Emotion Language in Trauma Narratives is Associated with Better Psychological Adjustment among Survivors of Childhood Sexual Abuse

Trauma Isn't One Size Fits All: How Online Support Communities Point to Different Diagnostic Criteria for C-PTSD and PTSD

Reddit has provided rich data on mental health discourse. The present study uses 40,335 online posts from Reddit communities to investigate how language can contribute to the understanding of PTSD and C-PTSD. The results showed distinct language patterns in the use of first-person pronouns, cognitive processing, and emotion words, suggesting that they are separate disorders with different effects on survivors. Further, while some social media studies have differentiated submissions and comments, few have investigated the language changes between these contexts. Post-hoc results showed a clear distinction between two contexts across several linguistic markers. Discussion and future directions are explored.

Metaphorical language and psychopathological symptoms: a case study of trauma victims' metaphor use

BACKGROUND

While clinical diagnosis of mental health issues focuses on factual details represented by literal language (e.g., the onset and process of the triggering event and duration of symptom), the relationship between metaphorical language and psychopathological experiences remains an intriguing question. Focusing on psychological trauma triggered by the 2019-2020 Hong Kong social unrest, this study explored the correlations between trauma victims' quantitative metaphor usage patterns and their experience of specific Acute Stress Disorder (ASD) symptoms.

METHODS

Forty-six individuals with trauma exposure within 28 days were recruited through convenience sampling. Each completed a 20- to 30-minute semi-structured interview and filled out the Chinese version of the Stanford Acute Stress Reaction Questionnaire (SASRQ; 1). Metaphors in the interviews were identified using the discourse dynamic approach (2), and clinically interesting categories related to trauma and emotion expression, as revealed by previous literature, were sorted out. Standardized frequencies of the categories were correlated with participants' SASRQ scores of five major ASD symptoms, and the correlational patterns were interpreted from a discourse analytic perspective.

RESULTS

The study reveals how metaphor usage patterns can reflect the speakers' differentiated experiences of psychopathological symptoms. Compared with individuals who experienced less trauma, those more disturbed by the re-experiencing symptom were more inclined to use emotion-related metaphors and to metaphorize about the self and the self-society relationship. Individuals who experienced more severe anxiety and hyperarousal showed a heightened awareness of self-related issues and diminished attention to others. Those who suffered from more severe impairment in functioning produced more metaphors in the negative valence. Dissociation and avoidance, which were less experientially salient and intense than the others, were not significantly correlated with metaphor usage patterns.

CONCLUSION

This study establishes symptom-level metaphor usage patterns as a previously overlooked but interesting avenue in trauma evaluation, treatment, and research. While the study is confined to a single context, it nevertheless reveals the potential for metaphor research findings to be incorporated as useful materials in psychology education and therapist training.

Detecting Presence of PTSD Using Sentiment Analysis From Text Data

Rates of Post-traumatic stress disorder (PTSD) have risen significantly due to the COVID-19 pandemic. Telehealth has emerged as a means to monitor symptoms for such disorders. This is partly due to isolation or inaccessibility of therapeutic intervention caused from the pandemic. Additional screening tools may be needed to augment identification and diagnosis of PTSD through a virtual medium. Sentiment analysis refers to the use of natural language processing (NLP) to extract emotional content from text information. In our study, we train a machine learning (ML) model on text data, which is part of the Audio/Visual Emotion Challenge and Workshop (AVEC-19) corpus, to identify individuals with PTSD using sentiment analysis from semi-structured interviews. Our sample size included 188 individuals without PTSD, and 87 with PTSD. The interview was conducted by an artificial character (Ellie) over a video-conference call. Our model was able to achieve a balanced accuracy of 80.4% on a held out dataset used from the AVEC-19 challenge. Additionally, we implemented various partitioning techniques to determine if our model was generalizable enough. This shows that learned models can use sentiment analysis of speech to identify the presence of PTSD, even through a virtual medium. This can serve as an important, accessible and inexpensive tool to detect mental health abnormalities during the COVID-19 pandemic.

Performing Multilingual Analysis With Linguistic Inquiry and Word Count 2015 (LIWC2015). An Equivalence Study of Four Languages

Today, there is a range of computer-aided techniques to convert text into data. However, they convey not only strengths but also vulnerabilities compared to traditional content analysis. One of the challenges that have gained increasing attention is performing automatic language analysis to make sound inferences in a multilingual assessment setting. The current study is the first to test the equivalence of multiple versions of one of the most appealing and widely used lexicon-based tools worldwide, Linguistic Inquiry and Word Count 2015 (LIWC2015). For this purpose, we employed supervised learning in a classification problem and computed Pearson's correlations and intraclass correlation coefficients on a large corpus of parallel texts in English, Dutch, Brazilian Portuguese, and Romanian. Our findings suggested that LIWC2015 is a valuable tool for multilingual analysis, but within-language standardization is needed when the aim is to analyze texts sourced from different languages.

Coronavirus Disease 2019: Exploring Media Portrayals of Public Sentiment on Funerals Using Linguistic Dimensions

Funerals are a reflective practice to bid farewell to the departed soul. Different religions, cultural traditions, rituals, and social beliefs guide how funeral practices take place. Family and friends gather together to support each other in times of grief. However, during the coronavirus pandemic, the way funerals are taking place is affected by the country's rules and region to avoid the spread of infection. The present study explores the media portrayal of public sentiments over funerals. In particular, the present study tried to identify linguistic dimensions associated with lexical components of social processes, affective processes, fear, and disgust. An exhaustive search of newspaper coverage of funeral and related articles was made for a specific corona period. After an initial screening for the details and language used, a total of 46 newspaper articles on funerals were finalized for the analysis. Linguistic Inquiry and Word Count (LIWC) software was used to determine the association between linguistic dimensions of function words and words related to social and affective processes, as presented in the newspaper articles. Sentiment Analysis and Cognition Engine (SEANCE) was applied for the analysis of sentiment, social cognition, and social order. Bayesian correlation analysis and regression revealed positive and significant associations between function words and affective processes, between pronouns and social processes, and between negative adjectives and psychological processes of fear and disgust. Also, significant negative associations were found between polarity nouns and psychological processes of fear and disgust and between polarity verbs and psychological processes of fear and disgust. Bayes factor 10 provides strong evidence in favor of the study hypotheses. The media is influenced by the prevailing sentiments in society and reflects their perception of the current social order and beliefs. The findings provide a glimpse into the prevailing sentiment of society through the lens of media coverage. These understandings are expected to enhance our observations of how people express their feelings over the loss of their loved ones and help mental health professionals develop their therapeutic protocols to treat the coronavirus disease 2019 (COVID-19)-affected population.

Objective analysis of language use in cognitive-behavioral therapy: associations with symptom change in adults with co-occurring substance use disorders and posttraumatic stress

Substance use disorders (SUD) commonly co-occur with posttraumatic stress disorder (PTSD) symptoms, and the comorbidity is prevalent and difficult-to-treat. Few studies have objectively analyzed language use in psychotherapy as a predictor of treatment outcomes. We conducted a secondary analysis of patient language use during cognitive-behavioral therapy (CBT) in a randomized clinical trial, comparing a novel, integrated CBT for PTSD/SUD with standard CBT for SUD. Participants included 37 treatment-seeking, predominantly African-American adults with SUD and at least four symptoms of PTSD. We analyzed transcripts of a single, matched session across both treatment conditions, using the Linguistic Inquiry and Word Count (LIWC) program. The program measures language use across multiple categories. Compared to standard CBT for SUD, patients in the novel, integrated CBT for PTSD/SUD used more negative emotion words, partially consistent with our hypothesis, but less positive emotion words. Further, exploratory analyses indicated an association between usage of cognitive processing words and clinician-observed reduction in PTSD symptoms, regardless of treatment condition. Our results suggest that language use during therapy may provide a window into mechanisms active in therapy.

Applying Natural Language Processing to Evaluate News Media Coverage of Bullying and Cyberbullying

Bullying events have frequently been the focus of coverage by news media, including news stories about teens whose death from suicide was attributed to cyberbullying. Previous work has shown that news media coverage is influential to readers in areas such as suicide, infectious disease outbreaks, and tobacco use. News media may be an untapped resource to promote bullying prevention messages, though current news media approaches to describing bullying and cyberbullying remain unexplored. The purpose of this study was to evaluate the current state of news media coverage of bullying and cyberbullying. A sample of newspaper articles covering bullying or cyberbullying across regional and national US newspapers from 6 recent years was identified. A content analysis using natural language processing was conducted with the Linguistic Inquiry and Word Count (LIWC) software program for key variables including affective, social, and cognitive processes. Evaluation included the percentage of words that represented Fear-based reporting such as alarmist words (e.g., epidemic, tragic), as well as words that represent Public Health-oriented messages such as prevention. A total of 463 newspaper articles met inclusion criteria, including 140 cyberbullying articles and 323 bullying articles. Findings indicated that cyberbullying articles scored higher on affective processes such as measures of anxiety (Mdn = 0.34) compared to bullying articles (Mdn = 0.22). A greater number of cyberbullying articles were Fear-based (41.4%) than were bullying articles (19.5%). An equivalent number of cyberbullying articles (50.0%) and bullying articles (49.8%) were Public Health-oriented. Findings may be used to collaborate with journalists toward optimizing prevention-oriented reporting.