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;24(20):2855 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 2022;41(4):685 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 2022;71(3):140 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 2022;41(2):1065 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
  32. Teague S, Shatte A, Weller E, Fuller-Tyszkiewicz M, Hutchinson D. Methods and Applications of Social Media Monitoring of Mental Health During Disasters: Scoping Review. JMIR Mental Health 2022;9(2):e33058 View
  33. Skalicky S, Brugman B, Droog E, Burgers C. Satire from a far-away land: psychological distance and satirical news. Information, Communication & Society 2023;26(8):1548 View
  34. Blauza S, Heuckmann B, Kremer K, Büssing A. Psychological distance towards COVID-19: Geographical and hypothetical distance predict attitudes and mediate knowledge. Current Psychology 2023;42(10):8632 View
  35. Amusa L, Twinomurinzi H, Okonkwo C. Modeling COVID-19 incidence with Google Trends. Frontiers in Research Metrics and Analytics 2022;7 View
  36. Dangi D, Dixit D, Bhagat A. Sentiment analysis of COVID-19 social media data through machine learning. Multimedia Tools and Applications 2022;81(29):42261 View
  37. 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
  38. Raheja S, Asthana A. Sentiment Analysis of Tweets During the COVID-19 Pandemic Using Multinomial Logistic Regression. International Journal of Software Innovation 2022;11(1):1 View
  39. Craig C, Ma S, Karabas I, Feng S. Camping, weather, and disasters: Extending the Construal Level Theory. Journal of Hospitality and Tourism Management 2021;49:353 View
  40. Fazel S, Zhang L, Javid B, Brikell I, Chang Z. Harnessing Twitter data to survey public attention and attitudes towards COVID-19 vaccines in the UK. Scientific Reports 2021;11(1) View
  41. Duan R, Bombara C. Visualizing climate change: the role of construal level, emotional valence, and visual literacy. Climatic Change 2022;170(1-2) View
  42. Amusa L, Twinomurinzi H, Phalane E, Phaswana-Mafuya R. Big Data and Infectious Disease Epidemiology: Bibliometric Analysis and Research Agenda. Interactive Journal of Medical Research 2023;12:e42292 View
  43. Skinner-Dorkenoo A, Sarmal A, Rogbeer K, André C, Patel B, Cha L. Highlighting COVID-19 racial disparities can reduce support for safety precautions among White U.S. residents. Social Science & Medicine 2022;301:114951 View
  44. Liu Z, Jiang Z, Kip G, Snigdha K, Xu J, Wu X, Khan N, Schultz T. An infodemiological framework for tracking the spread of SARS-CoV-2 using integrated public data. Pattern Recognition Letters 2022;158:133 View
  45. Asshoff R, Heuckmann B, Ryl M, Reinhardt K, Braun D. “Bed bugs live in dirty places”—How Using Live Animals in Teaching Contributes to Reducing Stigma, Disgust, Psychological Stigma, and Misinformation in Students. CBE—Life Sciences Education 2022;21(4) View
  46. Zaman S, Yaqub U, Saleem T. Analysis of Bitcoin’s price spike in context of Elon Musk’s Twitter activity. Global Knowledge, Memory and Communication 2023;72(4/5):341 View
  47. Yeasmin N, Mahbub N, Baowaly M, Singh B, Alom Z, Aung Z, Azim M. Analysis and Prediction of User Sentiment on COVID-19 Pandemic Using Tweets. Big Data and Cognitive Computing 2022;6(2):65 View
  48. Kohn V, Frank M, Holten R. How Sociotechnical Realignment and Sentiments Concerning Remote Work are Related – Insights from the COVID-19 Pandemic. Business & Information Systems Engineering 2023;65(3):259 View
  49. Zhang W, Yi J. Mediated Fire and Distant Suffering: The Global Spectacle of Australian Bushfires in Nature 2.0. Environmental Communication 2023;17(4):386 View
  50. Porcu G, Chen Y, Bonaugurio A, Villa S, Riva L, Messina V, Bagarella G, Maistrello M, Leoni O, Cereda D, Matone F, Gori A, Corrao G. Web-based surveillance of respiratory infection outbreaks: retrospective analysis of Italian COVID-19 epidemic waves using Google Trends. Frontiers in Public Health 2023;11 View
  51. Xia X, Zhang Y, Jiang W, Wu C. Staying Home, Tweeting Hope: Mixed Methods Study of Twitter Sentiment Geographical Index During US Stay-At-Home Orders. Journal of Medical Internet Research 2023;25:e45757 View
  52. Vanderkooi D, Mashatan A, Turetken O. Introducing technological disruption: how breaking media attention on corporate events impacts online sentiment. Journal of Business Analytics 2024;7(2):63 View
  53. Atilla F, Zwaan R. Impact of spatial distance on public attention and sentiment during the spread of COVID-19. Informatics in Medicine Unlocked 2024;45:101463 View
  54. Saleem T, Yaqub U, Zaman S. Twitter sentiment analysis and bitcoin price forecasting: implications for financial risk management. The Journal of Risk Finance 2024;25(3):407 View
  55. Mishi S, Mushonga F, Anakpo G. The use of fear appeals for pandemic compliance: A systematic review of empirical measurement, fear appeal strategies and effectiveness. Heliyon 2024;10(9):e30383 View
  56. Gao Y, Sun Y. How Does Psychological Distance Influence Public Risky Behavior During Public Health Emergencies. Risk Management and Healthcare Policy 2024;Volume 17:1437 View
  57. Mayiwar L, Björklund F. Fear and anxiety differ in construal level and scope. Cognition and Emotion 2023;37(3):559 View

Books/Policy Documents

  1. Valdez R, Keim-Malpass J. Social Web and Health Research. View
  2. Büssing A, Heuckmann B. Science | Environment | Health. View
  3. Kumar Varshney P, Sharma N, Bharara V, Kumar S, Gupta A. Exploration of Artificial Intelligence and Blockchain Technology in Smart and Secure Healthcare. View