Natural emotion vocabularies as windows on distress and well-being

Improving the Objective Measurement of Alexithymia Using a Computer-Scored Alexithymia Provoked Response Questionnaire with an Online Sample

The study and measurement of alexithymia - a trait marked by difficulty identifying and describing feelings - can be improved by incorporating objective measures to supplement self-report scales. The Alexithymia Provoked Response Questionnaire (APRQ) is an observer-rated alexithymia tool that shows promise yet can be time-consuming to administer. The present study aimed to assess the feasibility of computer administration and scoring of the APRQ. Further, the APRQ's association with verbal IQ and emotional vocabulary use was examined, as was the relationship between the APRQ and the self-report Bermond-Vorst Alexithymia Questionnaire-B (BVAQ-B). Adult participants (n = 366), including a proportion gathered through purposive sampling, participated in an online study. Inter-rater reliability measures indicated that computerized scoring of the APRQ is as reliable as human scoring, making the measure scalable for use with large samples. Alexithymia levels were independent of two measures of verbal IQ. Correlational analyses indicated overlap in alexithymia as measured by the APRQ and most of the subscales of the BVAQ-B. The APRQ, as an objective measure, may capture deficits in emotional awareness independent of self-insight.

Beyond trauma: a review of content and linguistic characteristics of nontrauma narratives in posttraumatic stress disorder

Background: Using narratives to reflect on experiences, emotions, and thoughts is associated with better health, enhanced mood, and improved symptoms of posttraumatic stress disorder (PTSD). Prior research examining narrative characteristics thought to reflect cognitive styles associated with PTSD has focused on trauma narratives, but the characteristics of nontrauma narratives in relation to PTSD are not fully understood.Objective: We reviewed the PTSD literature examining linguistic characteristics of nontrauma narratives, focusing on affective content, personal pronouns, and cognitive processing words.Method: We searched online databases for both laboratory and social media studies examining these characteristics of nontrauma narratives in relation to PTSD diagnostic status and/or PTSD symptom severity.Results: Following SWiM guidelines [Campbell et al., 2020. Synthesis without meta-analysis (SWiM) in systematic reviews: Reporting guideline. British Medical Journal, 368, l6890], there was moderate evidence for differential use of emotion words in nontrauma narratives in relation to PTSD symptom cluster severity. More severe avoidance/numbing symptoms were associated with greater use of negative emotion words and less use of positive emotion words. Results were mixed for other linguistic elements reviewed.Conclusions: Differential use of emotional language in trauma narratives generalises to nontrauma narratives in individuals with PTSD. Additional research is needed to elucidate the use of personal pronouns and cognitive processing words in nontrauma narratives.

How developments in natural language processing help us in understanding human behaviour

The ways people use language can reveal clues to their emotions, social behaviours, thinking styles, cultures and the worlds around them. In the past two decades, research at the intersection of social psychology and computer science has been developing tools to analyse natural language from written or spoken text to better understand social processes and behaviour. The goal of this Review is to provide a brief overview of the methods and data currently being used and to discuss the underlying meaning of what language analyses can reveal in comparison with more traditional methodologies such as surveys or hand-scored language samples.

Using Freely Generated Labels Instead of Rating Scales to Assess Emotion in Everyday Life

To measure emotion in daily life, studies often prompt participants to repeatedly rate their feelings on a set of prespecified terms. This approach has yielded key findings in the psychological literature yet may not represent how people typically describe their experiences. We used an alternative approach, in which participants labeled their current emotion with at least one word of their choosing. In an initial study, estimates of label positivity recapitulated momentary valence ratings and were associated with self-reported mental health. The number of unique emotion words used over time was related to the balance and spread of emotions endorsed in an end-of-day rating task, but not to other measures of emotional functioning. A second study tested and replicated a subset of these findings. Considering the variety and richness of participant responses, a free-label approach appears to be a viable as well as compelling means of studying emotion in everyday life.

Language sentiment predicts changes in depressive symptoms

The prevalence of depression is a major societal health concern, and there is an ongoing need to develop tools that predict who will become depressed. Past research suggests that depression changes the language we use, but it is unclear whether language is predictive of worsening symptoms. Here, we test whether the sentiment of brief written linguistic responses predicts changes in depression. Across two studies (N = 467), participants provided responses to neutral open-ended questions, narrating aspects of their lives relevant to depression (e.g., mood, motivation, sleep). Participants also completed the Patient Health Questionnaire (PHQ-9) to assess depressive symptoms and a risky decision-making task with periodic measurements of momentary happiness to quantify mood dynamics. The sentiment of written responses was evaluated by human raters (N = 470), Large Language Models (LLMs; ChatGPT 3.5 and 4.0), and the Linguistic Inquiry and Word Count (LIWC) tool. We found that language sentiment evaluated by human raters and LLMs, but not LIWC, predicted changes in depressive symptoms at a three-week follow-up. Using computational modeling, we found that language sentiment was associated with current mood, but language sentiment predicted symptom changes even after controlling for current mood. In summary, we demonstrate a scalable tool that combines brief written responses with sentiment analysis by AI tools that matches human performance in the prediction of future psychiatric symptoms.

Too much focus on your health might be bad for your health: Reddit user's communication style predicts their Long COVID likelihood

Long Covid is a chronic disease that affects more than 65 million people worldwide, characterized by a wide range of persistent symptoms following a Covid-19 infection. Previous studies have investigated potential risk factors contributing to elevated vulnerability to Long Covid. However, research on the social traits associated with affected patients is scarce. This study introduces an innovative methodological approach that allows us to extract valuable insights directly from patients' voices. By analyzing written texts shared on social media platforms, we aim to collect information on the psychological aspects of people who report experiencing Long Covid. In particular, we collect texts of patients they wrote BEFORE they were afflicted with Long Covid. We examined the differences in communication style, sentiment, language complexity, and psychological factors of natural language use among the profiles of 6.107 Reddit users, distinguishing between those who claim they have never contracted Covid -19, those who claim to have had it, and those who claim to have experienced Long Covid symptoms. Our findings reveal that people in the Long Covid group frequently discussed health-related topics before the pandemic, indicating a greater focus on health-related concerns. Furthermore, they exhibited a more limited network of connections, lower linguistic complexity, and a greater propensity to employ emotionally charged expressions than the other groups. Using social media data, we can provide a unique opportunity to explore potential risk factors associated with Long Covid, starting from the patient's perspective.

How do "words poorly expressed emotion" affect mental health? The mediating role of affect labelling effect

Previous studies have found that people with high negative emotional granularity(NEG) tend to have better health levels. It is generally believed that this is due to the selection and application of explicit emotion regulation strategies that affect mental health. However, no research has yet examined a more fundamental process, the role of affect labelling, an implicit emotion regulation strategy. This study focuses on the aforementioned issues and uses the experience sampling method to categorise participants into groups with high and low NEG. Using an affect labelling paradigm with ERP(event-related potential) technology, the study measures the effects of affect labelling in participants. Furthermore, it assesses the mental health levels of the participants through questionnaires to explore whether the affect labelling effect serves as a mediator between NEG and mental health. The results show that: (1) The high-NEG group exhibited significantly lower LPP wave amplitudes under affect labelling compared to under non-affect labelling, whereas the low-NEG group did not show significant differences. The results indicate that only the high-NEG group can produce the affect labelling effect. (2) The affect labelling effect mediates the relationship between NEG and mental health, meaning that NEG predicts mental health through the affect labelling effect.

Speaking Well and Feeling Good: Age-Related Differences in the Affective Language of Resting State Thought

Despite the prevalence and importance of resting state thought for daily functioning and psychological well-being, it remains unclear how such thoughts differ between young and older adults. Age-related differences in the affective tone of resting state thoughts, including the affective language used to describe them, could be a novel manifestation of the positivity effect, with implications for well-being. To examine this possibility, a total of 77 young adults (M = 24.9 years, 18-35 years) and 74 cognitively normal older adults (M = 68.6 years, 58-83 years) spoke their thoughts freely during a think-aloud paradigm across two studies. The emotional properties of spoken words and participants' retrospective self-reported affective experiences were computed and examined for age differences and relationships with psychological well-being. Study 1, conducted before the start of the COVID-19 pandemic, revealed that older adults exhibited more diversity of positive, but not negative, affectively tinged words compared to young adults and more positive self-reported thoughts. Despite being conducted virtually during the COVID-19 pandemic, study 2 replicated many of study 1's findings, generalizing results across samples and study contexts. In an aggregated analysis of both samples, positive diversity predicted higher well-being beyond other metrics of affective tone, and the relationship between positive diversity and well-being was not moderated by age. Considering that older adults also exhibited higher well-being, these results hint at the possibility that cognitively healthy older adults' propensity to experience more diverse positive concepts during natural periods of restful thought may partly underlie age-related differences in well-being and reveal a novel expression of the positivity effect.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42761-024-00239-z.

In the Words of Early Adolescents: A Novel Assessment of Positive Psychological Well-Being Predicts Young Adult Depressive Symptoms

PURPOSE

Given the burden of depression in young adulthood, identifying protective early life factors is important. Protective factors like positive psychological well-being may be challenging to assess via conventional methods if early adolescents lack personal insight or informants disagree. We investigated whether essays written by 11-year-olds could indicate the presence of positive psychological well-being and predict depressive symptom levels in young adulthood, beyond informant reports of problematic behaviors.

METHODS

Data were from 4,599 individuals in the 1958 National Child Development Study who wrote an essay at age 11 about how they imagined their life at age 25. Coders rated essays for seven facets of positive psychological well-being, which were averaged together (α = 0.92). Participants self-reported depressive symptoms (yes/no) at age 23 on the 24-item Malaise Inventory. Depressive symptoms were modeled as a sum, both continuously (range = 0-24) and dichotomously (depressed: total scores ≥8). Linear and logistic regressions adjusted for relevant age 11 covariates including teacher-reported internalizing and externalizing behaviors.

RESULTS

Unadjusted logistic regression showed a 1-SD higher positive psychological well-being score in early adolescence was associated with reduced odds of being depressed 12 years later (odds ratio = 0.83, 95% confidence interval [0.75, 0.93], p = .001). Associations remained when adjusting for all covariates (odds ratio = 0.87, 95% confidence interval [0.78, 0.98], p = .02); patterns were similar with continuous depressive symptoms.

DISCUSSION

A well-being measure derived from the words of 11-year-olds was associated with young adult depressive symptoms independent of teacher-reported internalizing and externalizing behaviors. Incorporating early adolescents' perspectives on positive functioning provides valuable information about current and future health beyond problem behaviors.

A systematic evaluation of text mining methods for short texts: Mapping individuals' internal states from online posts

Short texts generated by individuals in online environments can provide social and behavioral scientists with rich insights into these individuals' internal states. Trained manual coders can reliably interpret expressions of such internal states in text. However, manual coding imposes restrictions on the number of texts that can be analyzed, limiting our ability to extract insights from large-scale textual data. We evaluate the performance of several automatic text analysis methods in approximating trained human coders' evaluations across four coding tasks encompassing expressions of motives, norms, emotions, and stances. Our findings suggest that commonly used dictionaries, although performing well in identifying infrequent categories, generate false positives too frequently compared to other methods. We show that large language models trained on manually coded data yield the highest performance across all case studies. However, there are also instances where simpler methods show almost equal performance. Additionally, we evaluate the effectiveness of cutting-edge generative language models like GPT-4 in coding texts for internal states with the help of short instructions (so-called zero-shot classification). While promising, these models fall short of the performance of models trained on manually analyzed data. We discuss the strengths and weaknesses of various models and explore the trade-offs between model complexity and performance in different applications. Our work informs social and behavioral scientists of the challenges associated with text mining of large textual datasets, while providing best-practice recommendations.

Multimodal digital assessment of depression with actigraphy and app in Hong Kong Chinese

There is an emerging potential for digital assessment of depression. In this study, Chinese patients with major depressive disorder (MDD) and controls underwent a week of multimodal measurement including actigraphy and app-based measures (D-MOMO) to record rest-activity, facial expression, voice, and mood states. Seven machine-learning models (Random Forest [RF], Logistic regression [LR], Support vector machine [SVM], K-Nearest Neighbors [KNN], Decision tree [DT], Naive Bayes [NB], and Artificial Neural Networks [ANN]) with leave-one-out cross-validation were applied to detect lifetime diagnosis of MDD and non-remission status. Eighty MDD subjects and 76 age- and sex-matched controls completed the actigraphy, while 61 MDD subjects and 47 controls completed the app-based assessment. MDD subjects had lower mobile time (P = 0.006), later sleep midpoint (P = 0.047) and Acrophase (P = 0.024) than controls. For app measurement, MDD subjects had more frequent brow lowering (P = 0.023), less lip corner pulling (P = 0.007), higher pause variability (P = 0.046), more frequent self-reference (P = 0.024) and negative emotion words (P = 0.002), lower articulation rate (P < 0.001) and happiness level (P < 0.001) than controls. With the fusion of all digital modalities, the predictive performance (F1-score) of ANN for a lifetime diagnosis of MDD was 0.81 and 0.70 for non-remission status when combined with the HADS-D item score, respectively. Multimodal digital measurement is a feasible diagnostic tool for depression in Chinese. A combination of multimodal measurement and machine-learning approach has enhanced the performance of digital markers in phenotyping and diagnosis of MDD.

Reproductive Strategies and Romantic Love in Early Modern Europe

In Western Europe, the Early Modern Period is characterized by the rise of tenderness in romantic relationships and the emergence of companionate marriage. Despite a long research tradition, the origins of these social changes remain elusive. In this paper, we build on recent advances in behavioral sciences, showing that romantic emotional investment, which is more culturally variable than sexual attraction, enhances the cohesion of long-term relationships and increases investment in children. Importantly, this long-term strategy is considered especially advantageous when living standards are high. Here, we investigate the relationship between living standards, the emotional components of love expressed in fiction work, and behavioral outcomes related to pair bonding, such as nuptial and fertility rates. We developed natural language processing measures of "emotional investment" (tenderness) and "attraction" (passion) and computed romantic love in English plays (N = 847) as a ratio between the two. We found that living standards generally predicted and temporally preceded variations of romantic love in the Early Modern Period. Furthermore, romantic love preceded an increase in nuptial rates and a decrease in births per marriage. This suggests that increasing living standards in the Early Modern Period may have contributed to the emergence of modern romantic culture.

LEXpander: Applying colexification networks to automated lexicon expansion

Recent approaches to text analysis from social media and other corpora rely on word lists to detect topics, measure meaning, or to select relevant documents. These lists are often generated by applying computational lexicon expansion methods to small, manually curated sets of seed words. Despite the wide use of this approach, we still lack an exhaustive comparative analysis of the performance of lexicon expansion methods and how they can be improved with additional linguistic data. In this work, we present LEXpander, a method for lexicon expansion that leverages novel data on colexification, i.e., semantic networks connecting words with multiple meanings according to shared senses. We evaluate LEXpander in a benchmark including widely used methods for lexicon expansion based on word embedding models and synonym networks. We find that LEXpander outperforms existing approaches in terms of both precision and the trade-off between precision and recall of generated word lists in a variety of tests. Our benchmark includes several linguistic categories, as words relating to the financial area or to the concept of friendship, and sentiment variables in English and German. We also show that the expanded word lists constitute a high-performing text analysis method in application cases to various English corpora. This way, LEXpander poses a systematic automated solution to expand short lists of words into exhaustive and accurate word lists that can closely approximate word lists generated by experts in psychology and linguistics.

Behavioral consequences of second-person pronouns in written communications between authors and reviewers of scientific papers

Pronoun usage's psychological underpinning and behavioral consequence have fascinated researchers, with much research attention paid to second-person pronouns like "you," "your," and "yours." While these pronouns' effects are understood in many contexts, their role in bilateral, dynamic conversations (especially those outside of close relationships) remains less explored. This research attempts to bridge this gap by examining 25,679 instances of peer review correspondence with Nature Communications using the difference-in-differences method. Here we show that authors addressing reviewers using second-person pronouns receive fewer questions, shorter responses, and more positive feedback. Further analyses suggest that this shift in the review process occurs because "you" (vs. non-"you") usage creates a more personal and engaging conversation. Employing the peer review process of scientific papers as a backdrop, this research reveals the behavioral and psychological effects that second-person pronouns have in interactive written communications.

Natural language processing analysis of the psychosocial stressors of mental health disorders during the pandemic

Over the past few years, the COVID-19 pandemic has exerted various impacts on the world, notably concerning mental health. Nevertheless, the precise influence of psychosocial stressors on this mental health crisis remains largely unexplored. In this study, we employ natural language processing to examine chat text from a mental health helpline. The data was obtained from a chat helpline called Safe Hour from the "It Gets Better" project in Chile. This dataset encompass 10,986 conversations between trained professional volunteers from the foundation and platform users from 2018 to 2020. Our analysis shows a significant increase in conversations covering issues of self-image and interpersonal relations, as well as a decrease in performance themes. Also, we observe that conversations involving themes like self-image and emotional crisis played a role in explaining both suicidal behavior and depressive symptoms. However, anxious symptoms can only be explained by emotional crisis themes. These findings shed light on the intricate connections between psychosocial stressors and various mental health aspects in the context of the COVID-19 pandemic.

Telehealth Talk on Reddit: Understanding How Language Use About Telemedicine Evolved Throughout the COVID-19 Pandemic

The COVID-19 health pandemic acted as a punctuated event that spurred rapid change in healthcare delivery, pushing us to adopt new socio-cultural norms and ways of communicating. The pandemic also altered several long-standing structures within healthcare organizations. To better understand peoples' perceptions of how the pandemic shifted technological structures within healthcare, this study examines a telemedicine (TM) Reddit forum. Analyzing language use on Reddit offered a bottom-up means of examining the public's feelings, understandings, and conceptualizations of TM. Studying language use provides rich insight into how people experience and make sense of the world around them. We specifically examined three time periods: (1) prior to the COVID-19 outbreak, (2) the two years at the center of the outbreak, wherein TM coverage increased-high-risk COVID, and (3) the point at which COVID-19 community risk levels largely diminished -low-risk COVID. Using LIWC, we studied around 1500 conversations posted in the TM forum from 2015 to 2022. Results reveal how people's language use and emotions surrounding TM meaningfully shifted over-time, along with the pandemic stages. Specifically, negative emotion language significantly increased and positive emotion language significantly decreased during Time 3-low-risk COVID. Use of body and health words increased throughout the time periods, and there were no significant differences in cognitive processing words use-which were used very frequently across all time periods. Theoretical and practical implications are offered.

The Promise of Affective Language for Identifying and Intervening on Psychopathology

We are in dire need of innovative tools for reducing the global burden of psychopathology. Emerging evidence suggests that analyzing language (i.e., the words people use) can grant insight into an individual's emotional experiences, their ability to regulate their emotions, and even their current experiences of psychopathology. As such, linguistic analyses of people's everyday word use may be a diagnostic marker of emotional well-being, and manipulating the words people use could foster adaptive emotion regulation and mental health. Given the ubiquity of language in everyday life, such language-based tools for measuring and intervening in emotion and mental health can advance how we identify and treat mental illnesses at a large scale. In this paper, I outline the promise of this approach and identify key problems we must solve if we are to make it a reality. In particular, I summarize evidence connecting language, emotion, and mental health for three key constructs: sentiment (i.e., the valence of one's language), linguistic distancing (i.e., using language to separate oneself from distressing stimuli), and emotion differentiation (i.e., using words to specifically identify one's emotions). I also identify open questions in need of attention for each of these constructs and this area of research as a whole. Overall, I believe the future is bright for the application of psycholinguistic approaches to mental health detection and intervention.

Blending citizen science with natural language processing and machine learning: Understanding the experience of living with multiple sclerosis

The emergence of new digital technologies has enabled a new way of doing research, including active collaboration with the public ('citizen science'). Innovation in machine learning (ML) and natural language processing (NLP) has made automatic analysis of large-scale text data accessible to study individual perspectives in a convenient and efficient fashion. Here we blend citizen science with innovation in NLP and ML to examine (1) which categories of life events persons with multiple sclerosis (MS) perceived as central for their MS; and (2) associated emotions. We subsequently relate our results to standardized individual-level measures. Participants (n = 1039) took part in the 'My Life with MS' study of the Swiss MS Registry which involved telling their story through self-selected life events using text descriptions and a semi-structured questionnaire. We performed topic modeling ('latent Dirichlet allocation') to identify high-level topics underlying the text descriptions. Using a pre-trained language model, we performed a fine-grained emotion analysis of the text descriptions. A topic modeling analysis of totally 4293 descriptions revealed eight underlying topics. Five topics are common in clinical research: 'diagnosis', 'medication/treatment', 'relapse/child', 'rehabilitation/wheelchair', and 'injection/symptoms'. However, three topics, 'work', 'birth/health', and 'partnership/MS' represent domains that are of great relevance for participants but are generally understudied in MS research. While emotions were predominantly negative (sadness, anxiety), emotions linked to the topics 'birth/health' and 'partnership/MS' was also positive (joy). Designed in close collaboration with persons with MS, the 'My Life with MS' project explores the experience of living with the chronic disease of MS using NLP and ML. Our study thus contributes to the body of research demonstrating the potential of integrating citizen science with ML-driven NLP methods to explore the experience of living with a chronic condition.

Sound Healing Reduces Generalized Anxiety During the Pandemic: A Feasibility Study

OBJECTIVES

This study examined the feasibility and effectiveness of a virtually-delivered, biofield-based sound healing treatment to reduce anxiety for individuals meeting criteria for Generalized Anxiety Disorder.

DESIGN

This one-group, mixed-method feasibility study was conducted virtually via Zoom during the SARS-CoV-2 Pandemic. Fifteen participants with moderate to high levels of anxiety as determined by the Generalized Anxiety Disorder-7 (≥10), were enrolled.

INTERVENTION

Five certified Biofield Tuning Practitioners performed the interventions. Participants were given three weekly, hour-long sound healing treatments virtually, over a month's period.

OUTCOME MEASURES

Attrition rates and reports on feasibility of intervention delivery and outcomes assessment were obtained by participants. Data on anxiety, positive and negative affect, spiritual experience, perceived stress, and quality of life were obtained via validated surveys and analyzed via repeated-measures analysis of variance with intention-to-treat. Linguistic inquiry and word count was utilized to assess changes in affective processing as reflected in participants' spoken words over the course of the intervention. Qualitative interviews were conducted to further determine tolerability and experiences with receiving BT that may not have been captured by survey and language data.

RESULTS

Attrition rates were 13.3%, with two participants dropping out of the study after one session. The remaining participants reported acceptability of the data collection process and intervention delivery. Intention to treat analyses revealed statistically significant reductions in anxiety (State-Trait Anxiety Inventory), negative affect (Positive and Negative Affect Scale), and perceived stress (Perceived Stress Scale) (p <.001 in all cases). Linguistic and word count analysis revealed a significant linear decrease (p =.01) of participants' use of negative affect words over the course of the intervention. Qualitative data results are reported in another paper.

CONCLUSIONS

Results indicate that BT delivered virtually is feasible and amenable to study, and that the impact of BT may be substantial in reducing anxiety and improving mental health. This is the first study of its kind to report clinically significant reductions in anxiety levels in response to a virtually-delivered, biofield-based sound therapy. Data will be used to power a randomized controlled trial to more deeply examine the effects of BT on whole-person healing for those suffering from anxiety.

Predicting life satisfaction based on the emotion words in self-statement texts

Measuring people's life satisfaction in real time on a large scale is quite valuable for monitoring and promoting public mental health; however, the traditional questionnaire method cannot fully meet this need. This study utilized the emotion words in self-statement texts to train machine learning predictive models to identify an individual's life satisfaction. The SVR model was found to have the best performance, with the correlation between predicted scores and self-reported questionnaire scores achieving 0.42 and the split-half reliability achieving 0.939. This result demonstrates the possibility of identifying life satisfaction through emotional expressions and provides a method to measure the public's life satisfaction online. The word categories selected through the modeling process were happy (PA), sorrow (NB), boredom (NE), reproach (NN), glad (MH), aversion (ME), and N (negation + positive), which reveal the specific emotions in self-expression relevant to life satisfaction.

Detecting depression of Chinese microblog users via text analysis: Combining Linguistic Inquiry Word Count (LIWC) with culture and suicide related lexicons

INTRODUCTION

In recent years, research has used psycholinguistic features in public discourse, networking behaviors on social media and profile information to train models for depression detection. However, the most widely adopted approach for the extraction of psycholinguistic features is to use the Linguistic Inquiry Word Count (LIWC) dictionary and various affective lexicons. Other features related to cultural factors and suicide risk have not been explored. Moreover, the use of social networking behavioral features and profile features would limit the generalizability of the model. Therefore, our study aimed at building a prediction model of depression for text-only social media data through a wider range of possible linguistic features related to depression, and illuminate the relationship between linguistic expression and depression.

METHODS

We collected 789 users' depression scores as well as their past posts on Weibo, and extracted a total of 117 lexical features via Simplified Chinese Linguistic Inquiry Word Count, Chinese Suicide Dictionary, Chinese Version of Moral Foundations Dictionary, Chinese Version of Moral Motivation Dictionary, and Chinese Individualism/Collectivism Dictionary.

RESULTS

Results showed that all the dictionaries contributed to the prediction. The best performing model occurred with linear regression, with the Pearson correlation coefficient between predicted values and self-reported values was 0.33, the R-squared was 0.10, and the split-half reliability was 0.75.

DISCUSSION

This study did not only develop a predictive model applicable to text-only social media data, but also demonstrated the importance taking cultural psychological factors and suicide related expressions into consideration in the calculation of word frequency. Our research provided a more comprehensive understanding of how lexicons related to cultural psychology and suicide risk were associated with depression, and could contribute to the recognition of depression.

A computational approach to measure the linguistic characteristics of psychotherapy timing, responsiveness, and consistency

Although individual psychotherapy is generally effective for a range of mental health conditions, little is known about the moment-to-moment language use of effective therapists. Increased access to computational power, coupled with a rise in computer-mediated communication (telehealth), makes feasible the large-scale analyses of language use during psychotherapy. Transparent methodological approaches are lacking, however. Here we present novel methods to increase the efficiency of efforts to examine language use in psychotherapy. We evaluate three important aspects of therapist language use - timing, responsiveness, and consistency - across five clinically relevant language domains: pronouns, time orientation, emotional polarity, therapist tactics, and paralinguistic style. We find therapist language is dynamic within sessions, responds to patient language, and relates to patient symptom diagnosis but not symptom severity. Our results demonstrate that analyzing therapist language at scale is feasible and may help answer longstanding questions about specific behaviors of effective therapists.

Perceptions of Death Among Patients with Advanced Cancer Receiving Early Palliative Care and Their Caregivers: Results from a Mixed-Method Analysis

BACKGROUND

Oncologists are often concerned that talking about death with patients may hinder their relationship. However, the views of death held by patients have not been thoroughly investigated. This study aimed to describe the perception of death among patients with advanced cancer receiving early palliative care (EPC) and their caregivers.

MATERIAL AND METHODS

Qualitative and quantitative analyses were performed on 2 databases: (a) transcripts of open-ended questionnaires administered to 130 cancer patients receiving EPC with a mean age of 68.4 years and to 115 primary caregivers of patients on EPC with a mean age of 56.8; (b) texts collected from an Italian forum, containing instances of web-mediated interactions between patients and their caregivers.

RESULTS

Quantitative analysis shows that: (a) patients and caregivers are not afraid of speaking about death; (b) patients and caregivers on EPC use the word "death" significantly more than patients on standard oncology care (SOC) and their caregivers (P < .0001). For both participants on EPC and SOC, the adjectives and verbs associated with the word "death" have positive connotations; however, these associations are significantly more frequent for participants on EPC (verbs, Ps < .0001; adjectives, Ps < .003). Qualitative analysis reveals that these positive connotations refer to an actual, positive experience of the end of life in the EPC group and a wish or a negated event in the SOC group.

CONCLUSIONS

EPC interventions, along with proper physician-patient communication, may be associated with an increased acceptance of death in patients with advanced cancer and their caregivers.

Psychological Well-Being in Childhood and Cardiometabolic Risk in Middle Adulthood: Findings From the 1958 British Birth Cohort

Childhood adversity is linked to poor cardiometabolic outcomes, but less is known about positive childhood factors. Using data from 4,007 members of the 1958 British Birth Cohort, we investigated whether children with greater psychological well-being had lower adulthood cardiometabolic risk. At age 11, participants wrote essays about their future. Two judges rated each essay for nine psychological well-being items (Finn's r = .82-.91), which were combined into a standardized overall score (Cronbach's α = .91). When participants reached age 45, nurses assessed their blood pressure, heart rate, lipids, glycosylated hemoglobin, fibrinogen, and C-reactive protein, which were standardized and summed for total cardiometabolic risk. Regressions indicated that children with greater psychological well-being had lower cardiometabolic risk (b = -0.14, 95% confidence interval [CI] = [-0.28, -0.006]): specifically, healthier total cholesterol (b = -0.04, 95% CI = [-0.07, -0.003]) and triglycerides (b = -0.06, 95% CI = [-0.09, -0.02]). Childhood psychological well-being may promote adulthood cardiometabolic health.

How Differing Audiences Were Associated with User Emotional Expression on a Well-Being App

In the last five years there has been an explosion of mobile apps that aim to impact emotional well-being, yet limited research has examined the ways that users interact, and specifically write to develop a therapeutic alliance within these apps. Writing is a developmental practice in which a narrator transforms amorphous thoughts and emotions into expressions, and according to narrative theory, the linguistic characteristics of writing can be understood as a physical manifestation of a narrator's affect. Informed by literacy theorists who have argued convincingly that narrators address different audiences in different ways, we used IBM Watson's Natural Language Processing software (IBM Watson NLP) to examine how users' expression of emotion on a well-being app differed depending on the audience. Our findings demonstrate that audience was strongly associated with the way users' expressed emotions in writing. When writing to an explicit audience users wrote longer narratives, with less sadness, less anger, less disgust, less fear and more joy. These findings have direct relevance for researchers and well-being app design.

Tracking group identity through natural language within groups

To what degree can we determine people's connections with groups through the language they use? In recent years, large archives of behavioral data from social media communities have become available to social scientists, opening the possibility of tracking naturally occurring group identity processes. A feature of most digital groups is that they rely exclusively on the written word. Across 3 studies, we developed and validated a language-based metric of group identity strength and demonstrated its potential in tracking identity processes in online communities. In Studies 1a-1c, 873 people wrote about their connections to various groups (country, college, or religion). A total of 2 language markers of group identity strength were found: high affiliation (more words like we, togetherness) and low cognitive processing or questioning (fewer words like think, unsure). Using these markers, a language-based unquestioning affiliation index was developed and applied to in-class stream-of-consciousness essays of 2,161 college students (Study 2). Greater levels of unquestioning affiliation expressed in language predicted not only self-reported university identity but also students' likelihood of remaining enrolled in college a year later. In Study 3, the index was applied to naturalistic Reddit conversations of 270,784 people in 2 online communities of supporters of the 2016 presidential candidates-Hillary Clinton and Donald Trump. The index predicted how long people would remain in the group (3a) and revealed temporal shifts mirroring members' joining and leaving of groups (3b). Together, the studies highlight the promise of a language-based approach for tracking and studying group identity processes in online groups.

Large-Scale Social Media Analysis Reveals Emotions Associated with Nonmedical Prescription Drug Use

Background

The behaviors and emotions associated with and reasons for nonmedical prescription drug use (NMPDU) are not well-captured through traditional instruments such as surveys and insurance claims. Publicly available NMPDU-related posts on social media can potentially be leveraged to study these aspects unobtrusively and at scale.

Methods

We applied a machine learning classifier to detect self-reports of NMPDU on Twitter and extracted all public posts of the associated users. We analyzed approximately 137 million posts from 87,718 Twitter users in terms of expressed emotions, sentiments, concerns, and possible reasons for NMPDU via natural language processing.

Results

Users in the NMPDU group express more negative emotions and less positive emotions, more concerns about family, the past, and body, and less concerns related to work, leisure, home, money, religion, health, and achievement compared to a control group (i.e., users who never reported NMPDU). NMPDU posts tend to be highly polarized, indicating potential emotional triggers. Gender-specific analyses show that female users in the NMPDU group express more content related to positive emotions, anticipation, sadness, joy, concerns about family, friends, home, health, and the past, and less about anger than males. The findings are consistent across distinct prescription drug categories (opioids, benzodiazepines, stimulants, and polysubstance).

Conclusion

Our analyses of large-scale data show that substantial differences exist between the texts of the posts from users who self-report NMPDU on Twitter and those who do not, and between males and females who report NMPDU. Our findings can enrich our understanding of NMPDU and the population involved.

Eliciting Expressions of Emotion: An Exploratory Analysis of Alexithymia in Adults with Autism Utilising the APRQ

This study examined alternative methods for detecting alexithymia to the Toronto Alexithymia Scale-20 (TAS-20) by comparing the emotional linguistic performance of ASD and NT samples (n = 32 in each) on the Alexithymia Provoked Responses Questionnaire (APRQ). We utilised both the LIWC and tidytext approaches to linguistic analysis. The results indicate the ASD sample used significantly fewer affective words in response to emotionally stimulating scenarios and had less emotional granularity. Affective word use was correlated with ASD symptomatology but not with TAS-20 scores, suggesting that some elements of alexithymia are not well detected by the TAS-20 alone. The APRQ, in combination with the tidytext package, offers significant potential for sophisticated exploration of emotional expression in ASD.

Proximal Risk for Suicide: A Daily Diary Study Protocol (Preprint)

Background

Suicide is a prevalent public health concern in the United States across all age groups. Research has emphasized the need to identify risk markers that prevent suicide along shorter timeframes, such as days to weeks. Furthermore, little has been done to explore the relative significance of factors that can predict short-term suicide risk or to evaluate how daily variability in these factors impacts suicidal ideation or behavior. This proposed project aims to identify risk factors that best predict near-time changes in suicidal ideation and examine potential interactions between these factors to predict transitions into suicidal thinking or behaviors.

Objective

The aim of this proposed study is threefold: (1) To identify which psychological risk factors are most strongly associated with proximal changes in suicide risk across days and weeks. (2) To evaluate theoretical assumptions of the Integrative-Motivational-Volitional Theory of Suicide. (3) To determine how disruptions in physiological arousal interact with theoretical mechanisms of risk to predict concurrent and short-term prospective increase in suicidal thoughts and behaviors.

Methods

A daily diary or ecological momentary assessment design will be utilized with 200 participants. Participants will complete 2 in-person visits separated by 3 weeks during which they will complete 3 brief daily assessments within their natural environments using the ilumivu research app on a smart device. Research will occur at the Mayo Clinic Health System (MCHS) Eau Claire site. Participants will be recruited through chart review and standard care delivery assessment.

Results

This manuscript outlines the protocol that will guide the conduct of the forthcoming study.

Conclusions

The proposed project aims to lead efforts using technological advances to capture microchanges in suicidal thinking/behavior over shorter timeframes and thereby guide future clinical assessment and management of suicidal patients. Results of this study will generate robust evidence to evaluate which risk factors predict proximal changes in suicidal ideation and behaviors. They will also provide the ability to examine potential interactions with multiple theoretically derived risk factors to predict proximal transitions into worsening suicidal thinking or behaviors. Such information will provide new targets for intervention that could ultimately reduce suicide-related morbidity and mortality.

International Registered Report Identifier (IRRID)

PRR1-10.2196/37583

Psychological trauma and emotional upheaval as revealed in academic writing: The case of COVID-19

The current paper used a preregistered set of language dimensions to indicate how scientists psychologically managed the COVID-19 pandemic and its effects. Study 1 evaluated over 1.8 million preprints from arXiv.org and assessed how papers written during the COVID-19 pandemic reflected patterns of psychological trauma and emotional upheaval compared to those written before the pandemic. The data suggest papers written during the pandemic contained more affect and more cognitive processing terms to indicate writers working through a crisis than papers written before the pandemic. Study 2 (N = 74,744 published PLoS One papers) observed consistent emotion results, though cognitive processing patterns were inconsistent. Papers written specifically about COVID-19 contained more emotion than those not written about COVID-19. Finally, Study 3 (N = 361,189 published papers) replicated the Study 2 emotion results across more diverse journals and observed papers written during the pandemic contained a greater rate of cognitive processing terms, but a lower rate of analytic thinking, than papers written before the pandemic. These data suggest emotional upheavals are associated with psychological correlates reflected in the language of scientists at scale. Implications for psychology of language research and trauma are discussed.

Changes in Cancer Patients' and Caregivers' Disease Perceptions While Receiving Early Palliative Care: A Qualitative and Quantitative Analysis

BACKGROUND

Little is known about the underlying mechanisms through which early palliative care (EPC) improves multiple outcomes in patients with cancer and their caregivers. The aim of this study was to qualitatively and quantitatively analyze patients' and caregivers' thoughts and emotional and cognitive perceptions about the disease prior to and during the EPC intervention, and in the end of life, following the exposure to EPC.

MATERIALS AND METHODS

Seventy-seven patients with advanced cancer and 48 caregivers from two cancer centers participated in semistructured interviews. Their reports were qualitatively and quantitatively analyzed by the means of the grounded theory and a text-analysis program.

RESULTS

Participants reported their past as overwhelmed by unmanaged symptoms, with detrimental physical and psychosocial consequences. The EPC intervention allowed a prompt resolution of symptoms and of their consequences and empowerment, an appreciation of its multidimensional approach, its focus on the person and its environment, and the need for EPC for oncologic populations. Patients reported that conversations with the EPC team increased their acceptance of end of life and their expectation of a painless future. Quantitative analysis revealed higher use of Negative Affects (p < .001) and Biological Processes words (p < .001) when discussing the past; Agency words when discussing the present (p < .001); Positive Affects (p < .001), Optimism (p = .002), and Insight Thinking words (p < .001) when discussing the present and the future; and Anxiety (p = .002) and Sadness words (p = .003) when discussing the future.

CONCLUSION

Overall, participants perceived EPC to be beneficial. Our findings suggest that emotional and cognitive processes centered on communication underlie the benefits experienced by participants on EPC.

IMPLICATIONS FOR PRACTICE

By qualitative and quantitative analyses of the emotional and cognitive perceptions of cancer patients and their caregivers about their experiences before and during EPC interventions, this study may help physicians/nurses to focus on the disease perception by patients/caregivers and the benefits of EPC, as a standard practice. The analysis of words used by patients/caregivers provides a proxy for their psychological condition and support in tailoring an EPC intervention, based on individual needs. This study highlights that the relationship of the triad EPC team/patients/caregivers may rise as a therapeutic tool, allowing increasing awareness and progressive acceptance of the idea of death.

The dynamics of affect across the wake-sleep cycle: From waking mind-wandering to night-time dreaming

Affective experiences occur across the wake-sleep cycle-from active wakefulness to resting wakefulness (i.e., mind-wandering) to sleep (i.e., dreaming). Yet, we know little about the dynamics of affect across these states. We compared the affective ratings of waking, mind-wandering, and dream episodes. Results showed that mind-wandering was more positively valenced than dreaming, and that both mind-wandering and dreaming were more negatively valenced than active wakefulness. We also compared participants' self-ratings of affect with external ratings of affect (i.e., analysis of affect in verbal reports). With self-ratings all episodes were predominated by positive affect. However, the affective valence of reports changed from positively valenced waking reports to affectively balanced mind-wandering reports to negatively valenced dream reports. These findings show that (1) the positivity bias characteristic to waking experiences decreases across the wake-sleep continuum, and (2) conclusions regarding affective experiences depend on whether self-ratings or verbal reports describing these experiences are analysed.

Emotional word use in informal carers of people living with dementia: A linguistic analysis of online discussion forums (Preprint)

Background

Informal dementia care is uniquely stressful and necessitates effective methods of identifying and understanding the needs of potentially at-risk carers so that they can be supported and sustained in their roles. One such method is examining carers’ engagement in online support platforms. Research has explored emotional word use on online discussion forums as a proxy for underlying emotional functioning. We are not aware of any research that has analyzed the content of posts on discussion forums specific to carers of people living with dementia in order to examine their emotional states.

Objective

We addressed the following research questions: (1) To what extent does emotional language use differ between carers of people living with dementia and noncarers? (2) To what extent does emotional language use differ between spousal and parental carers? (3) To what extent does emotional language use differ between current and former carers?

Methods

We used the Linguistic Inquiry and Word Count (LIWC) program to examine emotional word use on a UK-based online forum for informal carers of people living with dementia and a discussion forum control group. Carers were separated into different subgroups for the analysis: current and former, and spousal and parental.

Results

We found that carers of people living with dementia used significantly more negative, but not positive, emotion words than noncarers. Spousal carers used more emotion words overall than parental carers, specifically more negative emotion words. Former carers used more emotional words overall than current carers, specifically more positive words.

Conclusions

The findings suggest that informal carers of people living with dementia may be at increased risk of negative emotional states relative to noncarers. Greater negativity in spousal carers may be explained by increased caregiver burden, whereas greater positivity in former carers may be explained by functional relief of caregiving responsibilities. The theoretical/applied relevance of these findings is discussed.

Natural Language Analysis and the Psychology of Verbal Behavior: The Past, Present, and Future States of the Field

Throughout history, scholars and laypeople alike have believed that our words contain subtle clues about what we are like as people, psychologically speaking. However, the ways in which language has been used to infer psychological processes has seen dramatic shifts over time and, with modern computational technologies and digital data sources, we are on the verge of a massive revolution in language analysis research. In this article, we discuss the past and current states of research at the intersection of language analysis and psychology, summarizing the central successes and shortcomings of psychological text analysis to date. We additionally outline and discuss a critical need for language analysis practitioners in the social sciences to expand their view of verbal behavior. Lastly, we discuss the trajectory of interdisciplinary research on language and the challenges of integrating analysis methods across paradigms, recommending promising future directions for the field along the way.

A dynamic causal model on self-regulation of aversive emotion

Cognitive regulation of emotion has been proven to be effective to take control the emotional responses. Some cognitive models have also been proposed to explain the neural mechanism that underlies this process. However, some characteristics of the models are still unclear, such as whether the cognitive regulation will be spontaneously employed by participants implicitly. The present study recruited the fMRI experiment to focus on the discomfort induced by viewing aversive pictures, and the emotional self-regulation during picture viewing. By using the dynamic causal modeling (DCM), 50 putative models of brain functional networks were constructed, one optimal model that fitted the real data best won the comparison from the candidates. As a result, the optimal model suggests that both the ventral striatum (VS)-centric bottom-up and the dorsolateral prefrontal cortex (DLPFC)-centric top-down regulations are recruited for self-regulation on negative emotions. The DLPFC will exert modulatory influence on the VS only when the VS fails to suppress the induced emotions by self-inhibition.