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Mapping loneliness through social intelligence analysis: a step towards creating global loneliness map
Hurmat Ali Shah1, Mowafa Househ2
1Hamad Bin Khalifa University, College of Science and Engineering, Doha, Ad-Dawhah, Qatar.
BMJ Health & Care Informatics
|October 12, 2023
Summary
This study used social media to map loneliness in the USA, revealing connections between loneliness, socioeconomic factors, and emotions. Findings inform public health strategies for targeted loneliness interventions.
Area of Science:
- Social Science
- Public Health
- Computational Social Science
Background:
- Loneliness is a significant global public health issue.
- Understanding the complex dynamics of loneliness is crucial for effective interventions.
- Social intelligence analysis offers novel approaches to study population-level loneliness.
Purpose of the Study:
- To develop a proof of concept for a global loneliness map.
- To analyze loneliness dynamics using social media data.
- To identify correlations between loneliness, socioeconomic factors, and emotional states.
Main Methods:
- Collected 841,796 tweets from the USA mentioning loneliness-related keywords in October 2022.
- Performed sentiment analysis on tweets using the Valence Aware Dictionary for Sentiment Reasoning (VADER).
- Analyzed psychosocial linguistic features and socioeconomic/emotional themes in negative sentiment tweets.
Main Results:
- Identified significant connections between loneliness, socioeconomic factors, and emotional themes.
- Revealed variations in topics between positive and negative sentiment tweets.
- Generated city-specific data on correlated topics within broader socioeconomic and emotional categories.
Conclusions:
- Social media data provide valuable insights into the subjective nature of loneliness.
- This study serves as a proof of concept for a global loneliness map.
- Findings have implications for public health strategies and targeted interventions.
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