First-person Singular Pronouns Research Articles

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.

"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.

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.

More I-talk in student teachers’ written reflections indicates higher stress during VR teaching

Video-based reflection on one's own teaching represents a crucial tool in teacher education. When student teachers reflect on negative classroom events, it elicits “self-focused attention,” which has been associated with more intense negative emotionality. Self-focused attention can be quantitatively captured using first-person singular pronouns (“I,” “me,” “my”) in written reflections by, for instance, student teachers. What is unclear is whether student teachers' use of these first-person singular pronouns in their written reflections is linked to and predicts their negative affective experiences during teaching. For the present study, a fully immersive virtual reality (VR) classroom was implemented in which student teachers taught a lesson, provided written reflections on their teaching, and then taught a second lesson. We measured N = 59 student teachers' self-reported stress and heartrate responses while teaching in the VR classroom and determined the percentage of first-person singular pronouns in their written reflections. Firstly, our results showed that the use of first-person singular pronouns provides incremental information on manual ratings of student teachers' foci in their written reflections. Secondly, student teachers' heartrates during instruction—a measure of physiological stress—were associated with the use of first-person singular pronouns in subsequent written reflections. Thirdly, the use of first-person singular pronouns predicted the increase in physiological stress from the first to the second round of VR teaching. We discuss implications for automated feedback and for designing reflective tasks.

Language use on Twitter reflects social structure and social disparities

Large-scale mental health assessments increasingly rely upon user-contributed social media data. It is widely known that mental health and well-being are affected by minority group membership and social disparity. But do these factors manifest in the language use of social media users? We elucidate this question using spatial lag regressions. We examined the county-level (N = 1069) associations of lexical indicators linked to well-being and mental health, notably depression (e.g., first-person singular pronouns, negative emotions) with markers of social disparity (e.g., the Area Deprivation Index–3) and ethnicity, using a sample of approximately 30 million content-coded tweets (U.S. county-level aggregation). Results confirmed most expected associations: County-level lexical indicators of depression are positively linked with county-level area disparity (e.g., economic hardship and inequity) and percentage of ethnic minority groups. Predictive validity checks show that lexical indicators are related to future health and mental health outcomes. Lexical indicators of depression and adjustment coded from tweets aggregated at the county level could play a crucial role in prioritizing public health campaigns, particularly in socially deprived counties.

Precognition of mental health and neurogenerative disorders using AI-parsed text and sentiment analysis

Abstract The paper examines the potential of artificial intelligence (AI) in parsing text and conducting sentiment analysis to identify early markers of mental health and neurodegenerative disorders. Through the analysis of textual data, we investigate whether AI can provide a noninvasive, continuous, and objective complement to traditional diagnostic practices. Background: the early detection of mental health (such as depression, anxiety, psychotic disorders, Alzheimer’s disease and dementia) and neurodegenerative disorders (like Parkinson’s disease) remains a critical challenge in clinical practice. Traditional diagnostic methods rely on clinical evaluations that may be subjective and episodic. Recent advancements in AI and natural language processing (NLP) have opened new avenues for precognitive health assessments, suggesting that variations in language and expressed sentiments in written text can serve as potential biomarkers for these conditions. Materials and Methods: the research used a dataset comprising various forms of textual data, including anonymized social media interactions, transcripts from patient interviews, and electronic health records. NLP algorithms were deployed to parse the text, and machine learning models were trained to identify language patterns and sentiment changes. The study also incorporated a sentiment analysis to gauge emotional expression, a key component of mental health diagnostics. Results: the AI models were able to identify language use patterns and sentiment shifts that correlated with clinically validated instances of mental health symptoms and neurodegenerative conditions. Notably, the models detected an increased use of negative a ect words, a higher frequency of first-person singular pronouns, and a decrease in future tense in individuals with depression. For neurode-generative conditions, there was a notable decline in language complexity and semantic coherence over time. Conclusions: the implemented pipeline of AI-parsed text and sentiment analysis appears to be a promising tool for the early detection and ongoing monitoring of mental health and neurodegenerative disorders. However, these methods are supplementary and cannot replace the nuanced clinical evaluation process. Future research must refine the AI algorithms to account for linguistic diversity and context, while also addressing ethical considerations regarding data use and privacy. The integration of AI tools in clinical settings necessitates a multidisciplinary approach, ensuring that technological advancements align with patient-centered care and ethical standards.

Depression and anxiety have distinct and overlapping language patterns: Results from a clinical interview.

Depression has been associated with heightened first-person singular pronoun use (I-usage; e.g., "I," "my") and negative emotion words. However, past research has relied on nonclinical samples and nonspecific depression measures, raising the question of whether these features are unique to depression vis-à-vis frequently co-occurring conditions, especially anxiety. Using structured questions about recent life changes or difficulties, we interviewed a sample of individuals with varying levels of depression and anxiety (N = 486), including individuals in a major depressive episode (n = 228) and/or diagnosed with generalized anxiety disorder (n = 273). Interviews were transcribed to provide a natural language sample. Analyses isolated language features associated with gold standard, clinician-rated measures of depression and anxiety. Many language features associated with depression were in fact shared between depression and anxiety. Language markers with relative specificity to depression included I-usage, sadness, and decreased positive emotion, while negations (e.g., "not," "no"), negative emotion, and several emotional language markers (e.g., anxiety, stress, depression) were relatively specific to anxiety. Several of these results were replicated using a self-report measure designed to disentangle components of depression and anxiety. We next built machine learning models to detect severity of common and specific depression and anxiety using only interview language. Individuals' speech characteristics during this brief interview predicted their depression and anxiety severity, beyond other clinical and demographic variables. Depression and anxiety have partially distinct patterns of expression in spoken language. Monitoring of depression and anxiety severity via language can augment traditional assessment modalities and aid in early detection. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Linguistic markers of depression: Insights from english-language tweets before and during the COVID-19 pandemic

Research shows that the COVID-19 outbreak negatively affected people’s mental health with depression cases being the most prevalently reported mental disorder around the world. This motivated studies on the symptoms of depression through social media such as Twitter, with most of them focusing on behavioural changes in depressive individuals. Linguistic changes in tweets of depressed Twitter users however are under-researched. To date, research on linguistic markers of depression has been linked to self-focused attention and negativity bias in depressed individuals while other domains of cognitive theories of depression have remained relatively unexplored in linguistic studies. This gap in the literature is the motivation for the current paper on linguistic markers of depression in tweets during the pandemic. It focuses on sensitive markers of depression namely first-person singular pronouns, negative emotion words, and absolutist words as they reflect increased self-focus, negativity, and absolutist thinking in depressed individuals. The current study analyses the change in the use of these linguistic markers among depressed Twitter users based on tweets in English one year prior to and a year into COVID-19 pandemic. It also explores emerging linguistic markers of depression beyond the scope of self-focused attention, negativity bias, and absolutist thinking providing understanding of how depression manifests in language during global crises such as the pandemic.

Lower cohesion and altered first-person pronoun usage in the spoken life narratives of individuals with schizophrenia.

Usage of computational tools to quantify language disturbances among individuals with psychosis is increasing, improving measurement efficiency and access to fine-grained constructs. However, few studies apply automated linguistic analysis to life narratives in this population. Such research could facilitate the measurement of psychosis-relevant constructs such as sense of agency, capacity to organize one's personal history, narrative richness, and perceptions of the roles that others play in one's life. Furthermore, research is needed to understand how narrative linguistic features relate to cognitive and social functioning. In the present study, individuals with schizophrenia (n=32) and individuals without a psychotic disorder (n=15) produced personal life narratives within the Indiana Psychiatric Illness Interview. Narratives were analyzed using the Coh-Metrix computational tool. Linguistic variables analyzed were indices of connections within causal and goal-driven speech (deep cohesion), unique word usage (lexical diversity), and pronoun usage. Individuals with schizophrenia compared to control participants produced narratives that were lower in deep cohesion, contained more first-person singular pronouns, and contained fewer first-person plural pronouns. Narratives did not significantly differ between groups in lexical diversity, third-person pronoun usage, or total word count. Cognitive-linguistic relationships emerged in the full sample, including significant correlations between greater working memory capacity and greater deep cohesion and lexical diversity. In the schizophrenia group, social problem-solving abilities did not correlate with linguistic variables but were associated with cognition. Findings highlight the relevance of psychotherapies which aim to promote recovery among individuals with psychosis through the construction of coherent life narratives and increasing agency and social connectedness.

The Application of Diachronic Corpus Compilation Principles in a Pilot Study of Subjectivity

Researchers claim (see Egbert, 2018) that, irrespective of the growing amount of corpora, there is insufficient focus on the research and discussion of corpus creation and analysis challenges. The ongoing international project LEXECON (2021-2024) raises awareness about these kinds of issues. The goal of this study is twofold: firstly, to explore corpus creation stages in relation to compilation criteria; and secondly, to pilot the functionality of the created subcorpus by researching first-person pronoun variations to uncover the subjectivity across the subcorpus genres. The pronouns were explored by observing their relative frequency, context, and surplus-deficit index. Two corpus analysis tools—Sketch Engine and Hyperbase 10—were applied. The corpus creation results confirm that balance is the most challenging corpus criterion to fulfil, whereas corpus editing is the most time-consuming corpus creation stage. The results obtained via first-person pronoun extraction confirm that the context and surplus-deficit index contribute to the research results no less than the relative frequency data. The analysis of personal pronoun data variations shows that essays contain the fewest first-person singular pronouns; however, in other genres, they often do not convey an authorial stance. Moreover, a greater surplus of possessive case reflects a more active authorial stance as opposed to objective case.

Self-Promotion and Identity Construction in Graduate Personal Statements

This qualitative corpus-based study investigates the first-person singular pronouns and their possessive determiners in graduate personal statements (PSs) that applicants use to create a self-promotional tenor in their statements. Data consisted on a corpus of 120 English and French PSs collected from four academic disciplines: Linguistics, Sociology, Engineering and Biology. The analysis has shown that the self-promotion strategy employed by the Anglo-American and French applicants shed light on the graduate students’ position and strengthen their identity in their statements. Further, the linguistic investigation revealed remarkable variations in using the self-mention signals across moves, disciplines and languages. With these findings, the study offered valuable theoretical and pedagogical implications regarding the linguistic features of the genre of PSs across various cultures, disciplines and institutional contexts. In addition, they underpin pedagogic self-promotional academic writings and shed light on using this genre as a potential pedagogical tool in classroom activities, particularly in ESL and EFL contexts.

From evaluative authorities to involved narrators

Abstract Is the use of the first-person singular becoming more prevalent in journalistic writing, like often claimed, and what is it used for? In order to tackle these questions, we analysed 11,775 articles published between 1960 and 2010 in the cultural sections of ABC and El País (Spain), Dagens Nyheter (Sweden), Helsingin Sanomat (Finland), Le Monde (France), and The Guardian (UK). Our analysis focuses on the variation and change in the frequency and functions of first-person singular pronoun and verb forms over time and between different newspapers. We provide a logistic regression analysis of the whole dataset, as well as a qualitative analysis of the 1,077 articles containing at least one first-person singular. The analyses reveal a considerable amount of variation in first-person singular usage between each newspaper. The findings, however, converge to reveal that the frequency of first-persons singulars increases over time while its narrative uses become predominant at the expense of the evaluative and epistemic ones.

Predicting Generalized Anxiety Disorder From Impromptu Speech Transcripts Using Context-Aware Transformer-Based Neural Networks: Model Evaluation Study.

The ability to automatically detect anxiety disorders from speech could be useful as a screening tool for an anxiety disorder. Prior studies have shown that individual words in textual transcripts of speech have an association with anxiety severity. Transformer-based neural networks are models that have been recently shown to have powerful predictive capabilities based on the context of more than one input word. Transformers detect linguistic patterns and can be separately trained to make specific predictions based on these patterns. This study aimed to determine whether a transformer-based language model can be used to screen for generalized anxiety disorder from impromptu speech transcripts. A total of 2000 participants provided an impromptu speech sample in response to a modified version of the Trier Social Stress Test (TSST). They also completed the Generalized Anxiety Disorder 7-item (GAD-7) scale. A transformer-based neural network model (pretrained on large textual corpora) was fine-tuned on the speech transcripts and the GAD-7 to predict whether a participant was above or below a screening threshold of the GAD-7. We reported the area under the receiver operating characteristic curve (AUROC) on the test data and compared the results with a baseline logistic regression model using the Linguistic Inquiry and Word Count (LIWC) features as input. Using the integrated gradient method to determine specific words that strongly affect the predictions, we inferred specific linguistic patterns that influence the predictions. The baseline LIWC-based logistic regression model had an AUROC value of 0.58. The fine-tuned transformer model achieved an AUROC value of 0.64. Specific words that were often implicated in the predictions were also dependent on the context. For example, the first-person singular pronoun "I" influenced toward an anxious prediction 88% of the time and a nonanxious prediction 12% of the time, depending on the context. Silent pauses in speech, also often implicated in predictions, influenced toward an anxious prediction 20% of the time and a nonanxious prediction 80% of the time. There is evidence that a transformer-based neural network model has increased predictive power compared with the single word-based LIWC model. We also showed that the use of specific words in a specific context-a linguistic pattern-is part of the reason for the better prediction. This suggests that such transformer-based models could play a useful role in anxiety screening systems.

Trends in Language Use During the COVID-19 Pandemic and Relationship Between Language Use and Mental Health: Text Analysis Based on Free Responses From a Longitudinal Study.

The COVID-19 pandemic and its associated restrictions have been a major stressor that has exacerbated mental health worldwide. Qualitative data play a unique role in documenting mental states through both language features and content. Text analysis methods can provide insights into the associations between language use and mental health and reveal relevant themes that emerge organically in open-ended responses. The aim of this web-based longitudinal study on mental health during the early COVID-19 pandemic was to use text analysis methods to analyze free responses to the question, "Is there anything else you would like to tell us that might be important that we did not ask about?" Our goals were to determine whether individuals who responded to the item differed from nonresponders, to determine whether there were associations between language use and psychological status, and to characterize the content of responses and how responses changed over time. A total of 3655 individuals enrolled in the study were asked to complete self-reported measures of mental health and COVID-19 pandemic-related questions every 2 weeks for 6 months. Of these 3655 participants, 2497 (68.32%) provided at least 1 free response (9741 total responses). We used various text analysis methods to measure the links between language use and mental health and to characterize response themes over the first year of the pandemic. Response likelihood was influenced by demographic factors and health status: those who were male, Asian, Black, or Hispanic were less likely to respond, and the odds of responding increased with age and education as well as with a history of physical health conditions. Although mental health treatment history did not influence the overall likelihood of responding, it was associated with more negative sentiment, negative word use, and higher use of first-person singular pronouns. Responses were dynamically influenced by psychological status such that distress and loneliness were positively associated with an individual's likelihood to respond at a given time point and were associated with more negativity. Finally, the responses were negative in valence overall and exhibited fluctuations linked with external events. The responses covered a variety of topics, with the most common being mental health and emotion, social or physical distancing, and policy and government. Our results identify trends in language use during the first year of the pandemic and suggest that both the content of responses and overall sentiments are linked to mental health.

Language use in online support provision: the impact of others’ supportive messages, support-seeking strategy, and channel publicness

Abstract People benefit from high-quality supportive messages online, yet the production of these messages is a complex process that is shaped by multiple factors. As informed by the lens model, this study seeks to investigate the interplay of third party’s supportive messages as a form of behavioral residue and the support seeker’s seeking strategy as an identity claim on providers’ language use in supportive messages conveyed in public and private channels online. Data showed that providers’ use of words that differentiate supportive messages of varying quality (i.e., first-person singular pronouns, second-person pronouns, social process words, cognitive process words, and anxiety words) was influenced by the quality and quantity of others’ comments, the support-seeking strategy, and the publicness of the communication channel in an interactive way. Findings extend the literature by providing a more comprehensive account of online supportive communication and illuminating the actual language used to support others.

Characterizing the Prevalence of Obesity Misinformation, Factual Content, Stigma, and Positivity on the Social Media Platform Reddit Between 2011 and 2019: Infodemiology Study.

Reddit is a popular social media platform that has faced scrutiny for inflammatory language against those with obesity, yet there has been no comprehensive analysis of its obesity-related content. We aimed to quantify the presence of 4 types of obesity-related content on Reddit (misinformation, facts, stigma, and positivity) and identify psycholinguistic features that may be enriched within each one. All sentences (N=764,179) containing "obese" or "obesity" from top-level comments (n=689,447) made on non-age-restricted subreddits (ie, smaller communities within Reddit) between 2011 and 2019 that contained one of a series of keywords were evaluated. Four types of common natural language processing features were extracted: bigram term frequency-inverse document frequency, word embeddings derived from Bidirectional Encoder Representations from Transformers, sentiment from the Valence Aware Dictionary for Sentiment Reasoning, and psycholinguistic features from the Linguistic Inquiry and Word Count Program. These features were used to train an Extreme Gradient Boosting machine learning classifier to label each sentence as 1 of the 4 content categories or other. Two-part hurdle models for semicontinuous data (which use logistic regression to assess the odds of a 0 result and linear regression for continuous data) were used to evaluate whether select psycholinguistic features presented differently in misinformation (compared with facts) or stigma (compared with positivity). After removing ambiguous sentences, 0.47% (3610/764,179) of the sentences were labeled as misinformation, 1.88% (14,366/764,179) were labeled as stigma, 1.94% (14,799/764,179) were labeled as positivity, and 8.93% (68,276/764,179) were labeled as facts. Each category had markers that distinguished it from other categories within the data as well as an external corpus. For example, misinformation had a higher average percent of negations (β=3.71, 95% CI 3.53-3.90; P<.001) but a lower average number of words >6 letters (β=-1.47, 95% CI -1.85 to -1.10; P<.001) relative to facts. Stigma had a higher proportion of swear words (β=1.83, 95% CI 1.62-2.04; P<.001) but a lower proportion of first-person singular pronouns (β=-5.30, 95% CI -5.44 to -5.16; P<.001) relative to positivity. There are distinct psycholinguistic properties between types of obesity-related content on Reddit that can be leveraged to rapidly identify deleterious content with minimal human intervention and provide insights into how the Reddit population perceives patients with obesity. Future work should assess whether these properties are shared across languages and other social media platforms.