Do Words Matter? Detecting Social Isolation and Loneliness in Older Adults Using Natural Language Processing

Perceived Impact of COVID-19 in an Underserved Community: A Natural Language Processing Approach

AIM

To utilise natural language processing (NLP) to analyse interviews about the impact of COVID-19 in underserved communities and to compare it to traditional thematic analysis in a small subset of interviews.

DESIGN

NLP and thematic analysis were used together to comprehensively examine the interview data.

METHODS

Fifty transcribed interviews with purposively sampled adults living in underserved communities in the United States, conducted from June 2021 to May 2022, were analysed to explore the impact of the COVID-19 pandemic on social activities, mental and emotional stress and physical and spiritual well-being. NLP includes several stages: data extraction, preprocessing, processing using word embeddings and topic modelling and visualisation. This was compared to thematic analysis in a random sample of 10 interviews.

RESULTS

Six themes emerged from thematic analysis: The New Normal, Juxtaposition of Emotions, Ripple Effects on Health, Brutal yet Elusive Reality, Evolving Connections and Journey of Spirituality and Self-Realisation. With NLP, four clusters of similar context words for each approach were analysed visually and numerically. The frequency-based word embedding approach was most interpretable and well aligned with the thematic analysis.

CONCLUSION

The NLP results complemented the thematic analysis and offered new insights regarding the passage of time, the interconnectedness of impacts and the semantic connections among words. This research highlights the interdependence of pandemic impacts, simultaneously positive and negative effects and deeply individual COVID-19 experiences in underserved communities.

IMPLICATIONS

The iterative integration of NLP and thematic analysis was efficient and effective, facilitating the analysis of many transcripts and expanding nursing research methodology.

IMPACT

While thematic analysis provided richer, more detailed themes, NLP captured new elements and combinations of words, making it a promising tool in qualitative analysis.

REPORTING METHOD

Not applicable.

PATIENT OR PUBLIC CONTRIBUTION

No patient or public contribution.

Analysis of social media language reveals the psychological interaction of three successive upheavals

Using social media data, the present study documents how three successive upheavals: the COVID pandemic, the Black Lives Matter (BLM) protests of 2020, and the US Supreme Court decision to overturn Roe v. Wade interacted to impact the cognitive, emotional, and social styles of people in the US. Text analyses were conducted on 45,225,895 Reddit comments from 2,451,289 users and 889,402 news headlines from four news sources. Results revealed significant shifts in language related to self-focus (e.g., first-person singular pronouns), collective-focus (e.g., first-person plural pronouns), negative emotion (anxiety and anger words), and engagement (e.g., discussion of upheaval-related topics) after each event. Language analyses captured how social justice-related upheavals (BLM, Roe v. Wade) may have affected people in different ways emotionally than those that affected them personally (COVID). The onset of COVID was related to people becoming increasingly anxious and people turned inward to focus on their personal situations. However, BLM and the overturning of Roe v. Wade aroused anger and action, as people may have looked beyond themselves to address these issues. Analysis of upheaval-related discussions captured the public's sustained interest in BLM and COVID, whereas interest in Roe v. Wade declined relatively quickly. Shifts in discussions also showed how events interacted as people focused on only one national event at a time, with interest in other events dampening when a new event occurred. The findings underscore the dynamic nature of culturally shared events that are apparent in everyday online language use.

First-person thought is associated with body awareness in daily life

Sensations from the body are thought to play a critical role in many aspects of conscious experience, including first-person thought. In the present set of studies, we examined within-person relationships between in-the-moment subjective awareness of sensations from the body and self-reported first-person thought in real-world settings using ecological momentary assessment (EMA) protocols. In Study 1, participants reported experiencing greater first-person thoughts in moments when they also reported heightened awareness of sensations from their body, and this relationship was stable over a 4-week period even with mean-level changes in body awareness and first-person thought. In Study 2, we replicated this association in a 1-week EMA protocol using both self-report measures and measures derived from participants' open-ended descriptions of their ongoing thoughts using a natural language processing approach. Taken together, findings shed light on the role of subjective body awareness in other facets of conscious experience.

Investigating Acoustic and Psycholinguistic Predictors of Cognitive Impairment in Older Adults: Modeling Study

BACKGROUND

About one-third of older adults aged 65 years and older often have mild cognitive impairment or dementia. Acoustic and psycho-linguistic features derived from conversation may be of great diagnostic value because speech involves verbal memory and cognitive and neuromuscular processes. The relative decline in these processes, however, may not be linear and remains understudied.

OBJECTIVE

This study aims to establish associations between cognitive abilities and various attributes of speech and natural language production. To date, the majority of research has been cross-sectional, relying mostly on data from structured interactions and restricted to textual versus acoustic analyses.

METHODS

In a sample of 71 older (mean age 83.3, SD 7.0 years) community-dwelling adults who completed qualitative interviews and cognitive testing, we investigated the performance of both acoustic and psycholinguistic features associated with cognitive deficits contemporaneously and at a 1-2 years follow up (mean follow-up time 512.3, SD 84.5 days).

RESULTS

Combined acoustic and psycholinguistic features achieved high performance (F1-scores 0.73-0.86) and sensitivity (up to 0.90) in estimating cognitive deficits across multiple domains. Performance remained high when acoustic and psycholinguistic features were used to predict follow-up cognitive performance. The psycholinguistic features that were most successful at classifying high cognitive impairment reflected vocabulary richness, the quantity of speech produced, and the fragmentation of speech, whereas the analogous top-ranked acoustic features reflected breathing and nonverbal vocalizations such as giggles or laughter.

CONCLUSIONS

These results suggest that both acoustic and psycholinguistic features extracted from qualitative interviews may be reliable markers of cognitive deficits in late life.

A More Connected Future: How Social Connection, Interdisciplinary Approaches, and New Technology Will Shape the Affective Science of Loneliness, a Commentary on the Special Issue

The recent Special Issue of Affective Science considered "The Future of Affective Science," offering new directions for the field. One recurring theme was the need to consider the social nature of emotional experiences. In this article, we take an interdisciplinary approach toward studies of social connection that builds upon current theoretical foundations to address an important public health issue - loneliness. Loneliness is an affective state that is characterized by feelings of isolation and has widespread adverse effects on mental and physical health. Recent studies have established links between loneliness, social connection, and well-being, but most of this work has been siloed in separate fields. We bridge these themes, leveraging advances in technology, such as artificial intelligence-based voice assistants (e.g., Alexa), to illuminate new avenues for detecting and intervening against loneliness "in the wild." Recognizing the power of connection among individuals as social beings and among researchers with shared goals, affective science can advance our understanding of loneliness and provide tangible benefits to society at large.

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

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.

How does the human brain process noisy speech in real life? Insights from the second-person neuroscience perspective

Comprehending speech with the existence of background noise is of great importance for human life. In the past decades, a large number of psychological, cognitive and neuroscientific research has explored the neurocognitive mechanisms of speech-in-noise comprehension. However, as limited by the low ecological validity of the speech stimuli and the experimental paradigm, as well as the inadequate attention on the high-order linguistic and extralinguistic processes, there remains much unknown about how the brain processes noisy speech in real-life scenarios. A recently emerging approach, i.e., the second-person neuroscience approach, provides a novel conceptual framework. It measures both of the speaker's and the listener's neural activities, and estimates the speaker-listener neural coupling with regarding of the speaker's production-related neural activity as a standardized reference. The second-person approach not only promotes the use of naturalistic speech but also allows for free communication between speaker and listener as in a close-to-life context. In this review, we first briefly review the previous discoveries about how the brain processes speech in noise; then, we introduce the principles and advantages of the second-person neuroscience approach and discuss its implications to unravel the linguistic and extralinguistic processes during speech-in-noise comprehension; finally, we conclude by proposing some critical issues and calls for more research interests in the second-person approach, which would further extend the present knowledge about how people comprehend speech in noise.

Sensor-Based Assessment of Social Isolation and Loneliness in Older Adults: A Survey

Social isolation (SI) and loneliness are 'invisible enemies'. They affect older people's health and quality of life and have significant impact on aged care resources. While in-person screening tools for SI and loneliness exist, staff shortages and psycho-social challenges fed by stereotypes are significant barriers to their implementation in routine care. Autonomous sensor-based approaches can be used to overcome these challenges by enabling unobtrusive and privacy-preserving assessments of SI and loneliness. This paper presents a comprehensive overview of sensor-based tools to assess social isolation and loneliness through a structured critical review of the relevant literature. The aim of this survey is to identify, categorise, and synthesise studies in which sensing technologies have been used to measure activity and behavioural markers of SI and loneliness in older adults. This survey identified a number of feasibility studies using ambient sensors for measuring SI and loneliness activity markers. Time spent out of home and time spent in different parts of the home were found to show strong associations with SI and loneliness scores derived from standard instruments. This survey found a lack of long-term, in-depth studies in this area with older populations. Specifically, research gaps on the use of wearable and smart phone sensors in this population were identified, including the need for co-design that is important for effective adoption and practical implementation of sensor-based SI and loneliness assessment in older adults.

Head versus heart: social media reveals differential language of loneliness from depression

We study the language differentially associated with loneliness and depression using 3.4-million Facebook posts from 2986 individuals, and uncover the statistical associations of survey-based depression and loneliness with both dictionary-based (Linguistic Inquiry Word Count 2015) and open-vocabulary linguistic features (words, phrases, and topics). Loneliness and depression were found to have highly overlapping language profiles, including sickness, pain, and negative emotions as (cross-sectional) risk factors, and social relationships and activities as protective factors. Compared to depression, the language associated with loneliness reflects a stronger cognitive focus, including more references to cognitive processes (i.e., differentiation and tentative language, thoughts, and the observation of irregularities), and cognitive activities like reading and writing. As might be expected, less lonely users were more likely to reference social relationships (e.g., friends and family, romantic relationships), and use first-person plural pronouns. Our findings suggest that the mechanisms of loneliness include self-oriented cognitive activities (i.e., reading) and an overattention to the interpretation of information in the environment. These data-driven ecological findings suggest interventions for loneliness that target maladaptive social cognitions (e.g., through reframing the perception of social environments), strengthen social relationships, and treat other affective distress (i.e., depression).

Automatic Assessment of Loneliness in Older Adults Using Speech Analysis on Responses to Daily Life Questions

Loneliness is a perceived state of social and emotional isolation that has been associated with a wide range of adverse health effects in older adults. Automatically assessing loneliness by passively monitoring daily behaviors could potentially contribute to early detection and intervention for mitigating loneliness. Speech data has been successfully used for inferring changes in emotional states and mental health conditions, but its association with loneliness in older adults remains unexplored. In this study, we developed a tablet-based application and collected speech responses of 57 older adults to daily life questions regarding, for example, one's feelings and future travel plans. From audio data of these speech responses, we automatically extracted speech features characterizing acoustic, prosodic, and linguistic aspects, and investigated their associations with self-rated scores of the UCLA Loneliness Scale. Consequently, we found that with increasing loneliness scores, speech responses tended to have less inflections, longer pauses, reduced second formant frequencies, reduced variances of the speech spectrum, more filler words, and fewer positive words. The cross-validation results showed that regression and binary-classification models using speech features could estimate loneliness scores with an R 2 of 0.57 and detect individuals with high loneliness scores with 95.6% accuracy, respectively. Our study provides the first empirical results suggesting the possibility of using speech data that can be collected in everyday life for the automatic assessments of loneliness in older adults, which could help develop monitoring technologies for early detection and intervention for mitigating loneliness.