Social media monitoring for health indicators

Abstract

Social media has been recognised as a new source of information from the general public to help achieve positive social outcomes. Some examples are detecting earthquakes, monitoring ongoing disaster events, tracking public opinion, marketing, human behaviour research and public health issues. Given the large volume of information available on numerous social media platforms currently in use, a significant challenge is to extract meaningful and relevant information for these different purposes. In the area of health research, social media has been investigated to provide health information to the community for the purposes of early warning or intervention, preparedness and targeted health advice. Crowd source content has also been used for disease mapping, see for example Google flu trends, https://www.google.org/flutrends/au/#AU, while information published on social media has been identified as an indicator for public health issues, such as detecting influenza epidemics (Aramaki et al. 2011). The importance of early detection of large-scale contagious disease outbreaks and the ability to understand how a population is reacting to such events, whether naturally occurring or as a result of bioterrorism, is of interest to governments world-wide. Health monitors and decision makers need credible early signals of disease outbreaks. Although this is difficult due to the variability of health monitoring capabilities, early warnings combined with available key data could be used for a number of improved population health outcomes such as estimating the spatiotemporal spread of diseases, severity of disease outbreaks, projected peak time and duration of disease outbreaks, the use and effect of early mitigation measures and the targeted deployment of limited medical resources. This has the potential to augment and complement existing information to reduce the cost of information gathering and analysis to increase the productivity, responsiveness and planning for health agencies to achieve a new perspective on population health for government agencies and health professionals. In Australia, CSIRO have been investigating these techniques using statistical data mining methods and natural language processing procedures, such as text classification and unsupervised clustering, applied to messages published on Twitter to identify content of relevance to emergency managers. A large collection of tweets from Australia and New Zealand have been processed since late 2011 to identify unexpected emergency incidents and to monitor ongoing disaster events (Yin et al. 2012; Power et al. 2014). This previous work has been adapted to develop an investigative tool using content published on Twitter to provide indicators of population health and well being. The aim was to conduct a preliminary feasibility study to better understand the potential for detecting and alerting on medical symptoms in on-line communities using social media postings. The following two key questions were investigated: 1. Is it feasible and valuable to detect and alert on unusual variations in medical symptoms within online Australian communities monitored through social media? 2. Can social media monitoring, equipped with novel statistical and online data mining algorithms, provide reliable early evidence of disease outbreak? This paper reports on our experience to date which includes preliminary positive results indicating that health issues such as colds, influenza and fever expressed by the general public can be identified from tweets originating from Australia. These results need to consider the issue of selection bias inherit in the Twitter data source before population inferences can be made.