A Literature Survey on Demographics and Topic Impacts on the co-spread of Healthcare prediction based on Twitter(X) Data
Downloads
Big data is being used much more frequently to monitor trends in business, economics, society, and health as a result of more individuals utilizing Twitter and other social media. Businesses utilize sophisticated statistical techniques to forecast and monitor events such as disease outbreaks and trends in mental health by combining a wide range of data sets from social media. This kind of data utilization has benefits, but there are drawbacks as well, such as the rapidity with which false information can proliferate on Twitter. The approach that is being suggested emphasizes the necessity of improving fact-checking procedures, especially in the healthcare industry where false information can have detrimental effects. This underscores the growing importance of reliable data, information, and knowledge flows in shaping economies while acknowledging the imperative to address challenges associated with the information revolution.
S.S.-A.Enrique Cano-Marin, Marc¸al Mora-Cantallops, “The power of big data analytics over fake news: A scientometric review of twitter as a predictive system in healthcare,” Technological Forecasting and Social Change, vol. 190, 2023.
R.K.Aakansha Gupta, “Social media based surveillance systems for healthcare using machine learning: A systematic review,” in Journal of Biomedical Informatics, 2020.
H. A. Gre´goire Burel, Tracie Farrell, “Demographics and topics impact on the co-spread of covid-19 misinformation and fact-checks on twitter,” Information Processing Management, vol. 58, 2021.
T. D. Jitendra Vikram Tembhurne, Md. Moin Almin, “Mc-dnn: Fake news detection using multi-channel deep neural networks,” in Interna- tional Journal on Semantic Web Information Systems, vol. 18, 2022.
M. G. Q. Wilson Ceron a, Mathias-Felipe de-Lima-Santos, “Fake news agenda in the era of covid-19: Identifying trends through fact-checking content,” in Online Social Networks and Media, vol. 21, pp. 1080–1083, 2021.
I. C.-H. F. H. L. K.-W. F. Sophie E. Jordan, Sierra E. Hovet and Z. T. H. Tse, “Using twitter for public health surveillance from monitoring and prediction to public response,” in Big Data and Digital Health, vol. 4, 2019.
M. E. Marouane Birjali, Abderrahim Beni-Hssane, “Machine learning and semantic sentiment analysis based algorithms for suicide sentiment prediction in social networks,” in Procedia Computer Science, vol. 113,pp. 65–72, 2017.
M. E. Marouane Birjali, Abderrahim Beni-Hssane, “Predicting the pop- ularity of tweets by analyzing public opinion and emotions in different stages of covid-19 pandemic,” in International Journal of Information Management Data Insights, vol. 2, pp. 65–72, 2022.
A. J.-L. S. J. Y.-P. E. C. D. Kim, J., “Fibvid: Comprehensive fake news diffusion dataset during the covid-19 period,” in International Journal of Telematics and Informatics, vol. 64, 2021.
G. A. D. C. H. d. O. F. D. L. L. D. d. F. N. Yasmim Mendes Rocha1, Gabriel Aca´cio de Moura, “The impact of fake news on social media and its influence on health during the covid-19 pandemic: a systematic review,” in Journal of Public Health: From Theory to Practice, vol. 31,pp. 1007–1016, 2023.
M. Aman, “Large language model based fake news detection,” vol. 231, pp. 740–745, 2024.
S.S.-A.Enrique Cano-Marin, Marc¸al Mora-Cantallops, “Twitter as a predictive system: A systematic literature review,” in Journal of Business Research, vol. 257, 2023.
N. A. Mudasir Ahmad Wani and P. Bours, “Impact of unreliable content on social media users during covid-19 and stance detection system,” in Electronic Solutions for Artificial Intelligence Healthcare, 2021.
G. Rampersad and T. Althiyabi, “Fake news: Acceptance by demograph- ics and culture on social media,” in JOURNAL OF INFORMATION TECHNOLOGY POLITICS, vol. 17, pp. 1–11, 2020.
S. C. E. C.-L. V. Gianluca Bonifazi, Bernardo Breve, “Investigating the covid-19 vaccine discussions on twitter through a multilayer network- based approach,” in Information Processing Management, vol. 59, 2022.