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.