First-person Singular Pronouns Research Articles

Using natural language processing to identify patterns associated with depression, anxiety, and stress symptoms during the COVID-19 pandemic.

Combining data-driven natural language processing techniques with traditional methods using predefined word lists may offer greater insights into the connections between language patterns and depression and anxiety symptoms, particularly within specific stressful contexts. Between 2020 and 2021, 1106 participants wrote narrative responses describing their experiences during the COVID-19 pandemic and completed the Depression Anxiety Stress Scale-21 (DASS). We investigated language patterns associated with DASS symptoms using established categories from Linguistic Inquiry and Word Count (LIWC) and sentiment analysis, as well as exploratory natural language processing techniques. Finally, we constructed machine learning regression models in order to assess how much of the variance in DASS symptoms is related to language use. We found significant positive bivariate correlations between total DASS symptoms and hypothesized LIWC categories: first-person singular pronouns, absolute language, and negative emotion words. These results remained largely similar when using negative sentiment scores and when statistically controlling for gender, age, and education. Exploratory n-gram analyses also revealed new individual words and phrases correlated with total DASS symptoms. Lastly, our regression models demonstrated a significant association between language use and total DASS symptoms (R2 = 0.36-0.62). The current study is one of the first to examine associations between language use and DASS symptoms during the pandemic using both traditional and data-driven techniques. These results replicate and extend prior findings regarding negative emotion and absolute language and identify unique correlates of DASS symptoms during pandemic-related stress, contributing to the literature on language and mental health more broadly.

Characterizing the patient experience of physical restraint in psychiatric settings via a linguistic, sentiment, and metaphor analysis

Physical Restraint (PR) is a coercive procedure used in emergency psychiatric care to ensure safety in life-threatening situations. Because of its traumatic nature, studies emphasize the importance of considering the patient’s subjective experience. We pursued this aim by overcoming classic qualitative approaches and innovatively applying a multilayered semiautomated language analysis to a corpus of narratives about PR collected from 99 individuals across seven mental health services in Italy. Compared to a reference corpus, PR narratives were characterized by reduced fluency and lexical density, yet a greater use of emotional and cognitive terms, verbs, and first-person singular pronouns. Sadness was the most represented emotion, followed by anger and fear. One-third of the PR narratives contained at least one metaphor, with Animals and War/Prison as the most distinctive source domains. The quality and length of the PR experience impacted both the structure and the sentiment of the narratives. Findings confirm the distressful nature of PR but also point to the use of various linguistic mechanisms which might serve as an early adaptive response toward healing from the traumatic experience. Overall, the study highlights the importance of Natural Language Processing as an unobtrusive window into subjective experience, offering insights for therapeutic choices.

Written Narrative in Patients with Different Mental Disorders

Language disorders are criteria of psychopathology diagnostics listed both in the 10th revision of International Statistical Classification of Diseases and Related Health Problems (ICD-10) and the Diagnostic and Statistic Manual of Mental Disorders (DSM). However, structure and grammar remain understudied in this aspect, which makes structural analysis a relevant research topic. A structural analysis of written narrative may objectivize particular language disorders, thus facilitating psychodiagnostics of different mental disorders. The research objective was to describe the structural features of written narratives obtained from 127 patients with schizophrenic, bipolar, and personality disorders. The narratives were subjected to structural and statistical analyses. Schizophrenic narratives demonstrated a poor vocabulary, a low volume, and a high degree of formality with a passive narrator. Bipolar texts tended to be long, detailed, egocentric, and active, with multiple participles. Patients with personality disorders emotionally relived their memories, producing reflective written narratives with a lot of actors. The final list of written speech indicators to be used to distinguish between schizophrenic, bipolar, and personality disorders included such variables as text volume, verb tense, non-finite verb forms, first-person singular pronoun, number of actors, passive / active narrator, and laconic / detailed narration.

Part-of-Speech Features in Bob Dylan's Song Lyrics: A Stylometric Analysis

Honoured as a Nobel Laureate in 2016, Bob Dylan's song lyrics have garnered well-deserved recognition and appreciation for their themes, content and artistic performances. Part-of-speech characteristics are effective in denoting stylistic features of texts in stylometric studies. The present study carried out a quantitative observation of stylistic features of Dylan's lyrics. Specifically, the study focuses on parts of speech like verbs, adjectives, adverbs, nouns and pronouns. Results of the present study reveal that: (1) Based on the distribution of verbs and adjectives, Dylan's lyrics are significantly active texts, and the activity sequences (Q-sequences) have validated the result in a dynamic way; (2) Dylan's lyrics tend to present the characteristics of a written body; (3) Individualism is prominent in Dylan's lyrics accompanied by the wide use of the first-person singular pronouns.

Actualization of Axiolemmas in Political Discourse of Russia and China (New Year Addresses of V. V. Putin and Xi Jinping)

This article presents the findings of a comparative study on the values articulated in the New Year addresses of the political leaders of Russia and China. The research addresses the challenge of identifying the determinants that influence their choices within epideictic texts. An analysis of the New Year addresses delivered by Vladimir Putin and Xi Jinping from 2019 to 2023, approached from a linguo-axiological perspective, has highlighted the concepts of PEOPLE and GOVERNMENT, indicating the significance of themes related to the populace and representatives of authority in the political discourses of both countries. It is demonstrated that these congratulatory speeches actualize vital, political, national-cultural, moral-ethical, economic, and ecological concepts. Among these, the Russian President’s addresses place the greatest emphasis on the representation of moral-ethical concepts, while the Chinese leader’s addresses provide a more detailed presentation of political concepts. It has been revealed that in the speeches of the Chinese political leader, the concept of GOVERNMENT is verbalized through references to the country’s political party and through the expression of personal sentiments using the first-person singular pronoun. The results of this study have identified both objective and subjective factors that determine the selection of value dominants reflected in the New Year addresses of the political leaders of Russia and China.

Using natural language processing approaches to characterize professional experiences of child welfare workers

High turnover rates among child welfare workers, including caseworkers and supervisors, are associated with negative attitudes and perceptions about work (NAPW). Our study aimed to (a) explore how child welfare workers’ demographic characteristics, professional attributes, and their work narratives are related to NAPW and (b) extract themes from these narratives to understand their professional needs and concerns. In 2022, a statewide online survey was administered to active child welfare caseworkers and supervisors in a large Midwestern state. This survey, combining standardized scales and an open-ended question about work experiences, yielded numeric (N = 551 survey respondents) and text data (N = 2,193 words). We analyzed the numeric data using general linear modeling and the text data using natural language processing (NLP) techniques. Results showed that workers associated with higher NAPW were female, younger, from racial and ethnic minority backgrounds, less tenured, and more likely to be employed in public agencies and to work longer hours. They are predominantly caseworkers rather than supervisors. Worker role and agency affiliation had the largest effect sizes. NLP analysis showed that workers with more NAPW frequently used third-person pronouns and emotionally negative language, suggesting a disconnection from colleagues and job dissatisfaction. Caseworkers often used first-person singular pronouns, indicating feelings of isolation. Two key themes emerged: a) the need for better agency support during home visits and b) concerns over traumatic experiences and ineffective trauma-informed practices. The findings suggest that future interventions should prioritize the well-being of caseworkers in public agencies and consider language patterns in workers’ narratives to understand their vulnerability to NAPW.

Detecting suicide risk among U.S. servicemembers and veterans: a deep learning approach using social media data.

Military Servicemembers and Veterans are at elevated risk for suicide, but rarely self-identify to their leaders or clinicians regarding their experience of suicidal thoughts. We developed an algorithm to identify posts containing suicide-related content on a military-specific social media platform. Publicly-shared social media posts (n = 8449) from a military-specific social media platform were reviewed and labeled by our team for the presence/absence of suicidal thoughts and behaviors and used to train several machine learning models to identify such posts. The best performing model was a deep learning (RoBERTa) model that incorporated post text and metadata and detected the presence of suicidal posts with relatively high sensitivity (0.85), specificity (0.96), precision (0.64), F1 score (0.73), and an area under the precision-recall curve of 0.84. Compared to non-suicidal posts, suicidal posts were more likely to contain explicit mentions of suicide, descriptions of risk factors (e.g. depression, PTSD) and help-seeking, and first-person singular pronouns. Our results demonstrate the feasibility and potential promise of using social media posts to identify at-risk Servicemembers and Veterans. Future work will use this approach to deliver targeted interventions to social media users at risk for suicide.

A history of PTSD changes the way people express themselves during the COVID-19 pandemic

ABSTRACT Background: The words people use in everyday life tell us about their emotions, their mental state and allow us to understand how people process and interpret an event. Previous research has established a link between the content analysis of narrative texts and the psychopathology of people who have experienced trauma. Objectives: This study examines whether the development of PTSD following exposure to a previous traumatic event alters the way people express themselves in the context of an anxiety-provoking event, the COVID-19 pandemic. Methods: This study is based on semi-structured interviews conducted during the first lockdown period in France (23 April–16 May 2020) with people exposed to the 13 November 2015 attacks (N = 31) and nonexposed people (N = 57). Results: People with PTSD had longer narratives and used more first-person singular pronouns, lower first-person plural pronouns, more words related to negative emotions and anxiety compared to the nonexposed group. Within the PTSD group, there was no significant difference between the use of words related to the attacks and the pandemic. Conversely, the nonexposed group used more words related to the COVID-19 pandemic compared to words related to the attacks. Conclusion: These results confirm, as have other studies, that a history of PTSD can specifically modify the style and narrative of past experiences. They underline the importance of including linguistic analyses in psychological assessments of PTSD.

Computational analysis of linguistic features in speech samples of first-episode bipolar disorder and psychosis

BackgroundIn recent years, automated analyses using novel NLP methods have been used to investigate language abnormalities in schizophrenia. In contrast, only a few studies used automated language analyses in bipolar disorder. To our knowledge, no previous research compared automated language characteristics of first-episode psychosis (FEP) and bipolar disorder (FEBD) using NLP methods. MethodsOur study included 53 FEP, 40 FEBD and 50 healthy control participants who are native Turkish speakers. Speech samples of the participants in the Thematic Apperception Test (TAT) underwent automated generic and part-of-speech analyses, as well as sentence-level semantic similarity analysis based on SBERT. ResultsBoth FEBD and FEP were associated with the use of shorter sentences and increased sentence-level semantic similarity but less semantic alignment with the TAT pictures. FEP also demonstrated reduced verbosity and syntactic complexity. FEP differed from FEBD in reduced verbosity, decreased first-person singular pronouns, fewer conjunctions, increased semantic similarity as well as shorter sentence and word length. The mean classification accuracy was 82.45 % in FEP vs HC, 71.1 % in FEBD vs HC, and 73 % in FEP vs FEBD. After Bonferroni correction, the severity of negative symptoms in FEP was associated with reduced verbal output and increased 5th percentile of semantic similarity. LimitationsThe main limitation of this study was the cross-sectional nature. ConclusionOur findings demonstrate that both patient groups showed language abnormalities, which were more severe and widespread in FEP compared to FEBD. Our results suggest that NLP methods reveal transdiagnostic linguistic abnormalities in FEP and FEBD.

Exploring barriers to care home research recruitment during the COVID-19 pandemic: The influence of social media recruitment posts and public sentiment.

Recruitment of care home staff to research studies is recognised as challenging. This was further exacerbated by the COVID-19 pandemic and the associated negative media portrayal of care home workers. Social media use has surged since the onset of COVID-19 lockdowns, offering a plausible approach to understanding the barriers to care home research recruitment and gaining insight into public perceptions of care home workers. To utilise comments from two Facebook recruitment posts to: 1) gain an understanding of potential barriers to recruitment of healthcare workers (HCWs) in UK care homes, and 2) explore public sentiment towards care home research and care homes in the context of the COVID-19 pandemic. This cross-sectional study analysed comments from two Facebook posts (available June-October 2021) advertising a separate study on psychological support for care staff during the pandemic. This study was situated within a larger investigation into the mental health and wellbeing of care home staff and employed both qualitative analysis and quantitative methods (word count and correlations between words used and between posts). Three themes were identified from the qualitative analysis: support, mistrust and blame. There was a greater use of words associated with support and negative emotive words in post 2. Post 2 comments featured significantly more choice words and first-person singular pronouns than post 1 which indicated a resentful sentiment from those who advocate freedom of choice and control. Discussion of mistrust towards researchers was most prominent in post 1 indicating the importance of relationship building between researchers and HCWs in UK care homes. With attribution to blame, there was a larger range of negative emotion words than positive emotion words. Taken together our findings offer novel insights into why recruitment to care home research during the pandemic including the use of social media might be problematic. Social media is a useful tool for recruitment but should not be considered as a one-time input. Researchers should pro-actively engage with the study population from the start using co-design with resident and public groups to support recruitment and ensure these populations are accurately represented within research.

Investigating Health and Well-Being Challenges Faced by an Aging Workforce in the Construction and Nursing Industries: Computational Linguistic Analysis of Twitter Data.

Construction and nursing are critical industries. Although both careers involve physically and mentally demanding work, the risks to workers during the COVID-19 pandemic are not well understood. Nurses (both younger and older) are more likely to experience the ill effects of burnout and stress than construction workers, likely due to accelerated work demands and increased pressure on nurses during the COVID-19 pandemic. In this study, we analyzed a large social media data set using advanced natural language processing techniques to explore indicators of the mental status of workers across both industries before and during the COVID-19 pandemic. This social media analysis aims to fill a knowledge gap by comparing the tweets of younger and older construction workers and nurses to obtain insights into any potential risks to their mental health due to work health and safety issues. We analyzed 1,505,638 tweets published on Twitter (subsequently rebranded as X) by younger and older (aged <45 vs >45 years) construction workers and nurses. The study period spanned 54 months, from January 2018 to June 2022, which equates to approximately 27 months before and 27 months after the World Health Organization declared COVID-19 a global pandemic on March 11, 2020. The tweets were analyzed using big data analytics and computational linguistic analyses. Text analyses revealed that nurses made greater use of hashtags and keywords (both monograms and bigrams) associated with burnout, health issues, and mental health compared to construction workers. The COVID-19 pandemic had a pronounced effect on nurses' tweets, and this was especially noticeable in younger nurses. Tweets about health and well-being contained more first-person singular pronouns and affect words, and health-related tweets contained more affect words. Sentiment analyses revealed that, overall, nurses had a higher proportion of positive sentiment in their tweets than construction workers. However, this changed markedly during the COVID-19 pandemic. Since early 2020, sentiment switched, and negative sentiment dominated the tweets of nurses. No such crossover was observed in the tweets of construction workers. The social media analysis revealed that younger nurses had language use patterns consistent with someone experiencing the ill effects of burnout and stress. Older construction workers had more negative sentiments than younger workers, who were more focused on communicating about social and recreational activities rather than work matters. More broadly, these findings demonstrate the utility of large data sets enabled by social media to understand the well-being of target populations, especially during times of rapid societal change.

Linguistic Analysis of Nonsuicidal Self-Injury Reddit Posts: Implications for Family Therapy

Nonsuicidal self-injury (NSSI) is a growing phenomenon, yet there is still a limited understanding of the behavior, intent behind the behavior and what individuals themselves say about their behavior. This study collected pro-NSSI public blog posts from Reddit and analyzed the content linguistically using Linguistic Inquiry and Word Count software, to examine the use of NSSI specific words, linguistic and psychological linguistic properties. The results inform current mental health practices by dispelling myths and providing insight into the inner world of people who engage in NSSI. The linguistic properties found in the analysis reflected the predicted results; authors of pro-NSSI posts used first-person singular pronouns extensively, indicating high levels of mental health distress and isolation. This study demonstrates how understanding the language of self-harm can help inform mental health treatment using family therapy.

Use of Language By generative AI Tools in Mathematical Problem Solving: The Case of ChatGPT

Texts generated by artificial intelligence agents have been suggested as tools supporting students’ learning. The present research analyses the language of texts generated by ChatGPT when solving mathematical problems related to the quadratic equation. We use the functional grammar theoretical framework that includes three meta-functions: the ideational meta-function, the interpersonal meta-function and the textual meta-function. The results indicated that in at least one of six problem-solving tasks ChatGPT provided a mathematically incorrect answer. The processes appearing in ChatGPT texts, aiming at developing students’ understanding of mathematical concepts, included verbal, mental, existential, relational and behavioural processes but no material processes. Specifically, ChatGPT performed a mathematically incorrect existential process. ChatGPT generally used the first plural pronoun ‘we’ when describing the processes of solving mathematical problems, while it generally used the first-person singular pronoun when taking responsibility for a specific mistake or when expressing happiness for the actions of the user. Moreover, generally the text of the solution did not include direct imperatives but used ‘let us do’. The advancement of the ChatGPT textual solution was made usually through steps like ‘first’, ‘second’, etc. The research results indicated that the way ChatGPT responded to the mathematical problems would be useful in supporting learners’ understanding of ways to solve quadratic equations, but only if the teacher critically accompanies the student in the problem-solving process. Self-study with ChatGPT could lead to or confirm students’ mathematical misconceptions.

"Nolite te bastardes carborundorum": Corpus Stylistics and Language of Margaret Atwood's The Handmaid's Tale

This paper investigates the stylistic and linguistic characteristics of Margaret Atwood's The Handmaid's Tale, a novel of Canadian origin. Employing a corpus-based approach, the researcher aims to analyze its stylistic features in-depth, focusing on specific predetermined categories. Quantitative analysis was facilitated through WordSmith 8.0 software, enabling the identification of keywords in context, which serve as indicators of the linguistic style within the selected corpus. Results indicate that the novel employs simple language and concise sentence structures. Moreover, it demonstrates lexical diversity, encompassing various themes with a higher type-token ratio (TTR), indicative of a diverse vocabulary. Notably, there is a notable prevalence of first-person singular pronouns throughout the text. Regarding content word distribution, verbs are the most frequently utilized, followed by nouns, pronouns, adverbs, and adjectives—additionally, the analysis of keywords aids in uncovering stylistic and linguistic elements embedded within the text.

Notes from the Underground: Seeking the top personality correlates of self-referencing.

Self-focused language use has been frequently assumed to reflect narcissism; however, research indicates that the association between first-person singular pronouns (i.e., "I-talk") and grandiose narcissism is negligible. To extend this literature, we progressively identify vulnerable narcissism and rumination as positive correlates of I-talk in five studies (valid Ns = 211, 475, 1253, 289, 1113). The first study revealed positive correlates of I-talk suggestive of vulnerable narcissism. The second study showed more directly that vulnerable narcissism was a positive correlate but that this association was attributable to shared variance with neuroticism. The third study, a preregistered effort, replicated and extended the results of the second study. The fourth and fifth studies focused on rumination in a preregistered manner. All the studies point to a clear distinction: While grandiose narcissism is negligibly related to I-talk, vulnerable narcissism is positively related to I-talk; moreover, rumination is a robust predictor of I-talk. A research synthesis revealed the following constructs significantly capture I-talk: depression (r = 0.10), neuroticism (r = 0.15), rumination (r = 0.14), and vulnerable narcissism (r = 0.12). The association between I-talk and neuroticism was partially mediated by rumination, providing a testable candidate mechanism for neuroticism interventions.

Linking Digital Traits from Facial Expression, Voice, and Head Motion to Montgomery–Åsberg Depression Rating Scale Subscales

IntroductionThe 10-item Montgomery–Åsberg Depression Rating Scale (MADRS) measures different dimensions of depression symptomatology. Digital traits may generate deeper understanding of the MADRS subscales and provide insights about depression symptomatology.ObjectivesTo identify digital traits that predict specific MADRS subscales and ascertain which digital traits are important for which MADRS subscales.Methods During a Phase II decentralised clinical trial in major depressive disorder (MDD), patients completed the MADRS and used AiCure (LLC, New York, NY, USA), a smartphone application, to complete image description tasks at baseline. Digital measurements identified from the literature as relevant to MDD symptomatology were conducted using audio and video data derived from the image description tasks. Digital measurements included speech (rate, sentiment and first-person singular pronouns), vocal acoustics (intensity, pause fraction and fundamental frequency), facial expressivity (regional facial movement) and head pose (Euclidean and angular head movement). Digital traits analysis involved data pre-processing followed by machine learning (ML) using Elastic Net, Decision Tree, and Random Forest models; model performance was evaluated using 5-fold cross-validation and mean absolute error (MAE). Important digital traits were calculated by percentage change in MAE after permuting a specific variable. Important digital traits for the MADRS Apparent Sadness subscale score were mapped to defined, interpretable domains.ResultsThe ML model predictions varied for different MADRS subscales (Table). Overall, Elastic Net and Random Forest models outperformed Decision Tree across all subscales scores other than suicidal thoughts. Half of the literature-based digital traits contributed to the prediction of ≥1 MADRS sadness sub-scale score. The important digital traits for the Apparent Sadness subscale score could be mapped to 4 domains (Figure); this aligned with findings from the literature.Image:Image 2:ConclusionsDigital traits collected from patients with MDD were able to predict certain MADRS subscales better than others.FundingBoehringer Ingelheim.Disclosure of InterestZ. Zhu Employee of: Boehringer Ingelheim Pharmaceuticals, Inc., Y. Wu Employee of: Boehringer Ingelheim Pharmaceuticals, Inc., J. Seidel Employee of: Boehringer Ingelheim International GmbH, D. Roy Employee of: Boehringer Ingelheim Pharmaceuticals, Inc., E. Salzmann Employee of: Boehringer Ingelheim International GmbH

Exploring the Role of First-Person Singular Pronouns in Detecting Suicidal Ideation: A Machine Learning Analysis of Clinical Transcripts.

Linguistic features, particularly the use of first-person singular pronouns (FPSPs), have been identified as potential indicators of suicidal ideation. Machine learning (ML) and natural language processing (NLP) have shown potential in suicide detection, but their clinical applicability remains underexplored. This study aimed to identify linguistic features associated with suicidal ideation and develop ML models for detection. NLP techniques were applied to clinical interview transcripts (n = 319) to extract relevant features, including four cases of FPSP (subjective, objective, dative, and possessive cases) and first-person plural pronouns (FPPPs). Logistic regression analyses were conducted for each linguistic feature, controlling for age, gender, and depression. Gradient boosting, support vector machine, random forest, decision tree, and logistic regression were trained and evaluated. Results indicated that all four cases of FPSPs were associated with depression (p < 0.05) but only the use of objective FPSPs was significantly associated with suicidal ideation (p = 0.02). Logistic regression and support vector machine models successfully detected suicidal ideation, achieving an area under the curve (AUC) of 0.57 (p < 0.05). In conclusion, FPSPs identified during clinical interviews might be a promising indicator of suicidal ideation in Chinese patients. ML algorithms might have the potential to aid clinicians in improving the detection of suicidal ideation in clinical settings.

Natural language processing for defining linguistic features in schizophrenia: A sample from Turkish speakers

Natural language processing (NLP) provides fast and accurate extraction of features related to the language of schizophrenia. We utilized NLP methods to test the hypothesis that schizophrenia is associated with altered linguistic features in Turkish, a non-Indo-European language, compared to controls. We also explored whether these possible altered linguistic features were language-dependent or -independent. We extracted and compared speech in schizophrenia (SZ, N = 38) and healthy well-matched control (HC, N = 38) participants using NLP. The analysis was conducted in two parts. In the first one, mean sentence length, total completed words, moving average type-token ratio to measure the lexical diversity, and first-person singular pronoun usage were calculated. In the second one, we used parts-of-speech tagging (POS) and Word2Vec in schizophrenia and control. We found that SZ had lower mean sentence length and moving average type-token ratio but higher use of first-person singular pronoun. All these significant results were correlated with the Thought and Language Disorder Scale score. The POS approach demonstrated that SZ used fewer coordinating conjunctions. Our methodology using Word2Vec detected that SZ had higher semantic similarity than HC and K-Means could differentiate between SZ and HC into two distinct groups with high accuracy, 86.84 %. Our findings showed that altered linguistic features in SZ are mostly language-independent. They are promising to describe language patterns in schizophrenia which proposes that NLP measurements may allow for rapid and objective measurements of linguistic features.

Linguistic Variables and Gender Differences Within a Messenger-Based Psychosocial Chat Counseling Service for Children and Adolescents: Cross-Sectional Study.

Text messaging is widely used by young people for communicating and seeking mental health support through chat-based helplines. However, written communication lacks nonverbal cues, and language usage is an important source of information about a person's mental health state and is known to be a marker for psychopathology. The aim of the study was to investigate language usage, and its gender differences and associations with the presence of psychiatric symptoms within a chat counseling service for adolescents and young adults. For this study, the anonymized chat content of a German messenger-based psychosocial chat counseling service for children and adolescents ("krisenchat") between May 2020 and July 2021 was analyzed. In total, 661,131 messages from 6962 users were evaluated using Linguistic Inquiry and Word Count, considering the following linguistic variables: first-person singular and plural pronouns, negations, positive and negative emotion words, insight words, and causation words. Descriptive analyses were performed, and gender differences of those variables were evaluated. Finally, a binary logistic regression analysis examined the predictive value of linguistic variables on the presence of psychiatric symptoms. Across all analyzed chats, first-person singular pronouns were used most frequently (965,542/8,328,309, 11.6%), followed by positive emotion words (408,087/8,328,309, 4.9%), insight words (341,460/8,328,309, 4.1%), negations (316,475/8,328,309, 3.8%), negative emotion words (266,505/8,328,309, 3.2%), causation words (241,520/8,328,309, 2.9%), and first-person plural pronouns (499,698/8,328,309, 0.6%). Female users and users identifying as diverse used significantly more first-person singular pronouns and insight words than male users (both P<.001). Negations were significantly more used by female users than male users or users identifying as diverse (P=.007). Similar findings were noted for negative emotion words (P=.01). The regression model of predicting psychiatric symptoms by linguistic variables was significant and indicated that increased use of first-person singular pronouns (odds ratio [OR] 1.05), negations (OR 1.11), and negative emotion words (OR 1.15) was positively associated with the presence of psychiatric symptoms, whereas increased use of first-person plural pronouns (OR 0.39) and causation words (OR 0.90) was negatively associated with the presence of psychiatric symptoms. Suicidality, self-harm, and depression showed the most significant correlations with linguistic variables. This study highlights the importance of examining linguistic features in chat counseling contexts. By integrating psycholinguistic findings into counseling practice, counselors may better understand users' psychological processes and provide more targeted support. For instance, certain linguistic features, such as high use of first-person singular pronouns, negations, or negative emotion words, may indicate the presence of psychiatric symptoms, particularly among female users and users identifying as diverse. Further research is needed to provide an in-depth look into language processes within chat counseling services.

Linguistic complexity measures of short narrative writings for modeling overall Chinese proficiency

This study investigates whether linguistic complexity measures of short narrative writings can model overall proficiency in Chinese as a second language. A sample of native Japanese undergraduate students with intermediate (n = 34) and advanced (n = 43) levels of Chinese proficiency, and a sample of native speakers of Chinese (n = 43) altogether provided 240 short narrative essays in Chinese, with a minimum length of 30 characters and an actual grand mean length of 45 characters. Linguistic complexity measures, as discussed in relevant literature, were identified and coded in the essays. Multivariate analyses of variance tests suggest that learners across proficiency levels performed significantly differently on 11 complexity measures of short narrative writings. Multinomial logistic regression further revealed that six of these measures (lexical diversity, high-difficulty vocabulary, complex noun phrase ratio, average sentence length, error-free T-unit ratio, and the first-person singular pronoun) could collectively distinguish student writers’ general Chinese proficiency levels, with an overall accuracy rate approaching 0.80. The implications of the findings of this study for writing assessments and writing instruction are discussed.