Employing online social networks in precision-medicine approach using information fusion predictive model to improve substance use surveillance: A lesson from Twitter and marijuana consumption

Abstract The impact that connected community have on precision health or medicine and vice versa offers opportunities for any type of research and survey information, e. g, to predict trends in health-related issues, specifically people behavior towards drug use. Here, precision medicine influences the way to treat the information and get a better outcome to support the stakeholder decision. Online social networks analysis seems to be good tools to quickly monitor the population behavior where users freely share large amounts of information related to their own lives on day- to- day basis. This novel kind of data can be used to get additional real time insights from people to understand their actual behavior related to drug use (Cortes et al., 2017). The aim of this research is to generated an information fusion model of marijuana use tendency confident enough to be employed by stakeholders. So, we will: (a) collect and process the data from Twitter; (b) design a set of algorithms to estimate the tendency of marijuana use in relation to age, localization and gender, moreover, used a set of processes and activities to verify if our model were performing as expected; and (c) fusion of the information in a model to fully characterize the marijuana use population comparable to the national marijuana consume survey for policy makers utilization to improve drug use prevention. First,we collect the data from Twitter accounts based in Chile using an algorithm for traversing graph data structures, we collected the data from Twitter accounts based in Chile. Then, we estimated marijuana user prevalence during a period from 2006 to 2018 and, within each of the years we predicted the prevalence of user population in relation with age (in range), the localization (regions) and gender. Finally, we built indicators to explore the similarity between data obtained through Twitter (our results) and the actual data collected by the National Service for the Prevention and Rehabilitation of Drug and Alcohol (SENDA) under the same variables analyze in their own survey. When we compare the results of the algorithms and methods developed by us with those provided by the SENDA, we observed that most of the indicators present similar trends, i.e., the variation of the prevalence by years in the age, location and gender, showed similar changes in both analyzes. Also, the algorithms effectiveness and capacity to predict variations of complex cases like marijuana use in Chilean population. This study is a key opportunity to obtain in a faster, low cost and continuous way information about marijuana use, also, is an excellent tool for marijuana surveillance to get information to support policy makers and stakeholder decisions.