Public wellbeing analytics framework using social media chatter data

Heba Ismail, M. Adel Serhani, Nada Hussien, Rawan Elabyad, Alramzana Navaz

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Article number163
JournalSocial Network Analysis and Mining
Issue number1
Publication statusPublished - Dec 2022


  • Distant supervision
  • Emotion lexicon
  • Public wellbeing
  • Sentence embeddings
  • Social media
  • Spatiotemporal analytics
  • Twitter

ASJC Scopus subject areas

  • Information Systems
  • Communication
  • Media Technology
  • Human-Computer Interaction
  • Computer Science Applications


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