Public wellbeing analytics framework using social media chatter data

Abstract

Public wellbeing has always been crucial. Many governments around the globe prioritize the impact of their decisions on public wellbeing. In this paper, we propose an end-to-end public wellbeing analytics framework designed to predict the public’s wellbeing status and infer insights through the continuous analysis of social media content over several temporal events and across several locations. The proposed framework implements a novel distant supervision approach designed specifically to generate wellbeing-labeled datasets. In addition, it implements a wellbeing prediction model trained on contextualized sentence embeddings using BERT. Wellbeing predictions are visualized using several spatiotemporal analytics that can support decision-makers in gauging the impact of several government decisions and temporal events on the public, aiding in improving the decision-making process. Empirical experiments evaluate the effectiveness of the proposed distant supervision approach, the prediction model, and the utility of the produced analytics in gauging the public wellbeing status in a specific context.

We chased COVID-19; Did we forget Measles? - A machine learning approach to public discourse and sentiment analysis on spiking Measles cases

“…Social media is proven to be a platform for deliberations and discussions on any topics, including pandemics and disease outbreaks [3][4][5] [6]. The public commonly shares their opinions, concerns, symptoms, treatments, and adverse reactions to medications on social media such as Twitter [7][8] and Facebook and news aggregators such as Reddit [9] [10]. These highly invaluable data contain trends, and latent themes that may be significantly important to aid public policymakers and governments in prioritizing their strategies to help combat the outbreaks [11][12] [13].…”