When thinking about possibility, one can consider both epistemic and deontic principles (i.e., physical possibility and permissibility). Cultural influences may lead individuals to weigh epistemic and deontic obligations differently; developing possibility conceptions are therefore positioned to be affected by cultural surroundings. Across two studies, 251 U.S. and Chinese 4-, 6-, and 8-year-olds sampled from major metropolitan areas in Texas and the Hubei, Sichuan, Gansu, and Guangdong Provinces judged the possibility of impossible, improbable, and ordinary events. Across cultures and ages, children judged ordinary events as possible and impossible events as impossible; cultural differences emerged in developing conceptions of improbable events. Whereas U.S. children became more likely to judge these events possible with age, Chinese children\'s judgments remained consistent with age: Chinese 4- to 8-year-olds judged these events to be possible ∼25% of the time. In Study 2, to test whether this difference was attributable to differential prioritization of epistemic versus deontic constraints, children also judged whether each event was an epistemic violation (i.e., required magic to happen) and a deontic violation (i.e., would result in someone getting in trouble). With age, epistemic judgments were increasingly predictive of possibility judgments for improbable events for U.S. children, and decreasingly so for Chinese children. Contrary to our predictions, deontic judgments were not predictive. We propose that cultural valuation of norms might shape children\'s developing intuitions about possibility. We discuss our findings in light of three accounts of possibility conceptions, suggesting ways to integrate cultural context into each.

UNASSIGNED: Implementing machine learning prediction of negative attitudes towards suicide may improve health outcomes. However, in previous studies, varied forms of negative attitudes were not adequately considered, and developed models lacked rigorous external validation. By analyzing a large-scale social media dataset (Sina Weibo), this paper aims to fully cover varied forms of negative attitudes and develop a classification model for predicting negative attitudes as a whole, and then to externally validate its performance on population and individual levels.
UNASSIGNED: 938,866 Weibo posts with relevant keywords were downloaded, including 737,849 posts updated between 2009 and 2014 (2009-2014 dataset), and 201,017 posts updated between 2015 and 2020 (2015-2020 dataset). (1) For model development, based on 10,000 randomly selected posts from 2009 to 2014 dataset, a human-based content analysis was performed to manually determine labels of each post (non-negative or negative attitudes). Then, a computer-based content analysis was conducted to automatically extract psycholinguistic features from each of the same 10,000 posts. Finally, a classification model for predicting negative attitudes was developed on selected features. (2) For model validation, on the population level, the developed model was implemented on remaining 727,849 posts from 2009 to 2014 dataset, and was externally validated by comparing proportions of negative attitudes between predicted and human-coded results. Besides, on the individual level, similar analyses were performed on 300 randomly selected posts from 2015 to 2020 dataset, and the developed model was externally validated by comparing labels of each post between predicted and actual results.
UNASSIGNED: For model development, the F1 and area under ROC curve (AUC) values reached 0.93 and 0.97. For model validation, on the population level, significant differences but very small effect sizes were observed for the whole sample (χ 2 1 = 32.35, p < 0.001; Cramer\'s V = 0.007, p < 0.001), men (χ 2 1 = 9.48, p = 0.002; Cramer\'s V = 0.005, p = 0.002), and women (χ 2 1 = 25.34, p < 0.001; Cramer\'s V = 0.009, p < 0.001). Besides, on the individual level, the F1 and AUC values reached 0.76 and 0.74.
UNASSIGNED: This study demonstrates the efficiency and necessity of machine learning prediction of negative attitudes as a whole, and confirms that external validation is essential before implementing prediction models into practice.

BACKGROUND: Commonly offered as supportive care, therapist-led online support groups (OSGs) are a cost-effective way to provide support to individuals affected by cancer. One important indicator of a successful OSG session is group cohesion; however, monitoring group cohesion can be challenging due to the lack of nonverbal cues and in-person interactions in text-based OSGs. The Artificial Intelligence-based Co-Facilitator (AICF) was designed to contextually identify therapeutic outcomes from conversations and produce real-time analytics.
OBJECTIVE: The aim of this study was to develop a method to train and evaluate AICF\'s capacity to monitor group cohesion.
METHODS: AICF used a text classification approach to extract the mentions of group cohesion within conversations. A sample of data was annotated by human scorers, which was used as the training data to build the classification model. The annotations were further supported by finding contextually similar group cohesion expressions using word embedding models as well. AICF performance was also compared against the natural language processing software Linguistic Inquiry Word Count (LIWC).
RESULTS: AICF was trained on 80,000 messages obtained from Cancer Chat Canada. We tested AICF on 34,048 messages. Human experts scored 6797 (20%) of the messages to evaluate the ability of AICF to classify group cohesion. Results showed that machine learning algorithms combined with human input could detect group cohesion, a clinically meaningful indicator of effective OSGs. After retraining with human input, AICF reached an F1-score of 0.82. AICF performed slightly better at identifying group cohesion compared to LIWC.
CONCLUSIONS: AICF has the potential to assist therapists by detecting discord in the group amenable to real-time intervention. Overall, AICF presents a unique opportunity to strengthen patient-centered care in web-based settings by attending to individual needs.
UNASSIGNED: RR2-10.2196/21453.

BACKGROUND: Digital phenotyping and monitoring tools are the most promising approaches to automatically detect upcoming depressive episodes. Especially, linguistic style has been seen as a potential behavioral marker of depression, as cross-sectional studies showed, for example, less frequent use of positive emotion words, intensified use of negative emotion words, and more self-references in patients with depression compared to healthy controls. However, longitudinal studies are sparse and therefore it remains unclear whether within-person fluctuations in depression severity are associated with individuals\' linguistic style.
METHODS: To capture affective states and concomitant speech samples longitudinally, we used an ambulatory assessment approach sampling multiple times a day via smartphones in patients diagnosed with depressive disorder undergoing sleep deprivation therapy. This intervention promises a rapid change of affective symptoms within a short period of time, assuring sufficient variability in depressive symptoms. We extracted word categories from the transcribed speech samples using the Linguistic Inquiry and Word Count.
RESULTS: Our analyses revealed that more pleasant affective momentary states (lower reported depression severity, lower negative affective state, higher positive affective state, (positive) valence, energetic arousal and calmness) are mirrored in the use of less negative emotion words and more positive emotion words.
CONCLUSIONS: We conclude that a patient\'s linguistic style, especially the use of positive and negative emotion words, is associated with self-reported affective states and thus is a promising feature for speech-based automated monitoring and prediction of upcoming episodes, ultimately leading to better patient care.

This preregistered study replicates and extends studies concerning emotional response to wartime rally speeches and applies it to U.S. President Donald Trump\'s first national address regarding the COVID-19 pandemic on March 11, 2020. We experimentally test the effect of a micro-expression (ME) by Trump associated with appraised threat on change in participant self-reported distress, sadness, anger, affinity, and reassurance while controlling for followership. We find that polarization is perpetuated in emotional response to the address which focused on portraying the COVID-19 threat as being of Chinese provenance. We also find a significant, albeit slight, effect by Trump\'s ME on self-reported sadness, suggesting that this facial behavior served did not diminish his speech, instead serving as a form of nonverbal punctuation. Further exploration of participant response using the Linguistic Inventory and Word Count software reinforces and extends these findings.

For the longest time, the gold standard in preparing spoken language corpora for text analysis in psychology was using human transcription. However, such standard comes at extensive cost, and creates barriers to quantitative spoken language analysis that recent advances in speech-to-text technology could address. The current study quantifies the accuracy of AI-generated transcripts compared to human-corrected transcripts across younger (n = 100) and older (n = 92) adults and two spoken language tasks. Further, it evaluates the validity of Linguistic Inquiry and Word Count (LIWC)-features extracted from these two kinds of transcripts, as well as transcripts specifically prepared for LIWC analyses via tagging. We find that overall, AI-generated transcripts are highly accurate with a word error rate of 2.50% to 3.36%, albeit being slightly less accurate for younger compared to older adults. LIWC features extracted from either transcripts are highly correlated, while the tagging procedure significantly alters filler word categories. Based on these results, automatic speech-to-text appears to be ready for psychological language research when using spoken language tasks in relatively quiet environments, unless filler words are of interest to researchers.

BACKGROUND: Challenging encounters in health care professions, including in dentistry, are relatively common. Challenging encounters can be defined as stressful or emotional situations involving patients that could impact both treatment outcomes and patients\' experiences. Through written web-based reviews, patients can share their experiences with health care providers, and these posts can be a useful source for investigating patient satisfaction and their experiences of challenging encounters.
OBJECTIVE: This study aims to identify dominant themes from patient-written, web-based reviews of dentists and investigate how these themes are related to patient satisfaction with dental treatment.
METHODS: The study data consisted of 11,764 reviews written by dental patients, which included 1- to 5-star ratings on overall satisfaction and free-text comments. The free-text comments were analyzed using Linguistic Inquiry and Word Count software, and the meaning extraction method was used to group words into thematic categories. These themes were used as variables in a multilevel logistic regression analysis to predict patient satisfaction.
RESULTS: Eight themes emerged from the analyses, of which 6 (75%)-explanation (odds ratio [OR] 2.56, 95% CI 2.16-3.04; P<.001), assurance (OR 3.61, 95% CI 2.57-5.06; P<.001), performance assessment (OR 2.17, 95% CI 1.84-2.55; P<.001), professional advice (OR 1.81, 95% CI 1.55-2.13; P<.001), facilities (OR 1.78, 95% CI 1.08-2.91; P=.02), and recommendation (OR 1.31, 95% CI 1.12-1.53; P<.001)-increased the odds of high patient satisfaction. The remaining themes (2/8, 25%)-consequences of treatment need (OR 0.24, 95% CI 0.20-0.29; P<.001) and patient-centered care (OR 0.62, 95% CI 0.52-0.74; P<.001)-reduced the odds of high patient satisfaction.
CONCLUSIONS: The meaning extraction method is an interesting approach to explore patients\' written accounts of encounters with dental health professionals. The experiences described by patients provide insight into key elements related to patient satisfaction that can be used in the education of dental health professionals and to improve the provision of dental health services.

The rise in global temperatures due to climate change has escalated the frequency and intensity of wildfires worldwide. Beyond their direct impact on physical health, these wildfires can significantly impact mental health. Conventional mental health studies predominantly rely on surveys, often constrained by limited sample sizes, high costs, and time constraints. As a result, there is an increasing interest in accessing social media data to study the effects of wildfires on mental health.
In this study, we focused on Twitter users affected by the California Tubbs Fire in 2017 to extract data signals related to emotional well-being and mental health. Our analysis aimed to investigate tweets posted during the Tubbs Fire disaster to gain deeper insights into their impact on individuals. Data were collected from October 8 to October 31, 2017, encompassing the peak activity period. Various analytical methods were employed to explore word usage, sentiment, temporal patterns of word occurrence, and emerging topics associated with the unfolding crisis.
The findings show increased user engagement on wildfire-related Tweets, particularly during nighttime and early morning, especially at the onset of wildfire incidents. Subsequent exploration of emotional categories using Linguistic Inquiry and Word Count (LIWC) revealed a substantial presence of negative emotions at 43.0%, juxtaposed with simultaneous positivity in 23.1% of tweets. This dual emotional expression suggests a nuanced and complex landscape, unveiling concerns and community support within conversations. Stress concerns were notably expressed in 36.3% of the tweets. The main discussion topics were air quality, emotional exhaustion, and criticism of the president\'s response to the wildfire emergency.
Social media data, particularly the data collected from Twitter during wildfires, provides an opportunity to evaluate the psychological impact on affected communities immediately. This data can be used by public health authorities to launch targeted media campaigns in areas and hours where users are more active. Such campaigns can raise awareness about mental health during disasters and connect individuals with relevant resources. The effectiveness of these campaigns can be enhanced by tailoring outreach efforts based on prevalent issues highlighted by users. This ensures that individuals receive prompt support and mitigates the psychological impacts of wildfire disasters.

Individuals suffering from social anxiety disorder (SAD) are increasingly turning to online support communities for self-disclosure and social support. Despite the extensive body of research on online mental health discourses, the cultural nuances within SAD-related discussions remain underexplored. In this study, we examine the cultural differences in online expression of social anxiety by analyzing individuals\' self-disclosure and support-seeking behaviors in social media posts. Using two-week data (n = 1,681) from two SAD support communities on the Reddit and Douban groups, we used both qualitative thematic analysis and quantitative semantic analysis to discern prevalent themes and linguistic attributes characterizing these online expressions. Our findings not only uncover common themes such as sharing personal experiences and seeking mutual validations in both communities but also identify their divergences, as Western users primarily sought advice and information in posts, whereas Chinese users were more inclined toward networking. Cultural variations in language use were evident, particularly in individuals\' affect and their expression of personal and social concerns. Western users were more likely to convey negative emotions and delve into personal matters related to SAD, whereas Chinese users tended to grapple more with workplace anxieties. This study contributes to the cultural understanding of online mental health discourses and offers insights for crafting culturally sensitive interventions and supports for people with SAD.

UNASSIGNED: The identification of language markers, referring to both form and content, for common mental health disorders such as major depressive disorder (MDD), can facilitate the development of innovative tools for early recognition and prevention. However, studies in this direction are only at the beginning and are difficult to implement due to linguistic variability and the influence of cultural contexts.
UNASSIGNED: This study aims to identify language markers specific to MDD through an automated analysis process based on RO-2015 LIWC (Linguistic Inquiry and Word Count).
UNASSIGNED: A sample of 62 medicated patients with MDD and a sample of 43 controls were assessed. Each participant provided language samples that described something that was pleasant for them.
UNASSIGNED: (1) Screening tests for MDD (MADRS and DASS-21); (2) Ro-LIWC2015 - Linguistic Inquiry and Word Count - a computerized text analysis software, validated for Romanian Language, that analyzes morphology, syntax and semantics of word use.
UNASSIGNED: Depressive patients use different approaches in sentence structure, and communicate in short sentences. This requires multiple use of the punctuation mark period, which implicitly requires directive communication, limited in exchange of ideas. Also, participants from the sample with depression mostly use impersonal pronouns, first person pronoun in plural form - not singular, a limited number of prepositions and an increased number of conjunctions, auxiliary verbs, negations, verbs in the past tense, and much less in the present tense, increased use of words expressing negative affects, anxiety, with limited use of words indicating positive affects. The favorite topics of interest of patients with depression are leisure, time and money.
UNASSIGNED: Depressive patients use a significantly different language pattern than people without mood or behavioral disorders, both in form and content. These differences are sometimes associated with years of education and sex, and might also be explained by cultural differences.