Detecting Narcissism From Older Adults' Daily Language Use: A Machine Learning Approach.

OBJECTIVES: Narcissism has been associated with poorer quality social connections in late life, yet less is known about how narcissism is associated with older adults' daily social interactions. This study explored the associations between narcissism and older adults' language use throughout the day. METHODS: Participants aged 65-89 (N = 281) wore electronically activated recorders which captured ambient sound for 30 s every 7 min across 5-6 days. Participants also completed the Narcissism Personality Inventory-16 scale. We used Linguistic Inquiry and Word Count to extract 81 linguistic features from sound snippets and applied a supervised machine learning algorithm (random forest) to evaluate the strength of links between narcissism and each linguistic feature. RESULTS: The random forest model showed that the top 5 linguistic categories that displayed the strongest associations with narcissism were first-person plural pronouns (e.g., we), words related to achievement (e.g., win, success), to work (e.g., hiring, office), to sex (e.g., erotic, condom), and that signal desired state (e.g., want, need). DISCUSSION: Narcissism may be demonstrated in everyday life via word use in conversation. More narcissistic individuals may have poorer quality social connections because their communication conveys an emphasis on self and achievement rather than affiliation or topics of interest to the other party.

Predicting Working Memory in Healthy Older Adults Using Real-Life Language and Social Context Information: A Machine Learning Approach.

BACKGROUND: Language use and social interactions have demonstrated a close relationship with cognitive measures. It is important to improve the understanding of language use and behavioral indicators from social context to study the early prediction of cognitive decline among healthy populations of older adults. OBJECTIVE: This study aimed at predicting an important cognitive ability, working memory, of 98 healthy older adults participating in a 4-day-long naturalistic observation study. We used linguistic measures, part-of-speech (POS) tags, and social context information extracted from 7450 real-life audio recordings of their everyday conversations. METHODS: The methods in this study comprise (1) the generation of linguistic measures, representing idea density, vocabulary richness, and grammatical complexity, as well as POS tags with natural language processing (NLP) from the transcripts of real-life conversations and (2) the training of machine learning models to predict working memory using linguistic measures, POS tags, and social context information. We measured working memory using (1) the Keep Track test, (2) the Consonant Updating test, and (3) a composite score based on the Keep Track and Consonant Updating tests. We trained machine learning models using random forest, extreme gradient boosting, and light gradient boosting machine algorithms, implementing repeated cross-validation with different numbers of folds and repeats and recursive feature elimination to avoid overfitting. RESULTS: For all three prediction routines, models comprising linguistic measures, POS tags, and social context information improved the baseline performance on the validation folds. The best model for the Keep Track prediction routine comprised linguistic measures, POS tags, and social context variables. The best models for prediction of the Consonant Updating score and the composite working memory score comprised POS tags only. CONCLUSIONS: The results suggest that machine learning and NLP may support the prediction of working memory using, in particular, linguistic measures and social context information extracted from the everyday conversations of healthy older adults. Our findings may support the design of an early warning system to be used in longitudinal studies that collects cognitive ability scores and records real-life conversations unobtrusively. This system may support the timely detection of early cognitive decline. In particular, the use of a privacy-sensitive passive monitoring technology would allow for the design of a program of interventions to enable strategies and treatments to decrease or avoid early cognitive decline.

With a little help from familiar interlocutors: real-world language use in young and older adults.

OBJECTIVES: Functional psychologists are concerned with the performance of cognitive activities in the real world in relation to cognitive changes in older age. Conversational contexts may mitigate the influence of cognitive aging on the cognitive activity of language production. This study examined effects of familiarity with interlocutors, as a context, on language production in the real world. METHOD: We collected speech samples using iPhones, where an audio recording app (i.e. Electronically Activated Recorder [EAR]) was installed. Over 31,300 brief audio files (30-second long) were randomly collected across four days from 61 young and 48 healthy older adults in Switzerland. We transcribed the audio files that included participants' speech and manually coded for familiar interlocutors (i.e. significant other, friends, family members) and strangers. We computed scores of vocabulary richness and grammatical complexity from the transcripts using computational linguistics techniques. RESULTS: Bayesian multilevel analyses showed that participants used richer vocabulary and more complex grammar when talking with familiar interlocutors than with strangers. Young adults used more diverse vocabulary than older adults and the age effects remained stable across contexts. Furthermore, older adults produced equally complex grammar as young adults did with the significant other, but simpler grammar than young adults with friends and family members. CONCLUSION: Familiarity with interlocutors is a promising contextual factor for research on aging and language complexity in the real world. Results were discussed in the context of cognitive aging.

A new approach to capturing pain disclosure in daily life in-person and online.

This feasibility study employed a new approach to capturing pain disclosure in face-to-face and online interactions, using a newly developed tool. In Study 1, 13 rheumatoid arthritis and 52 breast cancer patients wore the Electronically Activated Recorder to acoustically sample participants' natural conversations. Study 2 obtained data from two publicly available online social networks: fibromyalgia (343,439 posts) and rheumatoid arthritis (12,430 posts). Pain disclosure, versus non-pain disclosure, posts had a greater number of replies, and greater engagement indexed by language style matching. These studies yielded novel, multimethod evidence of how pain disclosure unfolds in naturally occurring social contexts in everyday life.

Social Reminiscence in Older Adults' Everyday Conversations: Automated Detection Using Natural Language Processing and Machine Learning.

BACKGROUND: Reminiscence is the act of thinking or talking about personal experiences that occurred in the past. It is a central task of old age that is essential for healthy aging, and it serves multiple functions, such as decision-making and introspection, transmitting life lessons, and bonding with others. The study of social reminiscence behavior in everyday life can be used to generate data and detect reminiscence from general conversations. OBJECTIVE: The aims of this original paper are to (1) preprocess coded transcripts of conversations in German of older adults with natural language processing (NLP), and (2) implement and evaluate learning strategies using different NLP features and machine learning algorithms to detect reminiscence in a corpus of transcripts. METHODS: The methods in this study comprise (1) collecting and coding of transcripts of older adults' conversations in German, (2) preprocessing transcripts to generate NLP features (bag-of-words models, part-of-speech tags, pretrained German word embeddings), and (3) training machine learning models to detect reminiscence using random forests, support vector machines, and adaptive and extreme gradient boosting algorithms. The data set comprises 2214 transcripts, including 109 transcripts with reminiscence. Due to class imbalance in the data, we introduced three learning strategies: (1) class-weighted learning, (2) a meta-classifier consisting of a voting ensemble, and (3) data augmentation with the Synthetic Minority Oversampling Technique (SMOTE) algorithm. For each learning strategy, we performed cross-validation on a random sample of the training data set of transcripts. We computed the area under the curve (AUC), the average precision (AP), precision, recall, as well as F1 score and specificity measures on the test data, for all combinations of NLP features, algorithms, and learning strategies. RESULTS: Class-weighted support vector machines on bag-of-words features outperformed all other classifiers (AUC=0.91, AP=0.56, precision=0.5, recall=0.45, F1=0.48, specificity=0.98), followed by support vector machines on SMOTE-augmented data and word embeddings features (AUC=0.89, AP=0.54, precision=0.35, recall=0.59, F1=0.44, specificity=0.94). For the meta-classifier strategy, adaptive and extreme gradient boosting algorithms trained on word embeddings and bag-of-words outperformed all other classifiers and NLP features; however, the performance of the meta-classifier learning strategy was lower compared to other strategies, with highly imbalanced precision-recall trade-offs. CONCLUSIONS: This study provides evidence of the applicability of NLP and machine learning pipelines for the automated detection of reminiscence in older adults' everyday conversations in German. The methods and findings of this study could be relevant for designing unobtrusive computer systems for the real-time detection of social reminiscence in the everyday life of older adults and classifying their functions. With further improvements, these systems could be deployed in health interventions aimed at improving older adults' well-being by promoting self-reflection and suggesting coping strategies to be used in the case of dysfunctional reminiscence cases, which can undermine physical and mental health.

Interpersonal positive reframing in the daily lives of couples coping with breast cancer.

OBJECTIVES: This study examined word use as an indicator of interpersonal positive reframing in daily conversations of couples coping with breast cancer and as a predictor of stress. DESIGN: The Electronically Activated Recorder (EAR) and Linguistic Inquiry and Word Count (LIWC) were used to examine naturally occurring word use conceptually linked to positive reframing (positive emotion, negative emotion, and cognitive processing words). SAMPLE: Fifty-two couples coping with breast cancer. METHODS: Couples wore the EAR, a device participants wear, that audio-recorded over one weekend (>16,000 sound files), and completed self-reports of positive reframing (COPE) and stress (Perceived Stress Scale). LIWC, a software program, measured word use. FINDINGS: Both partners' word use (i.e., positive emotion and cognitive processing words) was associated with their own reported positive reframing, and spouses' word use was also indicative of patients' positive reframing. Results also revealed that, in general, words indicating positive reframing predicted lower levels of stress. CONCLUSIONS: Findings supported the hypothesis that partners-and particularly spouses of breast cancer patients-may assist each other's coping by positively reframing the cancer experience and other negative experiences in conversation.

Reminiscence in Everyday Conversations: A Naturalistic Observation Study of Older Adults.

OBJECTIVES: We examined older adults' social reminiscence behavior in everyday life, and the relation between reminiscence functions and well-being. METHOD: The sample included 2,164 sound snippets that included speech from 45 healthy older adults. We examined reminiscence in daily conversations using the Electronically Activated Recorder. Across four days, we collected a random sample of about 280 sound files (30 seconds long) per participant. Participants' utterances were coded for whether they included reminiscence, for their functions and conversation partners. Participants completed mood and life satisfaction measures. RESULTS: Participants reminisced in 5% of their utterances (range: 0%-29%). They reminisced in 40% of cases with friends, 32.8% with their partner and 8% with their children/relatives. Three reminiscence functions were observed: identity, teaching/informing, and conversation. Participants' reminiscence served the identity function while they were reminiscing with their partner and children. Participants reminisced to teach/inform while reminiscing with their children and strangers. Reminiscing for conversation occurred mainly with partner and friends. We found positive relations between life satisfaction and identity, teach/inform, and conversation functions. Mood had a negative relation with identity and teach/inform functions. DISCUSSION: This is the first study to take a naturalistic observation approach to reminiscence and to build on self-report data.

Naturalistically observing noncancer conversations among couples coping with breast cancer.

OBJECTIVE: This study revealed the landscape of noncancer conversations, identifying topics and types of everyday conversation, and examined links to psychological adjustment among couples coping with breast cancer. METHODS: Fifty-two couples wore the Electronically Activated Recorder (EAR) over 1 weekend and self-reported psychological adjustment while patients were on treatment. The EAR sampled 50 s of ambient sound every 9 minutes to estimate the frequency of noncancer conversation and reveal topics and types of conversation. RESULTS: Analyses revealed noncancer conversations comprised over 93% of conversations. The most common topic discussed was people. Substantive conversation was associated with better, while emotional disclosure was associated with worse, well-being for patients, but not spouses. CONCLUSIONS: Results revealed that ordinary conversations are frequent among couples who face breast cancer, and they are associated with patients' psychological adjustment, providing a foundation for potential interventions for coping with cancer that do not focus on illness.

Eavesdropping on Character: Assessing Everyday Moral Behaviors.

Despite decades of interest in moral character, comparatively little is known about moral behavior in everyday life. This paper reports a novel method for assessing everyday moral behaviors using the Electronically Activated Recorder (EAR)-a digital audio-recorder that intermittently samples snippets of ambient sounds from people's environments-and examines the stability of these moral behaviors. In three samples (combined N = 186), participants wore an EAR over one or two weekends. Audio files were coded for everyday moral behaviors (e.g., showing sympathy, gratitude) and morally-neutral comparison language behaviors (e.g., use of prepositions, articles). Results indicate that stable individual differences in moral behavior can be systematically observed in daily life, and that their stability is comparable to the stability of neutral language behaviors.