Identifying Protective Health Behaviors on Twitter: Observational Study of Travel Advisories and Zika Virus

Journals

1. Safarnejad L, Xu Q, Ge Y, Bagavathi A, Krishnan S, Chen S. Identifying Influential Factors in the Discussion Dynamics of Emerging Health Issues on Social Media: Computational Study. JMIR Public Health and Surveillance 2020;6(3):e17175 View
2. Clavier T, Popoff B, Selim J, Beuzelin M, Roussel M, Compere V, Veber B, Besnier E. Association of Social Network Use With Increased Anxiety Related to the COVID-19 Pandemic in Anesthesiology, Intensive Care, and Emergency Medicine Teams: Cross-Sectional Web-Based Survey Study. JMIR mHealth and uHealth 2020;8(9):e23153 View
3. Medford R, Saleh S, Sumarsono A, Perl T, Lehmann C. An “Infodemic”: Leveraging High-Volume Twitter Data to Understand Early Public Sentiment for the Coronavirus Disease 2019 Outbreak. Open Forum Infectious Diseases 2020;7(7) View
4. Alvarez-Galvez J, Suarez-Lledo V, Rojas-Garcia A. Determinants of Infodemics During Disease Outbreaks: A Systematic Review. Frontiers in Public Health 2021;9 View
5. Yu S, Eisenman D, Han Z. Temporal Dynamics of Public Emotions During the COVID-19 Pandemic at the Epicenter of the Outbreak: Sentiment Analysis of Weibo Posts From Wuhan. Journal of Medical Internet Research 2021;23(3):e27078 View
6. Gerts D, Shelley C, Parikh N, Pitts T, Watson Ross C, Fairchild G, Vaquera Chavez N, Daughton A. “Thought I’d Share First” and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study. JMIR Public Health and Surveillance 2021;7(4):e26527 View
7. Chandrasekaran R, Mehta V, Valkunde T, Moustakas E. Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study. Journal of Medical Internet Research 2020;22(10):e22624 View
8. AGRAWAL A, GUPTA A. The Utility of Social Media during an Emerging Infectious Diseases Crisis: A Systematic Review of Literature. Journal of Microbiology and Infectious Diseases 2020:188 View
9. Daughton A, Shelley C, Barnard M, Gerts D, Watson Ross C, Crooker I, Nadiga G, Mukundan N, Vaquera Chavez N, Parikh N, Pitts T, Fairchild G. Mining and Validating Social Media Data for COVID-19–Related Human Behaviors Between January and July 2020: Infodemiology Study. Journal of Medical Internet Research 2021;23(5):e27059 View
10. Mohamed Ridhwan K, Hargreaves C. Leveraging Twitter data to understand public sentiment for the COVID‐19 outbreak in Singapore. International Journal of Information Management Data Insights 2021;1(2):100021 View
11. Malik A, Antonino A, Khan M, Nieminen M. Characterizing HIV discussions and engagement on Twitter. Health and Technology 2021;11(6):1237 View
12. Arias F, Zambrano Nunez M, Guerra-Adames A, Tejedor-Flores N, Vargas-Lombardo M. Sentiment Analysis of Public Social Media as a Tool for Health-Related Topics. IEEE Access 2022;10:74850 View
13. Golder S, Stevens R, O'Connor K, James R, Gonzalez-Hernandez G. Methods to Establish Race or Ethnicity of Twitter Users: Scoping Review. Journal of Medical Internet Research 2022;24(4):e35788 View
14. Al-Rakhami M, Al-Amri A. Lies Kill, Facts Save: Detecting COVID-19 Misinformation in Twitter. IEEE Access 2020;8:155961 View
15. Jing F, Li Z, Qiao S, Zhang J, Olatosi B, Li X. Using geospatial social media data for infectious disease studies: a systematic review. International Journal of Digital Earth 2023;16(1):130 View
16. Joshi A, Miller C. Review of machine learning techniques for mosquito control in urban environments. Ecological Informatics 2021;61:101241 View
17. Suri J, Agarwal S, Gupta S, Puvvula A, Viskovic K, Suri N, Alizad A, El-Baz A, Saba L, Fatemi M, Naidu D. Systematic Review of Artificial Intelligence in Acute Respiratory Distress Syndrome for COVID-19 Lung Patients: A Biomedical Imaging Perspective. IEEE Journal of Biomedical and Health Informatics 2021;25(11):4128 View
18. Xu W, Tshimula J, Dubé È, Graham J, Greyson D, MacDonald N, Meyer S. Unmasking the Twitter Discourses on Masks During the COVID-19 Pandemic: User Cluster–Based BERT Topic Modeling Approach. JMIR Infodemiology 2022;2(2):e41198 View
19. Bravo C, Castells V, Zietek-Gutsch S, Bodin P, Molony C, Frühwein M. Using social media listening and data mining to understand travellers’ perspectives on travel disease risks and vaccine-related attitudes and behaviours. Journal of Travel Medicine 2022;29(2) View
20. Mittal R, Mittal A, Aggarwal I. Identification of affective valence of Twitter generated sentiments during the COVID-19 outbreak. Social Network Analysis and Mining 2021;11(1) View
21. Alvarez-Mon M, Pereira-Sanchez V, Hooker E, Sanchez F, Alvarez-Mon M, Teo A. Content and User Engagement of Health-Related Behavior Tweets Posted by Mass Media Outlets From Spain and the United States Early in the COVID-19 Pandemic: Observational Infodemiology Study. JMIR Infodemiology 2023;3:e43685 View
22. Suri J, Puvvula A, Majhail M, Biswas M, Jamthikar A, Saba L, Faa G, Singh I, Oberleitner R, Turk M, Srivastava S, Chadha P, Suri H, Johri A, Nambi V, Sanches J, Khanna N, Viskovic K, Mavrogeni S, Laird J, Bit A, Pareek G, Miner M, Balestrieri A, Sfikakis P, Tsoulfas G, Protogerou A, Misra D, Agarwal V, Kitas G, Kolluri R, Teji J, Porcu M, Al-Maini M, Agbakoba A, Sockalingam M, Sexena A, Nicolaides A, Sharma A, Rathore V, Viswanathan V, Naidu S, Bhatt D. Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence. Reviews in Cardiovascular Medicine 2020;21(4) View

Books/Policy Documents

1. Ganasegeran K, Abdulrahman S. Human Behaviour Analysis Using Intelligent Systems. View
2. Kaur P, Kaur J, Singh P, Sharma S. Advances in Data and Information Sciences. View
3. Alowibdi J, Alshdadi A, Daud A, Dessouky M, Alhazmi E. Research Anthology on Implementing Sentiment Analysis Across Multiple Disciplines. View
4. Sedkaoui S, Khelfaoui M, Keltoum O. International Conference on Managing Business Through Web Analytics. View
5. Kumar Varshney P, Sharma N, Bharara V, Kumar S, Gupta A. Exploration of Artificial Intelligence and Blockchain Technology in Smart and Secure Healthcare. View