Location
Hilton Waikoloa Village, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
7-1-2025 12:00 AM
End Date
10-1-2025 12:00 AM
Description
In an era where digital interactions significantly influence our social interactions, understanding how loneliness is expressed online becomes paramount. This study delves into the linguistic representation of loneliness on Reddit, utilizing techniques in natural language processing (NLP) and machine learning. By employing frequency-based, similarity-based, and association-based methods, a unified lexicon was generated, demonstrating promising performance in classifying loneliness-related posts. The identification and validation of 536 most impactful entries from this lexicon underscore their predictive power and genuine relevance as markers of loneliness. This research advances our comprehension of loneliness within digital contexts and underscores the potential of computational methods in detecting and addressing loneliness online. The identified lexicon lays the groundwork for AI-driven mental health interventions, underscoring the significance of language in understanding and addressing loneliness in the digital age.
Recommended Citation
Fan, Winston; Fan, Jonathan; Yu, Jiyuan; Zhang, Min; Du, Qianzhou; Tong, Ling; and Fan, Weiguo (Patrick), "Loneliness Detection from Social Media: A Text Analytics Approach" (2025). Hawaii International Conference on System Sciences 2025 (HICSS-58). 3.
https://aisel.aisnet.org/hicss-58/in/avatars/3
Loneliness Detection from Social Media: A Text Analytics Approach
Hilton Waikoloa Village, Hawaii
In an era where digital interactions significantly influence our social interactions, understanding how loneliness is expressed online becomes paramount. This study delves into the linguistic representation of loneliness on Reddit, utilizing techniques in natural language processing (NLP) and machine learning. By employing frequency-based, similarity-based, and association-based methods, a unified lexicon was generated, demonstrating promising performance in classifying loneliness-related posts. The identification and validation of 536 most impactful entries from this lexicon underscore their predictive power and genuine relevance as markers of loneliness. This research advances our comprehension of loneliness within digital contexts and underscores the potential of computational methods in detecting and addressing loneliness online. The identified lexicon lays the groundwork for AI-driven mental health interventions, underscoring the significance of language in understanding and addressing loneliness in the digital age.
https://aisel.aisnet.org/hicss-58/in/avatars/3