Locating Loneliness Through Social Intelligence Analysis
Authors: Ali Shah, H., Househ, M.
Journal: Studies in Health Technology and Informatics
Publication Date: 25/01/2024
Volume: 310
Pages: 594-598
eISSN: 1879-8365
ISSN: 0926-9630
DOI: 10.3233/SHTI231034
Abstract:Loneliness is a global public health issue, but the dynamics of loneliness are not understood. Through a global loneliness map, we plan to understand the dynamics of loneliness better by analyzing social media data on loneliness through social intelligence analysis. In this paper, we present the first proof of concept of the global loneliness map. Data on loneliness using keywords associated with loneliness was collected from the USA and analyzed to find meaningful associations of themes with loneliness. The NLP tool used for sentiment analysis of the tweets is a valence aware dictionary for sentiment reasoning (VADER). The tweets with negative sentiment were further analyzed for psychosocial linguistic features to find meaningful correlation between loneliness and socioeconomic and emotional themes and factors. Loneliness is subjective, hence social intelligence analysis through social media and machine learning tools can help us better understand loneliness.
Source: Scopus
Locating Loneliness Through Social Intelligence Analysis.
Authors: Ali Shah, H., Househ, M.
Journal: Stud Health Technol Inform
Publication Date: 25/01/2024
Volume: 310
Pages: 594-598
eISSN: 1879-8365
DOI: 10.3233/SHTI231034
Abstract:Loneliness is a global public health issue, but the dynamics of loneliness are not understood. Through a global loneliness map, we plan to understand the dynamics of loneliness better by analyzing social media data on loneliness through social intelligence analysis. In this paper, we present the first proof of concept of the global loneliness map. Data on loneliness using keywords associated with loneliness was collected from the USA and analyzed to find meaningful associations of themes with loneliness. The NLP tool used for sentiment analysis of the tweets is a valence aware dictionary for sentiment reasoning (VADER). The tweets with negative sentiment were further analyzed for psychosocial linguistic features to find meaningful correlation between loneliness and socioeconomic and emotional themes and factors. Loneliness is subjective, hence social intelligence analysis through social media and machine learning tools can help us better understand loneliness.
Source: PubMed
Locating Loneliness Through Social Intelligence Analysis
Authors: Shah, H.A., Househ, M.
Journal: MEDINFO 2023 - THE FUTURE IS ACCESSIBLE
Publication Date: 2024
Volume: 310
Pages: 594-598
eISSN: 1879-8365
ISBN: 978-1-64368-456-7
ISSN: 0926-9630
DOI: 10.3233/SHTI231034
Source: Web of Science
Locating Loneliness Through Social Intelligence Analysis.
Authors: Ali Shah, H., Househ, M.
Journal: Studies in health technology and informatics
Publication Date: 01/2024
Volume: 310
Pages: 594-598
eISSN: 1879-8365
ISSN: 0926-9630
DOI: 10.3233/shti231034
Abstract:Loneliness is a global public health issue, but the dynamics of loneliness are not understood. Through a global loneliness map, we plan to understand the dynamics of loneliness better by analyzing social media data on loneliness through social intelligence analysis. In this paper, we present the first proof of concept of the global loneliness map. Data on loneliness using keywords associated with loneliness was collected from the USA and analyzed to find meaningful associations of themes with loneliness. The NLP tool used for sentiment analysis of the tweets is a valence aware dictionary for sentiment reasoning (VADER). The tweets with negative sentiment were further analyzed for psychosocial linguistic features to find meaningful correlation between loneliness and socioeconomic and emotional themes and factors. Loneliness is subjective, hence social intelligence analysis through social media and machine learning tools can help us better understand loneliness.
Source: Europe PubMed Central