title: Using online social networks to track a pandemic: A systematic review.
abstract: The popularity and proliferation of online social networks (OSNs) have created massive social interaction among users that generate an extensive amount of data. An OSN offers a unique opportunity for studying and understanding social interaction and communication among far larger populations now more than ever before. Recently, OSNs have received considerable attention as a possible tool to track a pandemic because they can provide an almost real-time surveillance system at a less costly rate than traditional surveillance systems.
  
  
  
  A systematic literature search for studies with the primary aim of using OSN to detect and track a pandemic was conducted. We conducted an electronic literature search for eligible English articles published between 2004 and 2015 using PUBMED, IEEExplore, ACM Digital Library, Google Scholar, and Web of Science. First, the articles were screened on the basis of titles and abstracts. Second, the full texts were reviewed. All included studies were subjected to quality assessment.
  
  
  
  OSNs have rich information that can be utilized to develop an almost real-time pandemic surveillance system. The outcomes of OSN surveillance systems have demonstrated high correlations with the findings of official surveillance systems. However, the limitation in using OSN to track pandemic is in collecting representative data with sufficient population coverage. This challenge is related to the characteristics of OSN data. The data are dynamic, large-sized, and unstructured, thus requiring advanced algorithms and computational linguistics.
  
  
  
  OSN data contain significant information that can be used to track a pandemic. Different from traditional surveys and clinical reports, in which the data collection process is time consuming at costly rates, OSN data can be collected almost in real time at a cheaper cost. Additionally, the geographical and temporal information can provide exploratory analysis of spatiotemporal dynamics of infectious disease spread. However, on one hand, an OSN-based surveillance system requires comprehensive adoption, enhanced geographical identification system, and advanced algorithms and computational linguistics to eliminate its limitations and challenges. On the other hand, OSN is probably to never replace traditional surveillance, but it can offer complementary data that can work best when integrated with traditional data.
authors.affiliation: Department of Information System, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia. Electronic address: mohammedali@siswa.um.edu.my.
authors.forename: Mohammed Ali
authors.initials: MA
authors.lastname: Al-Garadi
authors.affiliation: Department of Information System, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia.
authors.forename: Muhammad Sadiq
authors.initials: MS
authors.lastname: Khan
authors.affiliation: Department of Information System, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia.
authors.forename: Kasturi Dewi
authors.initials: KD
authors.lastname: Varathan
authors.affiliation: Department of Information System, Faculty of Computer Science & Information Technology, University of Malaya, Kuala Lumpur, Malaysia.
authors.forename: Ghulam
authors.initials: G
authors.lastname: Mujtaba
authors.affiliation: Medical Microbiology Cyberjaya University College of Medical Sciences (CUCMS), Cyberjaya, Selangor, Malaysia.
authors.forename: Abdelkodose M
authors.initials: AM
authors.lastname: Al-Kabsi
mesh_terms: D003141:Communicable Diseases; D006801:Humans; D058873:Pandemics; D011159:Population Surveillance / Q000379:methods; D061108:Social Media; D060756:Social Networking; D012944:Social Support; D011795:Surveys and Questionnaires
keywords: Infectious disease surveillance; Machine learning; Online social network; Systematic review