Challenges to Transforming Unconventional Social Media Data into Actionable Knowledge for Public Health Systems During Disasters

Abstract

Every year, there are larger and more severe disasters and health organizations are struggling to respond with services to keep public health systems running. Making decisions with limited health information can negatively affect response activities and impact morbidity and mortality. An overarching challenge is getting the right health information to the right health service personnel at the right time. As responding agencies engage in social media (eg, Twitter, Facebook) to communicate with the public, new opportunities emerge to leverage this non-traditional information for improved situational awareness. Transforming these big data is dependent on computers to process and filter content for health information categories relevant to health responders. To enable a more health-focused approach to social media analysis during disasters, 2 major research challenges should be addressed: (1) advancing methodologies to extract relevant information for health services and creating dynamic knowledge bases that address both the global and US disaster contexts, and (2) expanding social media research for disaster informatics to focus on health response activities. There is a lack of attention on health-focused social media research beyond epidemiologic surveillance. Future research will require approaches that address challenges of domain-aware, including multilingual language understanding in artificial intelligence for disaster health information extraction. New research will need to focus on the primary goal of health providers, whose priority is to get the right health information to the right medical and public health service personnel at the right time.

Risk Communication During COVID-19

“…Using multiple forms of social media including Facebook, Twitter, and YouTube videos allows the message to be dispersed more widely within the general public. 30,31 A second important lesson learned from SARS is to ensure an ongoing, consistent relationship with the media, as with SARS "daily headlines generated widespread fear and panic." 27 It is recommended that "efforts to decrease sensationalism, to portray an honest picture, and to elicit the help and understanding of the public" are lessons that can be applied to any epidemic or pandemic.…”

COVID-19 Vaccine Perception in South Korea: Web Crawling Approach

“…Furthermore, the WHO strongly encourages governments to deliver the accurate information about COVID-19 vaccines to citizens [ 12 ]. It is well-known that risk communication using social media, such as Facebook, Twitter, and YouTube, was the most effective way to disseminate information during the SARS epidemic in 2013 [ 13 , 14 ]. That is, governments’ risk communication during the COVID-19 pandemic is critical for increasing the acceptance of nonpharmaceutical approaches and COVID-19 vaccines.…”

Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence

“…Artificial intelligence, also known as machine learning, is a nonlinear mathematical modeling technology that is used extensively in modern day living, such as email communications, social media, web searching, stores and services, banking and finance, aviation and prediction of machinery failure, maps and directions, criminology, and war. 15,31 Recently, artificial intelligence is being increasingly used in clinical medicine including gastroenterology 32,33 endoscopy, 34 and hepatology, 15 radiology, 35 pathology, 36 dentistry, 37 oncology, 38 cardiology, 39 dermatology, 40 neurosurgery, 41 gynecology, 42 and in medical research, particularly big data analysis. Whereas convolutional neural network is the usual network used for image analysis, 43 feed-forward multilayer perceptron networks are the modeling technique for clinical prediction and have been used in the current study as well.…”