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Citing this Article

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Published on 19.06.17 in Vol 3, No 2 (2017): Apr-Jun

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

Works citing "What Are People Tweeting About Zika? An Exploratory Study Concerning Its Symptoms, Treatment, Transmission, and Prevention"

Michele Miller, Tanvi Banerjee, Roopteja Muppalla, William Romine, Amit Sheth

JMIR Public Health Surveill 2017 (Jun 19); 3(2):e38 

According to Crossref, the following articles are citing this article (DOI 10.2196/publichealth.7157):

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

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  7. Saura JR, Reyes-Menendez A, Thomas SB. Gaining a deeper understanding of nutrition using social networks and user-generated content. Internet Interventions 2020;20:100312
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  8. Mamidi R, Miller M, Banerjee T, Romine W, Sheth A. Identifying Key Topics Bearing Negative Sentiment on Twitter: Insights Concerning the 2015-2016 Zika Epidemic. JMIR Public Health and Surveillance 2019;5(2):e11036
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  14. Blbas HTA, Aziz KF, Nejad SH, Barzinjy AA. Phenomenon of depression and anxiety related to precautions for prevention among population during the outbreak of COVID-19 in Kurdistan Region of Iraq: based on questionnaire survey. Journal of Public Health 2022;30(3):567
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  22. Chen S, Xu Q, Buchenberger J, Bagavathi A, Fair G, Shaikh S, Krishnan S. Dynamics of Health Agency Response and Public Engagement in Public Health Emergency: A Case Study of CDC Tweeting Patterns During the 2016 Zika Epidemic. JMIR Public Health and Surveillance 2018;4(4):e10827
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  31. Saura JR, Palos-Sanchez P, Grilo A. Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining. Sustainability 2019;11(3):917
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  34. Wicke P, Bolognesi MM. Covid-19 Discourse on Twitter: How the Topics, Sentiments, Subjectivity, and Figurative Frames Changed Over Time. Frontiers in Communication 2021;6
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  35. Heyerdahl LW, Vray M, Leger V, Le Fouler L, Antouly J, Troit V, Giles-Vernick T. Evaluating the motivation of Red Cross Health volunteers in the COVID-19 pandemic: a mixed-methods study protocol. BMJ Open 2021;11(1):e042579
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  36. Park S, Han S, Kim J, Molaie MM, Vu HD, Singh K, Han J, Lee W, Cha M. COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication. Journal of Medical Internet Research 2021;23(3):e23272
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  37. Lyu JC, Luli GK. Understanding the Public Discussion About the Centers for Disease Control and Prevention During the COVID-19 Pandemic Using Twitter Data: Text Mining Analysis Study. Journal of Medical Internet Research 2021;23(2):e25108
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  38. Amara A, Hadj Taieb MA, Ben Aouicha M. Multilingual topic modeling for tracking COVID-19 trends based on Facebook data analysis. Applied Intelligence 2021;51(5):3052
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  39. Shah AM, Yan X, Qayyum A, Naqvi RA, Shah SJ. Mining topic and sentiment dynamics in physician rating websites during the early wave of the COVID-19 pandemic: Machine learning approach. International Journal of Medical Informatics 2021;149:104434
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  40. Jia S, Wu B, Zhou S. Topic modelling and opinion mining of user generated content on the internet using machine learning: An analysis of postpartum care centres in Shanghai. Journal of Intelligent & Fuzzy Systems 2021;41(3):4661
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  41. Alvarez-Galvez J, Suarez-Lledo V, Rojas-Garcia A. Determinants of Infodemics During Disease Outbreaks: A Systematic Review. Frontiers in Public Health 2021;9
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  42. 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
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  43. Ke Q, Du JT, Ji L. Toward a conceptual framework of health crisis information needs: an analysis of COVID-19 questions in a Chinese social Q&A website. Journal of Documentation 2021;77(4):851
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  44. Shahi GK, Dirkson A, Majchrzak TA. An exploratory study of COVID-19 misinformation on Twitter. Online Social Networks and Media 2021;22:100104
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  45. 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
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  47. . Analyzing Restaurant Customers’ Evolution of Dining Patterns and Satisfaction during COVID-19 for Sustainable Business Insights. Sustainability 2021;13(9):4981
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  48. Shah AM, Naqvi RA, Jeong O. Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets. International Journal of Environmental Research and Public Health 2021;18(9):4743
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  49. Miller M, Romine W, Oroszi T. Public Discussion of Anthrax on Twitter: Using Machine Learning to Identify Relevant Topics and Events. JMIR Public Health and Surveillance 2021;7(6):e27976
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  50. Fairie P, Zhang Z, D'Souza AG, Walsh T, Quan H, Santana MJ. Categorising patient concerns using natural language processing techniques. BMJ Health & Care Informatics 2021;28(1):e100274
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  51. Zahid S, Shams Malick RA, Sagri MR. Network Dynamics of COVID-19 Fake and True News Diffusion Networks. Journal of Information & Knowledge Management 2022;21(Supp01)
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  52. Bonifazi G, Corradini E, Ursino D, Virgili L. New Approaches to Extract Information From Posts on COVID-19 Published on Reddit. International Journal of Information Technology & Decision Making 2022;21(05):1385
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  53. Kandasamy G, Almaghaslah D, Almanasef M, Vasudevan R, Easwaran V. An evaluation of the psychological impact of COVID‐19 and the precautionary measure of social isolation on adults in the Asir region, Saudi Arabia. International Journal of Clinical Practice 2021;75(11)
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  54. Zuo C, Banerjee R, Chaleshtori FH, Shirazi H, Ray I. Seeing Should Probably Not Be Believing: The Role of Deceptive Support in COVID-19 Misinformation on Twitter. Journal of Data and Information Quality 2023;15(1):1
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  55. Chin H, Lima G, Shin M, Zhunis A, Cha C, Choi J, Cha M. User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis. Journal of Medical Internet Research 2023;25:e40922
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  56. Zhang G, Giachanou A, Rosso P. SceneFND: Multimodal fake news detection by modelling scene context information. Journal of Information Science 2024;50(2):355
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  57. Bravo C, Castells VB, 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)
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  58. Alshare KA, Moqbel M, Merhi MI. The double-edged sword of social media usage during the COVID-19 pandemic: demographical and cultural analyses. Journal of Enterprise Information Management 2023;36(1):197
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  59. Recuero-Virto N, Valilla-Arróspide C. Forecasting the next revolution: food technology’s impact on consumers' acceptance and satisfaction. British Food Journal 2022;124(12):4339
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  60. Nikookar SH, Maleki A, Fazeli-Dinan M, Shabani Kordshouli R, Enayati A. Entomological Surveillance of the Invasive Aedes Species at Higher-Priority Entry Points in Northern Iran: Exploratory Report on a Field Study. JMIR Public Health and Surveillance 2022;8(10):e38647
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  61. Chen S, Yin SJ, Guo Y, Ge Y, Janies D, Dulin M, Brown C, Robinson P, Zhang D. Content and sentiment surveillance (CSI): A critical component for modeling modern epidemics. Frontiers in Public Health 2023;11
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  62. Burwell E, Agarwal A, Romine WL. Understanding communication about the COVID-19 vaccines: analysis of emergent sentiments and topics of discussion on Twitter during the initial phase of the vaccine rollout. International Journal of Science Education, Part B 2024;14(1):18
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  63. Amores JJ, Blanco-Herrero D, Arcila-Calderón C. The Conversation around COVID-19 on Twitter—Sentiment Analysis and Topic Modelling to Analyse Tweets Published in English during the First Wave of the Pandemic. Journalism and Media 2023;4(2):467
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  64. Alnashwan R, O’Riordan A, Sorensen H. Multiple-Perspective Data-Driven Analysis of Online Health Communities. Healthcare 2023;11(20):2723
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  65. Sloesen B, O'Brien P, Verma H, Asaithambi S, Parashar N, Mothe RK, Shaikh J, Syntosi A. Patient Experiences and Insights on Chronic Ocular Pain: Social Media Listening Study. JMIR Formative Research 2024;8:e47245
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According to Crossref, the following books are citing this article (DOI 10.2196/publichealth.7157):

  1. Kho SJ, Padhee S, Bajaj G, Thirunarayan K, Sheth A. Emerging Research Challenges and Opportunities in Computational Social Network Analysis and Mining. 2019. Chapter 9:233
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  2. da Silva DA, Goncalves GS, dos Santos SC, Pugliese VU, Navas J, de Barros Santana RM, Queiroz FS, Dias LAV, da Cunha AM, Tasinaffo PM. Information Technology – New Generations. 2018. Chapter 34:233
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  3. Sheth A, Purohit H, Smith GA, Brunn J, Jadhav A, Kapanipathi P, Lu C, Wang W. Encyclopedia of Social Network Analysis and Mining. 2017. Chapter 345-1:1
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  4. Sheth A, Purohit H, Smith GA, Brunn J, Jadhav A, Kapanipathi P, Lu C, Wang W. Encyclopedia of Social Network Analysis and Mining. 2018. Chapter 345:3212
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  5. Gilbert J, Niu J, de Montigny S, Ng V, Rees E. AI for Disease Surveillance and Pandemic Intelligence. 2022. Chapter 9:101
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  6. . COVID-19 and a World of Ad Hoc Geographies. 2022. Chapter 38:683
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  7. Jabeen F, Khan FG, Shah S, Ahmad B, Jabeen S. Advances in Cybersecurity, Cybercrimes, and Smart Emerging Technologies. 2023. Chapter 23:289
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  8. Zuo C, Wang C, Banerjee R. Advanced Data Mining and Applications. 2023. Chapter 34:495
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