Linguistic Inquiry And Word Count Research Articles

Wildfires and social media discourse: exploring mental health and emotional wellbeing through Twitter

IntroductionThe 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.MethodsIn 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.ResultsThe 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.DiscussionSocial 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.

Data Augmented Graph Neural Networks for Personality Detection

Personality detection is a fundamental task for user psychology research. One of the biggest challenges in personality detection lies in the quantitative limitation of labeled data collected by completing the personality questionnaire, which is very time-consuming and labor-intensive. Most of the existing works are mainly devoted to learning the rich representations of posts based on labeled data. However, they still suffer from the inherent weakness of the amount limitation of labels, which potentially restricts the capability of the model to deal with unseen data. In this paper, we construct a heterogeneous personality graph for each labeled and unlabeled user and develop a novel psycholinguistic augmented graph neural network to detect personality in a semi-supervised manner, namely Semi-PerGCN. Specifically, our model first explores a supervised Personality Graph Neural Network (PGNN) to refine labeled user representation on the heterogeneous graph. For the remaining massive unlabeled users, we utilize the empirical psychological knowledge of the Linguistic Inquiry and Word Count (LIWC) lexicon for multi-view graph augmentation and perform unsupervised graph consistent constraints on the parameters shared PGNN. During the learning process of finite labeled users, noise-invariant learning on a large scale of unlabeled users is combined to enhance the generalization ability. Extensive experiments on three real-world datasets, Youtube, PAN2015, and MyPersonality demonstrate the effectiveness of our Semi-PerGCN in personality detection, especially in scenarios with limited labeled users.

A tale of two platforms: A comparative analysis of language use in consumer complaints on Reddit and Spotify Community

AbstractConsumer complaints play a crucial role for companies in understanding customer needs and spotting possible areas of improvement. In the digital era, online platforms have become popular venues for users to voice their concerns. According to Communication Accommodation Theory (CAT) and recipient design, consumers may have different recipients in mind when writing a complaint and tailor their language accordingly. This study aims to examine language variations in consumer complaints on an independent platform and a corporate platform, exploring how consumers adjust their language based on the intended recipients of their complaints. Comments under complaint posts from Reddit's dedicated subreddit, r/Spotify, and from the Spotify Community were extracted and analyzed using the Linguistic Inquiry and Word Count (LIWC), a text analysis tool. This comparative analysis identified and compared various linguistic categories within the consumer complaints from the perspectives of formality, emotion, and assertion. The results showed that complaints on the corporate platform (1) used more formal language, (2) contained more negative emotional tones and emotion words, and (3) exhibited more assertiveness compared to those on the independent platform. Understanding the distinct linguistic features in consumer complaints on different platforms can help companies tailor their responses and address customer concerns effectively. This research contributes to the growing field of online consumer complaining behavior by uncovering how language is employed within different online communities depending on the message recipient. Furthermore, it demonstrates the potential of LIWC as a valuable tool for analyzing unstructured consumer‐generated content across diverse digital platforms, offering insights into user sentiment and experiences.

Associations between continuous glucose monitoring (CGM) metrics and psycholinguistic measures: a correlational study.

Recently, the relationship between diabetes and mental health has been widely studied. With the advent of continuous glucose monitoring (CGM), some researchers have been interested in exploring the association between glucose-related metrics and psychological aspects. These studies have primarily relied on self-report questionnaires which present some limitations. Therefore, the present multicenter study aims at testing potential associations between CGM metrics and affective processes derived from narratives about using a CGM sensor. An exploratory correlational design was used. Fifty-eight adults with type 1 diabetes using CGM were enrolled and invited to complete an online survey, where they replied to an open-ended question regarding their personal experience with the CGM sensor. Texts derived from the answers were analyzed through Linguistic Inquiry andWord Count, a widely used text analysis tool that can automatically identify and quantify linguistic patterns related to various psychological dimensions. Psycholinguistic measures were correlated with CGM metrics. Higher levels of sadness/depression correlated with lower %TIR (r = - 339; p < .01) and higher %TAR (r = .342; p < .01). The study highlights the relationship between CGM metrics and psychological variables derived from patients' narratives. In particular, it is possible to hypothesize a positive role of %TIR in reducing depressive feelings in individuals with diabetes, as well as a negative role of depressive feelings in achieving desirable CGM outcomes. Additionally, there is a potential role of glycemic variability, particularly hyperglycemia, in the expression of depressive and sad feelings, which has been less studied compared to the effects of hypoglycemia.

Exploring influential factors in peer upvoting within social annotation

AbstractUpvotes serve important purposes in online social annotation environments. However, limited studies have explored the influential factors affecting peer upvoting in online collaborative learning. In this study, we analysed the factors influencing students' upvotes received from their peers as 91 participants utilized Perusall, an online social annotation system, for collaborative reading. The participants were asked to collaboratively annotate 29 reading materials in a semester. We collected student reading behaviours and analysed their annotations with a text‐mining tool of Linguistic Inquiry and Word Count (LIWC). Moreover, conditional inference tree was used to determine the relative importance of explanatory factors to the upvotes students received. The results showed that the high‐upvote group made significantly more annotations, posted more responses to others' annotations and displayed fewer negative emotions in annotations than those who did not receive upvotes. The two groups of students had no significant differences in the upvotes given to others, as well as cognitive activities and positive emotions involved in annotations. Moreover, the number of annotations was the determining factor in predicting the upvotes that one could receive in social annotation activities. This study has significant practical implications regarding providing interventions in social annotation‐based collaborative reading.Practitioner notesWhat is already known about this topic Social annotations enhance students' reading experience, facilitate knowledge sharing and collaboration, promote high‐quality learning interactions and ultimately lead to improved performance. In social annotation environments, receiving upvotes from peers is not only a type of feedback but also a form of motivation, social interaction and social validation. No study has explored the influential factors in peer upvoting within social annotation‐based learning. What this paper adds This study was the first to examine social annotations through the lens of the community of inquiry framework. We investigated the relationships between students' cognitive and social presence in their annotations and the upvotes they received in an online social annotation environment. Our study revealed the strategies for obtaining upvotes from peers in social annotation‐based learning environments. Implications for practice and/or policy The high‐upvote group made significantly more annotations, posted more responses to others' annotations and displayed fewer negative emotions in annotations compared to the low‐upvote group. The two groups of students did not show significant differences in the upvotes they gave to others or in the cognitive activities and positive emotions involved in annotations. The number of annotations was the primary factor predicting the number of upvotes received in the collaborative reading. This study could inform the design of future online social annotation systems to better support collaborative learning and peer interaction.

Linguistic markers for major depressive disorder: a cross-sectional study using an automated procedure.

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. 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). 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. (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. 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. 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.

It's not a lie … If you believe it: Narrative analysis of autobiographical memories reveals over-confidence disposition in patients who confabulate

Humans perceive their personal memories as fundamentally true, and although memory is prone to inaccuracies, flagrant memory errors are rare. Some patients with damage to the ventromedial prefrontal cortex (vmPFC) recall and act upon patently erroneous memories (spontaneous confabulations). Clinical observations suggest these memories carry a strong sense of confidence, a function ascribed to vmPFC in studies of memory and decision making. However, most studies of the underlying mechanisms of memory overconfidence do not directly probe personal recollections and resort instead to laboratory-based tasks and contrived rating scales. We analyzed naturalistic word use of patients with focal vmPFC damage (N = 18) and matched healthy controls (N = 23) while they recalled autobiographical memories using the Linguistic Inquiry and Word Count (LIWC) method. We found that patients with spontaneous confabulation (N = 7) tended to over-use words related to the categories of ‘certainty’ and of ‘swearwords’ compared to both non-confabulating vmPFC patients (N = 11) and control participants. Certainty related expressions among confabulating patients were at normal levels during erroneous memories and were over-expressed during accurate memories, contrary to our predictions. We found no elevation in expressions of affect (positive or negative), temporality or drive as would be predicted by some models of confabulation. Thus, erroneous memories may be associated with subjectively lower certainty, but still exceed patients' report criterion because of a global proclivity for overconfidence. This may be compounded by disinhibition reflected by elevated use of swearwords. These findings demonstrate that analysis of naturalistic expressions of memory content can illuminate global meta-mnemonic contributions to memory accuracy complementing indirect laboratory-based correlates of behavior. Memory accuracy is the result of complex interactions among multiple meta-mnemonic processes such as monitoring, report criteria, and control processes which may be shared across decision-making domains.

Linguistic analysis of health anxiety during the COVID-19 pandemic.

Health anxiety, which is defined as fear of having or contracting serious physical illness, is particularly salient in light of the COVID-19 pandemic. We conducted a mixed methods study in which 578 narrative samples were analyzed using Linguistic Inquiry and Word Count (LIWC) software to determine linguistic markers from six LIWC categories relevant to cognitive-behavioral features of health anxiety. Broad linguistic predictors were analyzed through three backward elimination regression models in order to inform subcategory predictors of each area of health anxiety. Thus, both broad and specific linguistic predictors of general health anxiety, virus-relevant body vigilance, and fears of viral contamination were examined. Greater use of affective category words in written narratives predicted general health anxiety, as well as body vigilance and viral contamination fears. These findings represent the first direct demonstration of linguistic analysis of health anxiety and provide nuanced information about the nature and etiology of health anxiety.

Power and vulnerability: managing sensitive language in organizational communication.

Organizational responsibilities can give people power but also expose them to scrutiny. This tension leads to divergent predictions about the use of potentially sensitive language: power might license it, while exposure might inhibit it. Analysis of peoples' language use in a large corpus of organizational emails using standardized Linguistic Inquiry and Word Count (LIWC) measures shows a systematic difference in the use of words with potentially sensitive (ethnic, religious, or political) connotations. People in positions of relative power are ~3 times less likely to use sensitive words than people more junior to them. The tendency to avoid potentially sensitive language appears to be independent of whether other people are using sensitive language in the same email exchanges, and also independent of whether these words are used in a sensitive context. These results challenge a stereotype about language use and the exercise of power. They suggest that, in at least some circumstances, the exposure and accountability associated with organizational responsibilities are a more significant influence on how people communicate than social power.

Deception detection based on the linguistic style of honest and dishonest stories

PurposeThis study aims to describe and analyze the differences in the linguistic styles of honest and dishonest stories.Design/methodology/approachThis paper uses a descriptive study with a multivariate analysis of linguistic categories according to the story. The research analyzed 37 honest stories and 15 dishonest stories produced during actual legal proceedings through software Linguistic Inquiry and Word Count (LIWC).FindingsThe authors find that individuals who engage in deception use a different number of words when they narrate facts. The results suggest a need for additional investigation of the linguistic style approach because of its high applicability and detection accuracy. This approach should be complemented by other types of verbal, nonverbal and psychophysiological deception detection techniques.Research limitations/implicationsAmong the limitations, the authors consider length of the stories should be considered and scarce scientific literature in Spanish to compare with outcomes in English.Practical implicationsThis research highlights the relevance to include linguistic style in real contexts to differentiate honest and dishonest stories due to objectivity and agility to implement.Social implicationsUnderstanding deception as a social behaviour and its psychological processes associated are elements that contribute to people and justice to comprehend it.Originality/valueAnalyzing real statements and discriminate differences in linguistic style, contribute to understand deeply this important behaviour to propose new methodologies and theories to explain it.

Clearing the air: evaluating institutions' social media health messaging on wildfire and smoke risks in the US Pacific Northwest.

Wildfire smoke contributes substantially to the global disease burden and is a major cause of air pollution in the US states of Oregon and Washington. Climate change is expected to bring more wildfires to this region. Social media is a popular platform for health promotion and a need exists for effective communication about smoke risks and mitigation measures to educate citizens and safeguard public health. Using a sample of 1,287 Tweets from 2022, we aimed to analyze temporal Tweeting patterns in relation to potential smoke exposure and evaluate and compare institutions' use of social media communication best practices which include (i) encouraging adoption of smoke-protective actions; (ii) leveraging numeric, verbal, and Air Quality Index risk information; and (iii) promoting community-building. Tweets were characterized using keyword searches and the Linguistic Inquiry and Word Count (LIWC) software. Descriptive and inferential statistics were carried out. 44% of Tweets in our sample were authored between January-August 2022, prior to peak wildfire smoke levels, whereas 54% of Tweets were authored during the two-month peak in smoke (September-October). Institutional accounts used Twitter (or X) to encourage the adoption of smoke-related protective actions (82% of Tweets), more than they used it to disseminate wildfire smoke risk information (25%) or promote community-building (47%). Only 10% of Tweets discussed populations vulnerable to wildfire smoke health effects, and 14% mentioned smoke mitigation measures. Tweets from Washington-based accounts used significantly more verbal and numeric risk information to discuss wildfire smoke than Oregon-based accounts (p = 0.042 and p = 0.003, respectively); however, Tweets from Oregon-based accounts on average contained a higher percentage of words associated with community-building language (p < 0.001). This research provides practical recommendations for public health practitioners and researchers communicating wildfire smoke risks on social media. As exposures to wildfire smoke rise due to climate change, reducing the environmental disease burden requires health officials to leverage popular communication platforms, distribute necessary health-related messaging rapidly, and get the message right. Timely, evidence-based, and theory-driven messaging is critical for educating and empowering individuals to make informed decisions about protecting themselves from harmful exposures. Thus, proactive and sustained communications about wildfire smoke should be prioritized even during wildfire "off-seasons."

A Study on the Correlation Between LIWC Word Categories and Chinese Composition Writing Performance of Fourth, Fifth, and Sixth Grade Students

This study focuses on the analysis of the Chinese composition writing performance of fourth, fifth, and sixth grade students in 16 selected schools in Longhua District, Shenzhen during the spring semester of 2023. Using LIWC (Linguistic Inquiry and Word Count) as a text analysis tool, the study explores the impact of LIWC categories on writing performance which is scaled by score. The results show that the simple LIWC word categories have a significant positive influence on the composition scores of lower-grade students; while complex LIWC word categories have a significant negative influence on the composition scores of lower-grade students but a significant positive influence on the composition scores of higher-grade students. Process word categories have a positive influence on the composition scores of all three grades, but the impact of complex process word categories increases as the grade level rises.

In their own words: linguistic analysis of interviews with Sesotho mothers on their child’s first thousand days

ABSTRACT Sensitive and responsive caregiving is a critical protective factor for children in Sub-Saharan Africa (SSA) during their first 1000 days of life. Health initiatives may benefit from evaluating the experience of caregivers in SSA to understand their parenting experiences. This study utilized linguistic analysis to gain insight into the lived experience of 19 Sesotho-speaking mothers of infants aged 18–30 months in low-income areas of Mangaung, Free State, South Africa. Women were asked the same set of questions about their child’s first 1000 days of life, and we used LIWC (Linguistic Inquiry and Word Count) LIWC to evaluate the perspectives of mothers in SA compared to published LIWC norms. Analyses revealed word use patterns associated with psychological processes such as negative affect, social processes, anger and anxiety. Findings are discussed within the context of Sesotho culture to aid in the future intervention development for this vulnerable population. This study is important because it yields important information about the subjective experience of an understudied critical population of mothers in SSA. Further, this study points to future directions for the field of understanding the impact of motherhood on women in low resource settings.

Emotional Reactions, Sentiment Disagreement, and Bitcoin Trading

Purpose - This study aims to explore the influence of emotional discrepancies among investors on the cryptocurrency market. It focuses on how varying emotions affect market dynamics such as volatility and trading volume in the context of Bitcoin trading.&#x0D; Design/methodology/approach - This study involves analyzing data from Bitcointalk.org, consisting of 57,963 posts and 2,215,776 responses from November 22, 2009, to December 31, 2022. Tools used include the Linguistic Inquiry and Word Count (LIWC) software for classifying emotional content and the Python Pattern library for sentiment analysis.&#x0D; Findings - The results show that heterogeneous emotional feedback, whether positive or negative, significantly influences Bitcoin's intraday volatility, skewness, and trading volume. These findings are more pronounced when the underlying emotion in the feedback is amplified.&#x0D; Research implications or Originality - This study underscores the significance of emotional factors in financial decision-making, especially within the realm of social media. It suggests that investors and market strategists should consider the emotional landscape of online forums when making investment choices or formulating market strategies. The research also paves the way for future studies regarding the behavioral impact of emotions on the cryptocurrency market.

Identifying changes in popular news media articles about the transgender community from 1990 to 2023

In the past few decades, newspaper articles about transgender people and the transgender community have become increasingly more common. At the same time, language used in this type of media has continued to evolve. In order to better understand the way public discourse around the trans community has changed, this study uses a linguistic analysis of newspaper articles from the ten most widely circulated newspapers in the United States. A sampling of articles from each year from 1990 to 2023 was analyzed using the Linguistic Inquiry and Word Count (LIWC) program to capture the authors’ attitudes. Correlation analyses were used with the LIWC categories to explore if any of the linguistic terms changed over the study period. In addition, change in the use of the terms “transsexual” and “transgender” was tracked over the same 34 year period. Results showed a statistically significant decrease in negative language used in articles about transgender people over time and a significant change in the use of the terms “transsexual” vs. “transgender”. Linguistic changes in these newspaper articles parallel changing attitudes about the trans community in mainstream American culture at large.

Students’ interest, engagement, and achievement in online high school science courses

Though documented extensively using self-report measures—especially in post-secondary course contexts—the relations among students’ interest, engagement, and academic achievement in high school science courses has been studied far less—especially when considering online versions of these courses. Self-reported interest collected at the beginning of the semester, process measures of emotional, behavioural, and cognitive engagement measured with textual analysis using the Linguistic Inquiry and Word Count (LIWC) measures of online discussion threads and time spent on the learning management system were collected throughout the semester, and final course grades were collected at the end of the semester. A sample of 622 high school students were recruited from five STEM courses to participate in this study. Results indicate that interest predicts behavioural engagement, behavioural and cognitive engagement predict final course grade, and interest predicts final course grade. Findings extend research by describing the role of engagement in online high school STEM courses.

A phenomenological exploration of the feedback experience of medical students after summative exam failure

BackgroundPreventing medical students entering cycles of underperformance following assessment is a priority due to the consequences for the student, faculty, and wider society. The benefits from feedback may be inadequately accessed by students in difficulty due to the emotional response evoked by examination failure. This study aims to explore medical students’ experiences of receiving feedback after summative assessment failure and investigate the role of emotions on motivation for learning after underperformance, to better support remediation and preparation for future assessments.MethodsThis study used interpretative phenomenological analysis (IPA) to explore the experiences of four medical students who failed summative assessments. Additionally, a content analysis was conducted using Linguistic Inquiry and Word Count (LIWC) to investigate the characteristics and use of language to describe their emotional response.ResultsAnger, fear, anxiety, and sadness were emotions frequently experienced after examination failure. These emotions led to feelings of mistrust of the medical school and subsequent distrust in the university’s assessment processes, impacting on the desire to engage with feedback. There was dissonance between the students' perceptions of what feedback should provide and what benefit feedback provided after summative assessments. The linguistic inquiry further confirmed an initial (and sometimes long lived) negative affective state after experiencing failure, and a barrier to engagement with remediation when not effectively managed.ConclusionsA range of emotions, directed at themselves and the medical school are experienced by students following exam failure. These emotions lead to a range of negative feelings and responses that affect how students make sense of and move on from the failure experience. There is a need for educators to better understand and support students to manage, reflect and contextualise their emotional responses, minimise external attribution and to enable focus on remediation and learning.

Detection of Suicidal Ideation in Clinical Interviews for Depression Using Natural Language Processing and Machine Learning: Cross-Sectional Study.

Assessing patients' suicide risk is challenging, especially among those who deny suicidal ideation. Primary care providers have poor agreement in screening suicide risk. Patients' speech may provide more objective, language-based clues about their underlying suicidal ideation. Text analysis to detect suicide risk in depression is lacking in the literature. This study aimed to determine whether suicidal ideation can be detected via language features in clinical interviews for depression using natural language processing (NLP) and machine learning (ML). This cross-sectional study recruited 305 participants between October 2020 and May 2022 (mean age 53.0, SD 11.77 years; female: n=176, 57%), of which 197 had lifetime depression and 108 were healthy. This study was part of ongoing research on characterizing depression with a case-control design. In this study, 236 participants were nonsuicidal, while 56 and 13 had low and high suicide risks, respectively. The structured interview guide for the Hamilton Depression Rating Scale (HAMD) was adopted to assess suicide risk and depression severity. Suicide risk was clinician rated based on a suicide-related question (H11). The interviews were transcribed and the words in participants' verbal responses were translated into psychologically meaningful categories using Linguistic Inquiry and Word Count (LIWC). Ordinal logistic regression revealed significant suicide-related language features in participants' responses to the HAMD questions. Increased use of anger words when talking about work and activities posed the highest suicide risk (odds ratio [OR] 2.91, 95% CI 1.22-8.55; P=.02). Random forest models demonstrated that text analysis of the direct responses to H11 was effective in identifying individuals with high suicide risk (AUC 0.76-0.89; P<.001) and detecting suicide risk in general, including both low and high suicide risk (AUC 0.83-0.92; P<.001). More importantly, suicide risk can be detected with satisfactory performance even without patients' disclosure of suicidal ideation. Based on the response to the question on hypochondriasis, ML models were trained to identify individuals with high suicide risk (AUC 0.76; P<.001). This study examined the perspective of using NLP and ML to analyze the texts from clinical interviews for suicidality detection, which has the potential to provide more accurate and specific markers for suicidal ideation detection. The findings may pave the way for developing high-performance assessment of suicide risk for automated detection, including online chatbot-based interviews for universal screening.

Understanding Mental Health Issues in Different Subdomains of Social Networking Services: Computational Analysis of Text-Based Reddit Posts.

Users increasingly use social networking services (SNSs) to share their feelings and emotions. For those with mental disorders, SNSs can also be used to seek advice on mental health issues. One available SNS is Reddit, in which users can freely discuss such matters on relevant health diagnostic subreddits. In this study, we analyzed the distinctive linguistic characteristics in users' posts on specific mental disorder subreddits (depression, anxiety, bipolar disorder, borderline personality disorder, schizophrenia, autism, and mental health) and further validated their distinctiveness externally by comparing them with posts of subreddits not related to mental illness. We also confirmed that these differences in linguistic formulations can be learned through a machine learning process. Reddit posts uploaded by users were collected for our research. We used various statistical analysis methods in Linguistic Inquiry and Word Count (LIWC) software, including 1-way ANOVA and subsequent post hoc tests, to see sentiment differences in various lexical features within mental health-related subreddits and against unrelated ones. We also applied 3 supervised and unsupervised clustering methods for both cases after extracting textual features from posts on each subreddit using bidirectional encoder representations from transformers (BERT) to ensure that our data set is suitable for further machine learning or deep learning tasks. We collected 3,133,509 posts of 919,722 Reddit users. The results using the data indicated that there are notable linguistic differences among the subreddits, consistent with the findings of prior research. The findings from LIWC analyses revealed that patients with each mental health issue show significantly different lexical and semantic patterns, such as word count or emotion, throughout their online social networking activities, with P<.001 for all cases. Furthermore, distinctive features of each subreddit group were successfully identified through supervised and unsupervised clustering methods, using the BERT embeddings extracted from textual posts. This distinctiveness was reflected in the Davies-Bouldin scores ranging from 0.222 to 0.397 and the silhouette scores ranging from 0.639 to 0.803 in the former case, with scores of 1.638 and 0.729, respectively, in the latter case. By taking a multifaceted approach, analyzing textual posts related to mental health issues using statistical, natural language processing, and machine learning techniques, our approach provides insights into aspects of recent lexical usage and information about the linguistic characteristics of patients with specific mental health issues, which can inform clinicians about patients' mental health in diagnostic terms to aid online intervention. Our findings can further promote research areas involving linguistic analysis and machine learning approaches for patients with mental health issues by identifying and detecting mentally vulnerable groups of people online.

It's in the Syllabus: The Relationship Between Syllabi Word Use and Teaching Evaluations

The authors analyzed 1,472 course syllabi written by 611 professors to determine whether communion-related and/or agency-related word use in professors’ syllabi predicted those same professors’ end-of-semester teaching evaluations. Linguistic inquiry and word count (LIWC) analyses revealed that professors who used more communal language—including third person plural pronouns (e.g., we), social words (e.g., discussion), and positive emotion words (e.g., please)—received significantly higher teaching evaluations, but agentic language did not significantly predict evaluation scores.