Decoding loneliness: Can explainable AI help in understanding language differences in lonely older adults?

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.