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Demonstrating a Social Intelligence Analysis Framework for Loneliness: Infodemiology Approach
Hurmat Ali Shah1, Mowafa Househ2, Loulwah Alsumait3
1The School of Computing and Engineering, Bournemouth University, Poole, United Kingdom.
Online Journal of Public Health Informatics
|January 15, 2026
Summary
This study introduces a novel framework for analyzing loneliness using social media and online data. The approach effectively captures loneliness dynamics, complementing traditional self-report methods for a comprehensive understanding.
Area of Science:
- Computational Social Science
- Digital Health
- Psychosocial Research
Background:
- Loneliness is a dynamic and pervasive psychosocial issue.
- Traditional research methods for loneliness often rely on self-report measures.
- Emerging digital data sources offer new avenues for studying loneliness.
Purpose of the Study:
- To introduce a comprehensive framework for investigating loneliness using social media and online data.
- To demonstrate the framework's utility through a case study.
- To highlight the effectiveness of digital data in loneliness research.
Main Methods:
- Data collection from diverse online platforms including X (formerly Twitter) and Reddit.
- Utilizing tools such as Google Trends and News API for trend analysis.
- Categorizing and analyzing various data modalities to understand loneliness comprehensively.
Main Results:
- The framework successfully collected diverse data relevant to loneliness.
- Regional loneliness trends were identified using Google Trends and News API.
- Sentiment analysis and social intelligence revealed behavioral data and correlations with personal-emotional and socioeconomic factors.
Conclusions:
- The proposed framework offers a valuable complement to traditional psychosocial approaches to loneliness.
- Integrating online data provides novel insights into the dynamics of loneliness.
- This digital data-driven approach enhances the comprehensive understanding of loneliness.
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