Published on in Vol 19, No 6 (2017): June

Too Far to Care? Measuring Public Attention and Fear for Ebola Using Twitter

Too Far to Care? Measuring Public Attention and Fear for Ebola Using Twitter

Too Far to Care? Measuring Public Attention and Fear for Ebola Using Twitter

Journals

  1. Mavragani A, Ochoa G, Tsagarakis K. Assessing the Methods, Tools, and Statistical Approaches in Google Trends Research: Systematic Review. Journal of Medical Internet Research 2018;20(11):e270 View
  2. Kapitány-Fövény M, Ferenci T, Sulyok Z, Kegele J, Richter H, Vályi-Nagy I, Sulyok M. Can Google Trends data improve forecasting of Lyme disease incidence?. Zoonoses and Public Health 2019;66(1):101 View
  3. Deng Q, Liu Y, Liu X, Zhang H, Deng X. Social Media Usage During Disasters: Exploring the Impact of Location and Distance on Online Engagement. Disaster Medicine and Public Health Preparedness 2020;14(2):183 View
  4. Karmegam D, Mappillairaju B. Spatio-temporal distribution of negative emotions on Twitter during floods in Chennai, India, in 2015: a post hoc analysis. International Journal of Health Geographics 2020;19(1) View
  5. Bempong N, Ruiz De Castañeda R, Schütte S, Bolon I, Keiser O, Escher G, Flahault A. Precision Global Health – The case of Ebola: a scoping review. Journal of Global Health 2019;9(1) View
  6. Deiner M, Fathy C, Kim J, Niemeyer K, Ramirez D, Ackley S, Liu F, Lietman T, Porco T. Facebook and Twitter vaccine sentiment in response to measles outbreaks. Health Informatics Journal 2019;25(3):1116 View
  7. Gianfredi V, Bragazzi N, Nucci D, Martini M, Rosselli R, Minelli L, Moretti M. Harnessing Big Data for Communicable Tropical and Sub-Tropical Disorders: Implications From a Systematic Review of the Literature. Frontiers in Public Health 2018;6 View
  8. Karmegam D, Ramamoorthy T, Mappillairajan B. A Systematic Review of Techniques Employed for Determining Mental Health Using Social Media in Psychological Surveillance During Disasters. Disaster Medicine and Public Health Preparedness 2020;14(2):265 View
  9. Mavragani A, Ochoa G. Infoveillance of infectious diseases in USA: STDs, tuberculosis, and hepatitis. Journal of Big Data 2018;5(1) View
  10. Vijaykumar S, Nowak G, Himelboim I, Jin Y. Virtual Zika transmission after the first U.S. case: who said what and how it spread on Twitter. American Journal of Infection Control 2018;46(5):549 View
  11. Mavragani A, Ochoa G. Google Trends in Infodemiology and Infoveillance: Methodology Framework. JMIR Public Health and Surveillance 2019;5(2):e13439 View
  12. Mavragani A. Tracking COVID-19 in Europe: Infodemiology Approach. JMIR Public Health and Surveillance 2020;6(2):e18941 View
  13. Rajan A, Sharaf R, Brown R, Sharaiha R, Lebwohl B, Mahadev S. Association of Search Query Interest in Gastrointestinal Symptoms With COVID-19 Diagnosis in the United States: Infodemiology Study. JMIR Public Health and Surveillance 2020;6(3):e19354 View
  14. Dubey A. Twitter Sentiment Analysis during COVID19 Outbreak. SSRN Electronic Journal 2020 View
  15. Sell T, Hosangadi D, Trotochaud M. Misinformation and the US Ebola communication crisis: analyzing the veracity and content of social media messages related to a fear-inducing infectious disease outbreak. BMC Public Health 2020;20(1) View
  16. 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
  17. Liu L, Xie J, Li K, Ji S. Exploring How Media Influence Preventive Behavior and Excessive Preventive Intention during the COVID-19 Pandemic in China. International Journal of Environmental Research and Public Health 2020;17(21):7990 View
  18. Mavragani A, Gkillas K. COVID-19 predictability in the United States using Google Trends time series. Scientific Reports 2020;10(1) View
  19. Liu Y, Long Y, Cheng Y, Guo Q, Yang L, Lin Y, Cao Y, Ye L, Jiang Y, Li K, Tian K, A X, Sun C, Zhang F, Song X, Liao G, Huang J, Du L. Psychological Impact of the COVID-19 Outbreak on Nurses in China: A Nationwide Survey During the Outbreak. Frontiers in Psychiatry 2020;11 View
  20. Iranmanesh A, Alpar Atun R. Reading the changing dynamic of urban social distances during the COVID-19 pandemic via Twitter. European Societies 2021;23(sup1):S872 View
  21. Cui H, Kertész J. Attention dynamics on the Chinese social media Sina Weibo during the COVID-19 pandemic. EPJ Data Science 2021;10(1) View
  22. Park Y. A socio-technological model of search information divide in US cities. Aslib Journal of Information Management 2020;73(2):144 View
  23. 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
  24. Craig C, Ma S, Karabas I. COVID-19, camping and construal level theory. Current Issues in Tourism 2021:1 View
  25. Shen L, Yao R, Zhang W, Evans R, Cao G, Zhang Z. Emotional Attitudes of Chinese Citizens on Social Distancing During the COVID-19 Outbreak: Analysis of Social Media Data. JMIR Medical Informatics 2021;9(3):e27079 View
  26. Wang T, Wang X, Jiang T, Wang S, Chen Z. Under the Threat of an Epidemic: People with Higher Subjective Socioeconomic Status Show More Unethical Behaviors. International Journal of Environmental Research and Public Health 2021;18(6):3170 View
  27. Wu L, Dodoo N, Wen T, Ke L. Understanding Twitter conversations about artificial intelligence in advertising based on natural language processing. International Journal of Advertising 2021:1 View
  28. Xie F, Sun X, Chen B, Chen Z, Shen S, Zhang M, Qin X, Liu Y, Shi P, Dai Q. Time map and predictors of on-spot emotional responses of Chinese people during COVID-19 outbreak: From January 27 to February 20, 2020. Journal of Affective Disorders Reports 2021;5:100165 View
  29. Ilyas H, Anwar A, Yaqub U, Alzamil Z, Appelbaum D. Analysis and visualization of COVID-19 discourse on Twitter using data science: a case study of the USA, the UK and India. Global Knowledge, Memory and Communication 2021;ahead-of-print(ahead-of-print) View
  30. Wu M, Long R, Chen H. Public psychological distance and spatial distribution characteristics during the COVID-19 pandemic: a Chinese context. Current Psychology 2021 View
  31. Southwick L, Guntuku S, Klinger E, Seltzer E, McCalpin H, Merchant R. Characterizing COVID-19 Content Posted to TikTok: Public Sentiment and Response During the First Phase of the COVID-19 Pandemic. Journal of Adolescent Health 2021;69(2):234 View

Books/Policy Documents

  1. Valdez R, Keim-Malpass J. Social Web and Health Research. View