Mining of health and disease events on Twitter: validating search protocols within the setting of Indonesia

As of May 2016, there are 24.34 million Indonesian, or around 10% of the population being active monthly on Twitter [1], sharing news, events, as well as their personal feelings and experiences including healthrelated information. Twitter offers a potential for data mining of public information flows [2] and these massive data sources may be exploited for public health monitoring and surveillance purposes [3]. Previous studies have explored the use of Twitter, for example, to track levels of disease activity [4], to predicts heart disease mortality [5], and for measuring health-related quality of life [6]. However, the validity of twitter mining protocols to correctly detect health and disease events is one methodological challenge of this media. This study seeks to validate a search protocol of ill health-related terms using real-time Twitter data which can later be used to understand if, and how, twitter can reveal information on the current health situation in Indonesia. In this validation study of mining protocols, we: 1) extracted geo-located conversations related to health and disease postings on Twitter using a set of predefined keywords, 2) assessed the prevalence, frequency and timing of such content in these conversations, and 3) validated how this search protocol was able to detect relevant disease tweets.