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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

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Public wellbeing analytics framework using social media chatter data.

Heba Ismail¹, M Adel Serhani², Nada Hussien¹

¹College of Engineering, Abu Dhabi University, Abu Dhabi, UAE.

Social Network Analysis and Mining

|November 8, 2022

Summary

This study introduces a public wellbeing analytics framework using social media data to predict wellbeing status. It aids governments in understanding the public

Keywords:

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

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Area of Science:

Social Sciences
Computational Social Science
Public Health

Background:

Public wellbeing is a key governmental priority.
Analyzing public sentiment and wellbeing is complex.
Existing methods lack continuous, large-scale analysis capabilities.

Purpose of the Study:

To develop an end-to-end framework for public wellbeing analytics.
To predict public wellbeing status using social media content.
To provide spatiotemporal insights for decision-making.

Main Methods:

A novel distant supervision approach for generating wellbeing-labeled datasets.
A wellbeing prediction model utilizing BERT contextualized sentence embeddings.
Spatiotemporal analytics for visualizing wellbeing trends.

Main Results:

The proposed framework effectively predicts public wellbeing status.
The distant supervision approach successfully generates labeled datasets.
Spatiotemporal analytics provide actionable insights for policymakers.

Conclusions:

The developed framework offers a robust method for continuous public wellbeing monitoring.
This approach can significantly aid governments in evaluating policy impacts.
Social media analysis provides valuable data for understanding public wellbeing.