Language Patterns as Concurrent and Longitudinal Predictors of Depression in Adolescence

A growing body of literature using primarily samples of adults suggests a link between specific patterns of language use and depression (e.g., Rude, Gortner, & Pennebaker, 2004). The current study sought to evaluate whether this link might exist in adolescence, particularly given the rise in depression that occurs in this stage (Garber, Weiss, & Shanley, 1993). This linkage was investigated using a cross-sectional and 2-year longitudinal design, utilizing a community sample of 192 adolescents (Mage = 12.65, 53.1% girls, 76.0% White, middle-class). Adolescents reported on their depression and engaged in a 15-minute discussion task with their good friend. Four specific patterns of language use were evaluated from the videotaped discussion task and included pronouns, tense, positive and negative emotion word use and specific negative emotion word use. Further, the role of gender was examined as a potential moderator of the relation between language use and depression. Relations were found between language patterns and depression both concurrently and longitudinally, with notable developmental differences. At both time points, first-person singular pronouns predicted greater depressive symptoms. Second-person pronoun use significantly predicted greater depressive symptoms concurrently. Use of present tense significantly predicted depressive symptoms concurrently, whereas future tense use significantly predicted greater depressive symptoms at both time points. Adolescents who used more sadness emotion words reported greater depressive symptoms. Gender moderated the relation between positive emotion words and depressive symptoms concurrently and at both time points for anxiety words and depressive symptoms. Taken together, these findings add to our understanding of depression, and may help to inform preventative intervention for adolescent depression

Assessing Suicide Risk and Emotional Distress in Chinese Social Media: A Text Mining and Machine Learning Study

published_or_final_versio

The Stylometric Processing of Sensory Open Source Data

This research project’s end goal is on the Lone Wolf Terrorist. The project uses an exploratory approach to the self-radicalisation problem by creating a stylistic fingerprint of a person's personality, or self, from subtle characteristics hidden in a person's writing style. It separates the identity of one person from another based on their writing style. It also separates the writings of suicide attackers from ‘normal' bloggers by critical slowing down; a dynamical property used to develop early warning signs of tipping points. It identifies changes in a person's moods, or shifts from one state to another, that might indicate a tipping point for self-radicalisation. Research into authorship identity using personality is a relatively new area in the field of neurolinguistics. There are very few methods that model how an individual's cognitive functions present themselves in writing. Here, we develop a novel algorithm, RPAS, which draws on cognitive functions such as aging, sensory processing, abstract or concrete thinking through referential activity emotional experiences, and a person's internal gender for identity. We use well-known techniques such as Principal Component Analysis, Linear Discriminant Analysis, and the Vector Space Method to cluster multiple anonymous-authored works. Here we use a new approach, using seriation with noise to separate subtle features in individuals. We conduct time series analysis using modified variants of 1-lag autocorrelation and the coefficient of skewness, two statistical metrics that change near a tipping point, to track serious life events in an individual through cognitive linguistic markers. In our journey of discovery, we uncover secrets about the Elizabethan playwrights hidden for over 400 years. We uncover markers for depression and anxiety in modern-day writers and identify linguistic cues for Alzheimer's disease much earlier than other studies using sensory processing. In using these techniques on the Lone Wolf, we can separate their writing style used before their attacks that differs from other writing

Adolescent Suicide Through the Lens of 13 Reasons Why: A Corpus Based Study

With growing recognition of adolescence as the time in which suicidal thoughts and behaviors first occur, early identification and intervention systems in schools have become an international priority. Understanding and incorporating language indicators associated with the core psychological and interpersonal processes of adolescent suicidality could provide promising tools for school practitioners. Corpus linguistic research has identified several consistent verbal markers across written and spoken, formal and informal, and expressive or fictional texts. This dissertation research draws upon these findings along with interpersonal suicide theory to investigate potential replications and practical implications in the language of 13 Reasons Why (13RW), young adult “suicide fiction’s” most popular representation. The dissertation is composed of two research arms; Research Arm A looked at psychological and linguistic suicide�related language within Jay Asher’s 13RW novel. Research Arm B looked at the same verbal markers in the context of the 13RW web TV series dialogue. Informed by the literature, Research Arm A sought to examine the level of 21 specific linguistic and psychological variables associated with youth suicide. Both examinations were conducted using Linguistic Inquiry and Word Count (LIWC) software. To ascertain if unique levels of these variables were present, this novel was compared against general novel norms using inferential statistics. Results encountered included the following rates higher than novel norms: (a) first-person singular three times higher; (b) second person two times higher; (c) present time focus one and a half times higher; and (d) causal explanation thinking style one and a half times higher. Research Arm B employed the same methodology as Arm A but examined the first season dialogue of the mass media Netflix series adaptation of 13RW. For this arm, the comparison norms were from a large database of TV series subtitle texts. For Research Arm B, the findings included the following rates higher than TV subtitle norms: (a) first person singular one and one-third higher; (b) second person one and one-third higher; (c) friend one and a half times higher; and (d) present focus one and a half times higher. These results show how the 13RW texts have their own ways of using language and hint at features of the emerging "suicide fiction" genre that are linked to known signs of suicidality in teens. These results extend previous corpus linguistic research findings related to suicidality language markers and suggest the need for additional applications of this research specific to adolescent school community populations. Implications for school-based suicide prevention include support for the incorporation of core language analysis concepts to strengthen evidence-based practices and efficiency

Deep into that Darkness Peering:A Computational Analysis of the Role of Depression in Edgar Allan Poe's Life and Death

Background: To help shed light on the peculiar circumstances surrounding the death of the famed macabre and mystery writer, poet, editor, and literary critic, we explored the potential role of depression in the life and death of Edgar Allan Poe via his written language. Method: Using computerized language analysis, we analyzed works from Poe’s corpora of personal letters (N = 309), poems (N = 49), and short stories (N = 63), and investigated whether a pattern of linguistic cues consistent with depression and suicidal cognition were discernible throughout the writer’s life, particularly in his final years. Building on past work, language scores were collapsed into a composite depression metric for each text. Data from each work type was subsequently compiled and graphed into a single plot by year, with scores exceeding the 95th percentile (p <.05) considered statistically significant and treated as potential depressive episodes. Results: Significant, consistent patterns of depression were not found and do not support suicide as a cause of death. However, linguistic evidence was found suggesting the presence of several potential depressive episodes over the course of Poe’s life – these episodes were the most pronounced during years of Poe’s greatest success, as well as those following the death of his late wife. Limitations: Given the sampling method, it is not possible to establish direct causality; results should be considered informed but tentative. Conclusion: This investigation demonstrates the utility of language analysis for capturing disruptive/maladaptive emotional responses to life events

In an absolute state: elevated use of absolutist words is a marker specific to anxiety, depression and suicidal ideation

Absolutist thinking is considered a cognitive distortion by most cognitive therapies for anxiety and depression. Yet, there is little empirical evidence of its prevalence or specificity. Across three studies, we conducted a text analysis of 63 internet forums (over 6,400 members) using the Linguistic- Inquiry and Word Count software (Pennebaker, Booth, Boyd, & Francis, 2015) to examine absolutism at the linguistic level. We predicted and found that anxiety, depression and suicidal ideation forums contained more absolutist words than control forums (d’s > 3.14). Suicidal ideation forums also contained more absolutist words than anxiety and depression forums (d’s > 1.71). We show that these differences are more reflective of absolutist thinking than psychological distress. Interestingly, absolutist words tracked the severity of affective disorder forums more faithfully than ‘negative emotion’ words. Finally, we found elevated levels of absolutist words in depression ‘recovery’ forums. This suggests that absolutist thinking may be a vulnerability factor

Computerized text-analysis of offenders of mass shootings: an investigation of moral foundations using linguistic inquiry and word count

The present study used a computational linguistic approach to examine moral foundations, emotionality, and personal concerns in the written communications of mass shooters. Writings by mass shooters (N = 36) were harvested from an online database and coded for writing type using five writing categories: manifestos, blog posts, social media, private letters, and journal entries. They were then submitted to linguistic analyses using Linguistic Inquiry and Word Count (LIWC). Shooters’ writings were then compared to a sample of prisoners (N = 35) who were convicted of violent crime along with the normative data available in LIWC. The Moral Foundations Dictionary (MFD) was also used to predict differences between the samples, although no significant differences were found between shooters and prisoners on the five moral foundations. Overall, results of these analyses indicated that mass shooters primarily use high rates of negative emotion and swear words. Unlike previous studies, however, mass shooters were comparatively low on cognitive processes and complexity relative to the prisoner sample. Exploratory analyses were then conducted using the entire LIWC2015 and MFD dictionaries to identify the set of word categories that maximally predicts differences between groups across writing types.Thesis (M.S.

Writing Style And Word Usage In Detecting Depression In Social Media: A Review

In today’s digital age, social media have become the most common channel for individuals to express their opinions and feelings. As common as this, the extensive usage of social media has also been associated with mental illnesses such as anxiety, suicidality and depression. The digital traces the individuals left provide insights into not just their daily life but also on their health and mental state. This allows for various prediction and preliminary diagnosis to be made. The advancement of research in the Natural Language Processing (NLP) field has allowed researchers to understand individuals based on texts they shared in their social media account. This paper reviews the techniques and methods used in detecting depression from social media texts where emphasis are being placed on the writing style and the word usage of the social media users. Writing styles and choices of words have been seen as a possible indicator in detecting depression from social media texts. Various methods and platforms have been adopted to investigate the effectiveness of detecting depression based on these two components. This paper discusses these methods and techniques as well as the areas where improvements can be mad

Analyzing Songs Used for Lyric Analysis With Mental Health Consumers Using Linguistic Inquiry and Word Count (LIWC) Software

Lyric analysis is one of the most commonly used music therapy interventions with the mental health population, yet there is a gap in the research literature regarding song selection. The primary purpose of this study was to determine distinguishing linguistic characteristics of song lyrics most commonly used for lyric analysis with mental health consumers, as measured by LIWC2015 software. A secondary purpose was to provide an updated song list resource for music therapists and music therapy students working with the mental health population. The researcher emailed a survey to 6,757 board-certified music therapists, 316 of whom completed the survey. Respondents contributed 700 different songs that they deemed most effective for lyric analysis with mental health consumers. The researcher used the LIWC2015 software to analyze the 48 songs that were listed by five or more music therapists. Song lyrics contained linguistic indicators of self-focused attention, present-focused attention, poor social relationships, and high cognitive processing. Lyrics were written in an informal, personal, and authentic style. Some lyrics were more emotionally positive, while others were more emotionally negative. While results must be interpreted with caution, it may be helpful to consider linguistic elements when choosing songs for lyric analysis with mental health consumers