Surveillance of communicable diseases using social media: A systematic review

• Published in: PloS one 2023-02, Vol.18 (2), p.e0282101-e0282101
• Main Authors: Pilipiec, Patrick, Samsten, Isak, Bota, András
• Format: Article
• Language: English
• Subjects: Analysis; Communicable diseases; Communicable Diseases - epidemiology; Computer and Information Sciences; Data collection; Data entry; Data mining; Diagnosis; Digital media; Disease prevention; Disease transmission; Diseases; Ebola virus; Economic development; Economic growth; Epidemics; Health aspects; Health risks; Health surveillance; Humans; Infectious diseases; Influenza; Information management; Internet service providers; Language; Literature reviews; Machine Learning; Maskininlärning; Medical research; Medicine and Health Sciences; Medicine, Experimental; Mortality; Natural language processing; Public Health; Public Health Surveillance - methods; Real time; Reliability analysis; Remote monitoring; Reproducibility of Results; Research and Analysis Methods; Risk factors; Severe acute respiratory syndrome coronavirus 2; Social Media; Social networks; Social Sciences; Society; Surveillance equipment; Surveillance systems; Systematic review; Trends
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0282101

Communicable diseases pose a severe threat to public health and economic growth. The traditional methods that are used for public health surveillance, however, involve many drawbacks, such as being labor intensive to operate and resulting in a lag between data collection and reporting. To effectively address the limitations of these traditional methods and to mitigate the adverse effects of these diseases, a proactive and real-time public health surveillance system is needed. Previous studies have indicated the usefulness of performing text mining on social media. To conduct a systematic review of the literature that used textual content published to social media for the purpose of the surveillance and prediction of communicable diseases. Broad search queries were formulated and performed in four databases. Both journal articles and conference materials were included. The quality of the studies, operationalized as reliability and validity, was assessed. This qualitative systematic review was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Twenty-three publications were included in this systematic review. All studies reported positive results for using textual social media content to surveille communicable diseases. Most studies used Twitter as a source for these data. Influenza was studied most frequently, while other communicable diseases received far less attention. Journal articles had a higher quality (reliability and validity) than conference papers. However, studies often failed to provide important information about procedures and implementation. Text mining of health-related content published on social media can serve as a novel and powerful tool for the automated, real-time, and remote monitoring of public health and for the surveillance and prediction of communicable diseases in particular. This tool can address limitations related to traditional surveillance methods, and it has the potential to supplement traditional methods for public health surveillance.