Population-centric Profiling with Social Data for Large-scale Epidemiological studies

Abstract

Large-scale social data has the potential for enabling insightful epidemiological studies due to the availability of streaming user-generated data. Research has focused on harnessing the predictive power of deep neural models for applications like a region's health status modelling and behavioural profiling of the population using the location-specific user data for identifying health determinants for designing effective population health management policies and prevention measures. Previous studies have often overlooked the need for semantic representation for extracting behavioural insights from social data. This work presents an approach that attempts to leverage contextual representation models for identifying latent population-centric health determinants. Language variants like Character-level, Word-level and Document-level are experimented with to capture the opinion expressed by the users towards this objective. Experimental results revealed that the Document-level approach achieved the most decisive correlation score of 0.7297, which is nearly 60% more than the score obtained by Character-level and Word-level models. The proposed method also outperformed state-of-the-art works by 30%. © 2022 Owner/Author.