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Published on 26.05.15 in Vol 17, No 5 (2015): May

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

Works citing "Disease Detection or Public Opinion Reflection? Content Analysis of Tweets, Other Social Media, and Online Newspapers During the Measles Outbreak in the Netherlands in 2013"

Liesbeth Mollema, Irene Anhai Harmsen, Emma Broekhuizen, Rutger Clijnk, Hester De Melker, Theo Paulussen, Gerjo Kok, Robert Ruiter, Enny Das

J Med Internet Res 2015 (May 26); 17(5):e128 

According to Crossref, the following articles are citing this article (DOI 10.2196/jmir.3863):

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

  1. Deiner MS, Fathy C, Kim J, Niemeyer K, Ramirez D, Ackley SF, Liu F, Lietman TM, Porco TC. Facebook and Twitter vaccine sentiment in response to measles outbreaks. Health Informatics Journal 2019;25(3):1116
    CrossRef
  2. Saleh SN, Lehmann CU, McDonald SA, Basit MA, Medford RJ. Understanding public perception of coronavirus disease 2019 (COVID-19) social distancing on Twitter. Infection Control & Hospital Epidemiology 2021;42(2):131
    CrossRef
  3. Tu Y, Peng B, Wei G, Wu W. EPR system participants’ behavior: Evolutionary game and strategy simulation. Journal of Cleaner Production 2020;271:122659
    CrossRef
  4. Becker BF, Larson HJ, Bonhoeffer J, van Mulligen EM, Kors JA, Sturkenboom MC. Evaluation of a multinational, multilingual vaccine debate on Twitter. Vaccine 2016;34(50):6166
    CrossRef
  5. . Infodemiology and Infoveillance: Scoping Review. Journal of Medical Internet Research 2020;22(4):e16206
    CrossRef
  6. Yoo W, Choi D, Park K. The effects of SNS communication: How expressing and receiving information predict MERS-preventive behavioral intentions in South Korea. Computers in Human Behavior 2016;62:34
    CrossRef
  7. Albalawi Y, Sixsmith J. Agenda Setting for Health Promotion: Exploring an Adapted Model for the Social Media Era. JMIR Public Health and Surveillance 2015;1(2):e21
    CrossRef
  8. Kieh MD, Cho EM, Myles IA, Kuhn JH. Contrasting academic and lay press print coverage of the 2013-2016 Ebola Virus Disease outbreak. PLOS ONE 2017;12(6):e0179356
    CrossRef
  9. Margagliotti G, Bollé T, Rossy Q. Worldwide analysis of crimes by the traces of their online media coverage: The case of jewellery store robberies. Digital Investigation 2019;31:200889
    CrossRef
  10. Thomas TL, Friedman DB, Brandt HM, Spencer SM, Tanner A. Uncharted Waters: Communicating Health Risks During the 2014 West Virginia Water Crisis. Journal of Health Communication 2016;21(9):1062
    CrossRef
  11. Smith RJ, Crutchley P, Schwartz HA, Ungar L, Shofer F, Padrez KA, Merchant RM. Variations in Facebook Posting Patterns Across Validated Patient Health Conditions: A Prospective Cohort Study. Journal of Medical Internet Research 2017;19(1):e7
    CrossRef
  12. Tang L, Bie B, Park S, Zhi D. Social media and outbreaks of emerging infectious diseases: A systematic review of literature. American Journal of Infection Control 2018;46(9):962
    CrossRef
  13. Reintjes R, Das E, Klemm C, Richardus JH, Keßler V, Ahmad A, van Boven M. “Pandemic Public Health Paradox”: Time Series Analysis of the 2009/10 Influenza A / H1N1 Epidemiology, Media Attention, Risk Perception and Public Reactions in 5 European Countries. PLOS ONE 2016;11(3):e0151258
    CrossRef
  14. 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
    CrossRef
  15. Khan Y, Leung GJ, Belanger P, Gournis E, Buckeridge DL, Liu L, Li Y, Johnson IL. Comparing Twitter data to routine data sources in public health surveillance for the 2015 Pan/Parapan American Games: an ecological study. Canadian Journal of Public Health 2018;109(3):419
    CrossRef
  16. Nic Lochlainn LM, Woudenberg T, van Lier A, Zonnenberg I, Philippi M, de Melker HE, Hahné SJ. A novel measles outbreak control strategy in the Netherlands in 2013–2014 using a national electronic immunization register: A study of early MMR uptake and its determinants. Vaccine 2017;35(43):5828
    CrossRef
  17. Kim Y, Huang J, Emery S. Garbage in, Garbage Out: Data Collection, Quality Assessment and Reporting Standards for Social Media Data Use in Health Research, Infodemiology and Digital Disease Detection. Journal of Medical Internet Research 2016;18(2):e41
    CrossRef
  18. Tangherlini TR, Roychowdhury V, Glenn B, Crespi CM, Bandari R, Wadia A, Falahi M, Ebrahimzadeh E, Bastani R. “Mommy Blogs” and the Vaccination Exemption Narrative: Results From A Machine-Learning Approach for Story Aggregation on Parenting Social Media Sites. JMIR Public Health and Surveillance 2016;2(2):e166
    CrossRef
  19. Tamul DJ, Martínez-Carrillo NI. Ample Sample? An Examination of the Representativeness of Themes Between Sampling Durations Generated From Keyword Searches for 12 Months of Immigration News From LexisNexis and Newspaper Websites. Journalism & Mass Communication Quarterly 2018;95(1):96
    CrossRef
  20. Klinkenberg D, Hahné SJM, Woudenberg T, Wallinga J. The Reduction of Measles Transmission During School Vacations. Epidemiology 2018;29(4):562
    CrossRef
  21. Radzikowski J, Stefanidis A, Jacobsen KH, Croitoru A, Crooks A, Delamater PL. The Measles Vaccination Narrative in Twitter: A Quantitative Analysis. JMIR Public Health and Surveillance 2016;2(1):e1
    CrossRef
  22. Schwab-Reese LM, Hovdestad W, Tonmyr L, Fluke J. The potential use of social media and other internet-related data and communications for child maltreatment surveillance and epidemiological research: Scoping review and recommendations. Child Abuse & Neglect 2018;85:187
    CrossRef
  23. Gastañaduy PA, Banerjee E, DeBolt C, Bravo-Alcántara P, Samad SA, Pastor D, Rota PA, Patel M, Crowcroft NS, Durrheim DN. Public health responses during measles outbreaks in elimination settings: Strategies and challenges. Human Vaccines & Immunotherapeutics 2018;14(9):2222
    CrossRef
  24. Fadda M, Allam A, Schulz PJ. Arguments and sources on Italian online forums on childhood vaccinations: Results of a content analysis. Vaccine 2015;33(51):7152
    CrossRef
  25. Kim I, Feng C, Wang Y, Spitzberg BH, Tsou M. Exploratory Spatiotemporal Analysis in Risk Communication during the MERS Outbreak in South Korea. The Professional Geographer 2017;69(4):629
    CrossRef
  26. Yan S, Chughtai A, Macintyre C. Utility and potential of rapid epidemic intelligence from internet-based sources. International Journal of Infectious Diseases 2017;63:77
    CrossRef
  27. SHIMASAKI N, OKAUE A, KIKUNO R, SHINOHARA K. Comparison of the Filter Efficiency of Medical Nonwoven Fabrics against Three Different Microbe Aerosols. Biocontrol Science 2018;23(2):61
    CrossRef
  28. Hansen ND, Mølbak K, Cox IJ, Lioma C. Relationship Between Media Coverage and Measles-Mumps-Rubella (MMR) Vaccination Uptake in Denmark: Retrospective Study. JMIR Public Health and Surveillance 2019;5(1):e9544
    CrossRef
  29. Quade P, Nsoesie EO. A Platform for Crowdsourced Foodborne Illness Surveillance: Description of Users and Reports. JMIR Public Health and Surveillance 2017;3(3):e42
    CrossRef
  30. Sarker A, Belousov M, Friedrichs J, Hakala K, Kiritchenko S, Mehryary F, Han S, Tran T, Rios A, Kavuluru R, de Bruijn B, Ginter F, Mahata D, Mohammad SM, Nenadic G, Gonzalez-Hernandez G. Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task. Journal of the American Medical Informatics Association 2018;25(10):1274
    CrossRef
  31. Gozzi N, Tizzani M, Starnini M, Ciulla F, Paolotti D, Panisson A, Perra N. Collective Response to Media Coverage of the COVID-19 Pandemic on Reddit and Wikipedia: Mixed-Methods Analysis. Journal of Medical Internet Research 2020;22(10):e21597
    CrossRef
  32. Wang T, Lu K, Chow KP, Zhu Q. COVID-19 Sensing: Negative Sentiment Analysis on Social Media in China via BERT Model. IEEE Access 2020;8:138162
    CrossRef
  33. Gupta A, Katarya R. Social media based surveillance systems for healthcare using machine learning: A systematic review. Journal of Biomedical Informatics 2020;108:103500
    CrossRef
  34. 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
    CrossRef
  35. Pruss D, Fujinuma Y, Daughton AR, Paul MJ, Arnot B, Albers Szafir D, Boyd-Graber J, Xia F. Zika discourse in the Americas: A multilingual topic analysis of Twitter. PLOS ONE 2019;14(5):e0216922
    CrossRef
  36. Fagerlin A, Valley TS, Scherer AM, Knaus M, Das E, Zikmund-Fisher BJ. Communicating infectious disease prevalence through graphics: Results from an international survey. Vaccine 2017;35(32):4041
    CrossRef
  37. Peng W, Occa A, McFarlane SJ, Morgan SE. A Content Analysis of the Discussions about Clinical Trials on A Cancer-dedicated Online Forum. Journal of Health Communication 2019;24(12):912
    CrossRef
  38. Xie T, Tan T, Li J.

    An Extensive Search Trends-Based Analysis of Public Attention on Social Media in the Early Outbreak of COVID-19 in China

    . Risk Management and Healthcare Policy 2020;Volume 13:1353
    CrossRef
  39. Liu K, Chen L. Medical Social Media Text Classification Integrating Consumer Health Terminology. IEEE Access 2019;7:78185
    CrossRef
  40. Suppli CH, Hansen ND, Rasmussen M, Valentiner-Branth P, Krause TG, Mølbak K. Decline in HPV-vaccination uptake in Denmark – the association between HPV-related media coverage and HPV-vaccination. BMC Public Health 2018;18(1)
    CrossRef
  41. van Lent LG, Sungur H, Kunneman FA, van de Velde B, Das E. Too Far to Care? Measuring Public Attention and Fear for Ebola Using Twitter. Journal of Medical Internet Research 2017;19(6):e193
    CrossRef
  42. Matza LS, Paulus TM, Garris CP, Van de Velde N, Chounta V, Deger KA. Qualitative Thematic Analysis of Social Media Data to Assess Perceptions of Route of Administration for Antiretroviral Treatment among People Living with HIV. The Patient - Patient-Centered Outcomes Research 2020;13(4):409
    CrossRef
  43. Qiu J, Xu L, Wang J, Gu W. Mutual influences between message volume and emotion intensity on emerging infectious diseases: An investigation with microblog data. Information & Management 2020;57(4):103217
    CrossRef
  44. Paul MJ, Dredze M. Social Monitoring for Public Health. Synthesis Lectures on Information Concepts, Retrieval, and Services 2017;9(5):1
    CrossRef
  45. Kim SC, Hawkins KH. The psychology of social media communication in influencing prevention intentions during the 2019 U.S. measles outbreak. Computers in Human Behavior 2020;111:106428
    CrossRef
  46. Tang L, Bie B, Zhi D. Tweeting about measles during stages of an outbreak: A semantic network approach to the framing of an emerging infectious disease. American Journal of Infection Control 2018;46(12):1375
    CrossRef
  47. Kunneman F, Lambooij M, Wong A, Bosch AVD, Mollema L. Monitoring stance towards vaccination in twitter messages. BMC Medical Informatics and Decision Making 2020;20(1)
    CrossRef
  48. Bonnevie E, Goldbarg J, Gallegos-Jeffrey AK, Rosenberg SD, Wartella E, Smyser J. Content Themes and Influential Voices Within Vaccine Opposition on Twitter, 2019. American Journal of Public Health 2020;110(S3):S326
    CrossRef
  49. Wicke P, Bolognesi MM, Athanasopoulos P. Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter. PLOS ONE 2020;15(9):e0240010
    CrossRef
  50. Postma LGM, Donyai P. The cooccurrence of heightened media attention and adverse drug reaction reports for hormonal contraception in the United Kingdom between 2014 and 2017. British Journal of Clinical Pharmacology 2021;87(4):1768
    CrossRef
  51. Gencoglu O, Gruber M. Causal Modeling of Twitter Activity during COVID-19. Computation 2020;8(4):85
    CrossRef
  52. . Large-Scale, Language-Agnostic Discourse Classification of Tweets During COVID-19. Machine Learning and Knowledge Extraction 2020;2(4):603
    CrossRef
  53. Shi W, Liu D, Yang J, Zhang J, Wen S, Su J. Social Bots’ Sentiment Engagement in Health Emergencies: A Topic-Based Analysis of the COVID-19 Pandemic Discussions on Twitter. International Journal of Environmental Research and Public Health 2020;17(22):8701
    CrossRef
  54. Al-Khalifa KS, AlSheikh R, Alsahafi YA, Alkhalifa A, Sadaf S, Muazen YY, Al-Moumen SA, Yermal AS. Dental care during the COVID-19 Pandemic: An Arabic tweets analysis (Preprint). JMIR Public Health and Surveillance 2020;
    CrossRef
  55. 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
    CrossRef
  56. Karafillakis E, Martin S, Simas C, Olsson K, Takacs J, Dada S, Larson HJ. Methods for Social Media Monitoring Related to Vaccination: Systematic Scoping Review. JMIR Public Health and Surveillance 2021;7(2):e17149
    CrossRef
  57. Bonnevie E, Gallegos-Jeffrey A, Goldbarg J, Byrd B, Smyser J. Quantifying the rise of vaccine opposition on Twitter during the COVID-19 pandemic. Journal of Communication in Healthcare 2021;14(1):12
    CrossRef
  58. Alvarez-Galvez J, Suarez-Lledo V, Rojas-Garcia A. Determinants of Infodemics During Disease Outbreaks: A Systematic Review. Frontiers in Public Health 2021;9
    CrossRef
  59. 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
    CrossRef
  60. Nguyen TH, Le XC. How social media fosters the elders' COVID-19 preventive behaviors: perspectives of information value and perceived threat. Library Hi Tech 2021;39(3):776
    CrossRef
  61. Xie R, Chu SKW, Chiu DKW, Wang Y. Exploring Public Response to COVID-19 on Weibo with LDA Topic Modeling and Sentiment Analysis. Data and Information Management 2021;5(1):86
    CrossRef
  62. Gabarron E, Rivera-Romero O, Miron-Shatz T, Grainger R, Denecke K. Role of Participatory Health Informatics in Detecting and Managing Pandemics: Literature Review. Yearbook of Medical Informatics 2021;30(01):200
    CrossRef
  63. Bonnevie E, Sittig J, Smyser J. The case for tracking misinformation the way we track disease. Big Data & Society 2021;8(1):205395172110138
    CrossRef
  64. Bonnevie E, Goldbarg J, Gallegos-Jeffry AK, Rosenberg SD, Wartella E, Smyser J. Temas de contenido y voces influyentes dentro de la oposición a las vacunas en Twitter, 2019. Revista Panamericana de Salud Pública 2021;45:1
    CrossRef
  65. Al-Khalifa KS, AlSheikh R, Alsahafi YA, Alkhalifa A, Sadaf S, Al-Moumen SA, Muazen YY, Shetty AC. Dental Care in the Arab Countries During the COVID-19 Pandemic: An Infodemiological Study. Risk Management and Healthcare Policy 2021;Volume 14:2153
    CrossRef
  66. Deng W, Yang Y. Cross-Platform Comparative Study of Public Concern on Social Media during the COVID-19 Pandemic: An Empirical Study Based on Twitter and Weibo. International Journal of Environmental Research and Public Health 2021;18(12):6487
    CrossRef
  67. Al-Rawi A, Grepin K, Li X, Morgan R, Wenham C, Smith J. Investigating Public Discourses Around Gender and COVID-19: a Social Media Analysis of Twitter Data. Journal of Healthcare Informatics Research 2021;5(3):249
    CrossRef
  68. . Do public comments make a difference in open rulemaking? Insights from information management using machine learning and QCA analysis. Government Information Quarterly 2023;40(1):101778
    CrossRef
  69. . COVID-19 dialogue on Facebook: Crisis Communication relationship between Ghanaian Authorities and Citizens. Journal of International Crisis and Risk Communication Research 2022;5(1):57
    CrossRef
  70. Allen C, Andersen B, Khoury M, Roberts M. Current Social Media Conversations about Genetics and Genomics in Health: A Twitter-Based Analysis. Public Health Genomics 2018;21(1-2):93
    CrossRef
  71. Izhar TAT, Torabi T. Online searching trend on Covid-19 using Google trend: infodemiological study in Malaysia. International Journal of Information Technology 2022;14(2):675
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  72. Roe C, Lowe M, Williams B, Miller C. Public Perception of SARS-CoV-2 Vaccinations on Social Media: Questionnaire and Sentiment Analysis. International Journal of Environmental Research and Public Health 2021;18(24):13028
    CrossRef
  73. Stege H, Schneider S, Forschner A, Eigentler T, Nashan D, Huening S, Meiss F, Lehr S, Kaatz M, Kuchen R, Kaehler KC, Haist M, Huebner J, Loquai C. eHealth Literacy in German Skin Cancer Patients. International Journal of Environmental Research and Public Health 2022;19(14):8365
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  75. Zhang M, Chen Z, Qi X, Liu J. Could Social Bots’ Sentiment Engagement Shape Humans’ Sentiment on COVID-19 Vaccine Discussion on Twitter?. Sustainability 2022;14(9):5566
    CrossRef
  76. . Population Control, Deadly Vaccines, and Mutant Mosquitoes: The Construction and Circulation of Zika Virus Conspiracy Theories Online. Canadian Journal of Communication 2019;44(2):211
    CrossRef
  77. Dong X, Lian Y. A review of social media-based public opinion analyses: Challenges and recommendations. Technology in Society 2021;67:101724
    CrossRef
  78. Wang H, Shi J, Sharma K. Intermedia Agenda Setting amid the Pandemic: A Computational Analysis of China’s Online News. Computational Intelligence and Neuroscience 2022;2022:1
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  80. Shao C, Guan X, Sun J, Cole M, Liu G. Social media interactions between government and the public: A Chinese case study of government WeChat official accounts on information related to COVID-19. Frontiers in Psychology 2022;13
    CrossRef
  81. Momynaliev K, Khoroshun D, Akimkin V. Online queries as a criterion for evaluating epidemiological status and effectiveness of COVID-19 control measures in Russia: results from Yandex.Wordstat analysis. BMJ Open 2022;12(7):e056716
    CrossRef
  82. Khoroshun DK, Momynaliev K, Voronin EM, Akimkin VG. Analysis of Yandex search queries related to COVID‑19 in Russian Federation. Medical alphabet 2022;(14):14
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  83. Gour A, Aggarwal S, Kumar S. Lending ears to unheard voices: An empirical analysis of user‐generated content on social media. Production and Operations Management 2022;31(6):2457
    CrossRef
  84. . The value of public service broadcasting in Japan during COVID-19 pandemic: An analysis of WTP by Blinder-Oaxaca decomposition. Telecommunications Policy 2023;47(3):102523
    CrossRef
  85. Arendt F, Scherr S. News-stimulated public-attention dynamics and vaccination coverage during a measles outbreak: An observational study. Social Science & Medicine 2020;265:113495
    CrossRef
  86. Friemel TN, Geber S. Social Distancing during the COVID-19 Pandemic in Switzerland: Health Protective Behavior in the Context of Communication and Perceptions of Efficacy, Norms, and Threat. Health Communication 2023;38(4):779
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  87. . A Trend Analysis of Emotions on the Issue of Unidentified Land Owners in Social Media:Case Study of Land Acquisition of Water Resource Area by Foreign Investors. Studies in Regional Science 2021;51(1):115
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  88. Khademi Habibabadi S, Delir Haghighi P, Burstein F, Buttery J. Vaccine Adverse Event Mining of Twitter Conversations: 2-Phase Classification Study. JMIR Medical Informatics 2022;10(6):e34305
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  90. Pilipiec P, Samsten I, Bota A, Rocha LM. Surveillance of communicable diseases using social media: A systematic review. PLOS ONE 2023;18(2):e0282101
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  91. 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
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  92. Shi C, So MC, Stelmach S, Earn A, Earn DJD, Dushoff J. From science to politics: COVID-19 information fatigue on YouTube. BMC Public Health 2022;22(1)
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  93. Sano Y, Hori A, Kolahi J. 12-year observation of tweets about rubella in Japan: A retrospective infodemiology study. PLOS ONE 2023;18(5):e0285101
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  94. Mitchell SS, Beanlands J. “The mask is not for you” : A framing analysis of pro- and anti-mask sentiment on Twitter. Health & New Media Research 2022;6(1):3
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  95. Grage L, Cuellar MJ. Did text-based news-media coverage about the COVID-19 pandemic increase vaccine uptake? A population-based study in Alaska. International Journal of Circumpolar Health 2023;82(1)
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  96. Qiu D, Huang L. Comparative analysis of epidemic public opinion and policies in two regions of China based on big data. Intelligent Data Analysis 2024;28(2):533
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  97. Diaz MI, Medford RJ, Lehmann CU, Petersen C. The lived experience of people with disabilities during the COVID-19 pandemic on Twitter: Content analysis. DIGITAL HEALTH 2023;9
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  98. Su M, Cheng D, Xu Y, Weng F. An improved BERT method for the evolution of network public opinion of major infectious diseases: Case Study of COVID-19. Expert Systems with Applications 2023;233:120938
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  101. Ribeiro F, Silva S, Perona JJ. Estudar o desconhecido, publicar o possível? A resposta à pandemia da covid-19 nas principais revistas Scopus em Ciências da Comunicação. Palabra Clave 2024;26(4):1
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According to Crossref, the following books are citing this article (DOI 10.2196/jmir.3863):

  1. Shin EK, Shaban-Nejad A. Public Health Intelligence and the Internet. 2017. Chapter 1:1
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  2. Rustagi S, Patel D. Transactions on Large-Scale Data- and Knowledge-Centered Systems XLIII. 2020. Chapter 5:114
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  4. Novak J, Maljur T, Drenska K. HCI International 2022 – Late Breaking Papers: Interacting with eXtended Reality and Artificial Intelligence. 2022. Chapter 31:441
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