Wang, N., et al. (2024).
Psychiatry research, 339, 116078.
Abstract
Study objectives
Loneliness impacts the health of many older adults, yet effective and targeted interventions are lacking. Compared to surveys, speech data can capture the personalized experience of loneliness. In this proof-of-concept study, we used Natural Language Processing to extract novel linguistic features and AI approaches to identify linguistic features that distinguish lonely adults from non-lonely adults.
Methods
Participants completed UCLA loneliness scales and semi-structured interviews (sections: social relationships, loneliness, successful aging, meaning/purpose in life, wisdom, technology and successful aging). We used the Linguistic Inquiry and Word Count (LIWC-22) program to analyze linguistic features and built a classifier to predict loneliness. Each interview section was analyzed using an explainable AI (XAI) model to classify loneliness.
Results
The sample included 97 older adults (age 66–101 years, 65 % women). The model had high accuracy (Accuracy: 0.889, AUC: 0.8), precision (F1: 0.8), and recall (1.0). The sections on social relationships and loneliness were most important for classifying loneliness. Social themes, conversational fillers, and pronoun usage were important features for classifying loneliness.
Conclusions
XAI approaches can be used to detect loneliness through the analyses of unstructured speech and to better understand the experience of loneliness.
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Here are some thoughts. AI has the potential to be helpful for mental health professionals.
Scientists have made a groundbreaking discovery in detecting loneliness through artificial intelligence (AI). A recent study published reveals that AI can identify loneliness by analyzing unstructured speech patterns. This innovative approach offers a promising solution for addressing loneliness, particularly among older adults.
The analysis showed that lonely individuals frequently referenced social status, religion, and expressed more negative emotions. In contrast, non-lonely individuals focused on social connections, family, and lifestyle. Additionally, lonely individuals used more first-person singular pronouns, indicating a self-focused perspective, whereas non-lonely individuals used more first-person plural pronouns, suggesting a sense of inclusion and connection.
Furthermore, the study found that conversational fillers, non-fluencies, and internet slang were more prevalent in the speech of lonely individuals. Lonely individuals also used more causation conjunctions, indicating a tendency to provide detailed explanations of their experiences. These findings suggest that the way people communicate may reflect their feelings about social relationships.
The AI model offers a scalable and less intrusive method for assessing loneliness, which can significantly impact mental and physical health, particularly in older adults. While the study has limitations, including a relatively small sample size, the researchers aim to expand their work to more diverse populations and explore how to better assess loneliness.
