Xinsong Du1 Author Orcid Image ;   Laura J Ramirez-Diaz1 Author Orcid Image ;   Zhe He5 Author Orcid Image ;   Yuan Sun6 Author Orcid Image
Article Authors Cited by (34) Tweetations (20) Metrics

Journals

  1. Aramburu M, Berlanga R, Lanza I. Social Media Multidimensional Analysis for Intelligent Health Surveillance. International Journal of Environmental Research and Public Health 2020;17(7):2289 View
  2. Allen C, Peterson S, Khoury M, Brody L, McBride C. A scoping review of social and behavioral science research to translate genomic discoveries into population health impact. Translational Behavioral Medicine 2021;11(4):901 View
  3. Ma R, Deng Z, Wu M. Effects of Health Information Dissemination on User Follows and Likes during COVID-19 Outbreak in China: Data and Content Analysis. International Journal of Environmental Research and Public Health 2020;17(14):5081 View
  4. Zunic A, Corcoran P, Spasic I. Sentiment Analysis in Health and Well-Being: Systematic Review. JMIR Medical Informatics 2020;8(1):e16023 View
  5. Huang M, Zolnoori M, Balls-Berry J, Brockman T, Patten C, Yao L. Technological Innovations in Disease Management: Text Mining US Patent Data From 1995 to 2017. Journal of Medical Internet Research 2019;21(4):e13316 View
  6. Zhang H, Wheldon C, Dunn A, Tao C, Huo J, Zhang R, Prosperi M, Guo Y, Bian J. Mining Twitter to assess the determinants of health behavior toward human papillomavirus vaccination in the United States. Journal of the American Medical Informatics Association 2020;27(2):225 View
  7. Pérez-Pérez M, Pérez-Rodríguez G, Fdez-Riverola F, Lourenço A. Using Twitter to Understand the Human Bowel Disease Community: Exploratory Analysis of Key Topics. Journal of Medical Internet Research 2019;21(8):e12610 View
  8. Lee E, Yee A. Toward Data Sense-Making in Digital Health Communication Research: Why Theory Matters in the Age of Big Data. Frontiers in Communication 2020;5 View
  9. Jayaraman P, Forkan A, Morshed A, Haghighi P, Kang Y. Healthcare 4.0: A review of frontiers in digital health. WIREs Data Mining and Knowledge Discovery 2020;10(2) View
  10. Zhao Y, Guo Y, He X, Wu Y, Yang X, Prosperi M, Jin Y, Bian J. Assessing mental health signals among sexual and gender minorities using Twitter data. Health Informatics Journal 2020;26(2):765 View
  11. Du J, Chen Q, Peng Y, Xiang Y, Tao C, Lu Z. ML-Net: multi-label classification of biomedical texts with deep neural networks. Journal of the American Medical Informatics Association 2019;26(11):1279 View
  12. Mavragani A. Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206 View
  13. Lara Ródenas M. El voto vigilado. Influencia y control electoral en las hermandades de Huelva durante el Antiguo Régimen. Hispania Sacra 2019;71(144):521 View
  14. Du J, Tang L, Xiang Y, Zhi D, Xu J, Song H, Tao C. Public Perception Analysis of Tweets During the 2015 Measles Outbreak: Comparative Study Using Convolutional Neural Network Models. Journal of Medical Internet Research 2018;20(7):e236 View
  15. Vernon E, Gottesman Z, Warren R. The value of health awareness days, weeks and months: A systematic review. Social Science & Medicine 2021;268:113553 View
  16. He L, Yin T, Hu Z, Chen Y, Hanauer D, Zheng K. Developing a standardized protocol for computational sentiment analysis research using health-related social media data. Journal of the American Medical Informatics Association 2021;28(6):1125 View
  17. Ibrahim M, Ghani Khan M, Mehmood F, Asim M, Mahmood W. GHS-NET a generic hybridized shallow neural network for multi-label biomedical text classification. Journal of Biomedical Informatics 2021;116:103699 View
  18. Pérez-Pérez M, Igrejas G, Fdez-Riverola F, Lourenço A. A framework to extract biomedical knowledge from gluten-related tweets: The case of dietary concerns in digital era. Artificial Intelligence in Medicine 2021;118:102131 View
  19. Zhao Y, He X, Feng Z, Bost S, Prosperi M, Wu Y, Guo Y, Bian J. Biases in using social media data for public health surveillance: A scoping review. International Journal of Medical Informatics 2022;164:104804 View
  20. He L, He C. Help Me #DebunkThis: Unpacking Individual and Community's Collaborative Work in Information Credibility Assessment. Proceedings of the ACM on Human-Computer Interaction 2022;6(CSCW2):1 View
  21. Septia Irawan A, Shahin B, Wangeshi Njuguna D, Nellamkuzhi N, Thiện B, Mahrouseh N, Varga O. Analysis of Content, Social Networks, and Sentiment of Front-of-Pack Nutrition Labeling in the European Union on Twitter. Frontiers in Nutrition 2022;9 View
  22. Déguilhem A, Malaab J, Talmatkadi M, Renner S, Foulquié P, Fagherazzi G, Loussikian P, Marty T, Mebarki A, Texier N, Schuck S. Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media. JMIR Infodemiology 2022;2(2):e39849 View
  23. Haggerty T, Sedney C, Cowher A, Holland D, Davisson L, Dekeseredy P. Twitter and Communicating Stigma about Medications to Treat Obesity. Health Communication 2023;38(14):3238 View
  24. Baroutsou V, Cerqueira Gonzalez Pena R, Schweighoffer R, Caiata-Zufferey M, Kim S, Hesse-Biber S, Ciorba F, Lauer G, Katapodi M. Predicting Openness of Communication in Families With Hereditary Breast and Ovarian Cancer Syndrome: Natural Language Processing Analysis. JMIR Formative Research 2023;7:e38399 View
  25. Dehghani Soufi M, Rezaei Hachesu P, Ferdousi R. Oncology Informatics for Lynch Syndrome Research and Care: A Literature Review. JCO Clinical Cancer Informatics 2022;(6) View
  26. Lossio-Ventura J, Weger R, Lee A, Guinee E, Chung J, Atlas L, Linos E, Pereira F. A Comparison of ChatGPT and Fine-Tuned Open Pre-Trained Transformers (OPT) Against Widely Used Sentiment Analysis Tools: Sentiment Analysis of COVID-19 Survey Data. JMIR Mental Health 2024;11:e50150 View
  27. Kusumaningrum R, Khoerunnisa S, Khadijah K, Syafrudin M. Exploring Community Awareness of Mangrove Ecosystem Preservation through Sentence-BERT and K-Means Clustering. Information 2024;15(3):165 View
  28. Mariscal-de-Gante Á. La frontera de Europa en África: reacciones sociales online ante la crisis migratoria de Ceuta en cinco idiomas europeos. Migraciones. Publicación del Instituto Universitario de Estudios sobre Migraciones 2024;(60):1 View
  29. Dekeseredy P, Sedney C, Razzaq B, Haggerty T, Brownstein H. Tweeting Stigma: An Exploration of Twitter Discourse Regarding Medications Used for Both Opioid Use Disorder and Chronic Pain. Journal of Drug Issues 2021;51(2):340 View

Books/Policy Documents

  1. Zhao Y, Prosperi M, Lyu T, Guo Y, Zhou L, Bian J. Trends in Artificial Intelligence Theory and Applications. Artificial Intelligence Practices. View
  2. M. Sergi C. Interactive Multimedia - Multimedia Production and Digital Storytelling. View
  3. Zhang H, Wheldon C, Tao C, Dunn A, Guo Y, Huo J, Bian J. Social Web and Health Research. View
  4. Rakesh B, Nayak S. Deep Learning in Personalized Healthcare and Decision Support. View