Linguistic distancing predicts response to a digital single-session intervention for adolescent depression

Emotion Regulation is Associated with Increases in Linguistic Measures of Both Psychological Distancing and Abstractness

Emerging research shows that distancing one's language by reducing the use of "I" and present-tense verbs is associated with more successful emotion regulation. However, construal level theory posits that increasing psychological distance should also shift mental representations to be more abstract. Here, we test whether successful emotion regulation is related to linguistic measures of both abstractness and psychological distance in two emotion regulation experiments reported previously (N 1 = 217, N 2 = 464). As hypothesized, we found that (i) linguistic measures of psychological distancing and abstractness were highly correlated, (ii) linguistic abstractness increased when people used cognitive reappraisal strategies to regulate their responses to negative images, (iii) instructing participants to distance their language by not using the word "I" increased linguistic abstractness, and (iv) the extent to which people made their language more abstract while regulating correlated with regulation success. These results extend theoretical arguments that psychological distance and abstract construals are strongly connected in the mind. Furthermore, they provide novel evidence that successful cognitive reappraisal is associated with more abstract representations of stimuli and that this cognitive shift can be achieved merely by shifting one's language.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42761-024-00269-7.

Emotion regulation elicits cross-linguistically shared and language-specific forms of linguistic distancing

Cognitively reappraising a stressful experience-reinterpreting the situation to blunt its emotional impact-is effective for regulating negative emotions. English speakers have been shown to engage in linguistic distancing when reappraising, spontaneously using words that are more abstract or impersonal. Across two preregistered studies (N = 299), we investigated whether such shifts in language use generalize to Spanish, a language proposed to offer unique tools for expressing psychological distance. Bilingual speakers of Spanish and English and a comparison group of English monolinguals transcribed their thoughts in each of their languages while responding naturally to negative images or reappraising them. Reappraisal shifted markers of psychological distance common to both languages (e.g., reduced use of "I"/"yo"), as well as Spanish-specific markers (e.g., greater use of "estar": "to be" for temporary states). Whether these linguistic shifts reflected successful emotion regulation depended on language experience: in exploratory analyses, the common markers were more strongly linked to reduced negative affect for late than early Spanish learners, and one Spanish-specific marker ("estar") also predicted reduced negative affect for early learners. Our findings suggest that people distance their language in both cross-linguistically shared and language-specific ways when regulating their emotions.

Language sentiment predicts changes in depressive symptoms

The prevalence of depression is a major societal health concern, and there is an ongoing need to develop tools that predict who will become depressed. Past research suggests that depression changes the language we use, but it is unclear whether language is predictive of worsening symptoms. Here, we test whether the sentiment of brief written linguistic responses predicts changes in depression. Across two studies (N = 467), participants provided responses to neutral open-ended questions, narrating aspects of their lives relevant to depression (e.g., mood, motivation, sleep). Participants also completed the Patient Health Questionnaire (PHQ-9) to assess depressive symptoms and a risky decision-making task with periodic measurements of momentary happiness to quantify mood dynamics. The sentiment of written responses was evaluated by human raters (N = 470), Large Language Models (LLMs; ChatGPT 3.5 and 4.0), and the Linguistic Inquiry and Word Count (LIWC) tool. We found that language sentiment evaluated by human raters and LLMs, but not LIWC, predicted changes in depressive symptoms at a three-week follow-up. Using computational modeling, we found that language sentiment was associated with current mood, but language sentiment predicted symptom changes even after controlling for current mood. In summary, we demonstrate a scalable tool that combines brief written responses with sentiment analysis by AI tools that matches human performance in the prediction of future psychiatric symptoms.

The Influence of Cognitive and Emotional Factors on Social Media Users' Information-Sharing Behaviours during Crises: The Moderating Role of the Construal Level and the Mediating Role of the Emotional Response

Understanding the intricate dynamics of social media users' information-sharing behaviours during crises is essential for effective public opinion management. While various scholarly efforts have attempted to uncover the factors influencing information sharing through different lenses, the underlying mechanisms remain elusive. Building upon the heuristic-systematic model (HSM) and construal level theory (CLT), this study explores the complex mechanisms that govern social media users' information-sharing behaviours. The results indicate that both cognition and emotion play crucial roles in shaping users' information-sharing behaviours, with systematic cues having the most significant impact on information-sharing behaviours. In terms of heuristic cues, positive emotions are more influential on information-sharing behaviours than primary cognition and negative emotions. Furthermore, spatial distance emerges as a key moderator, influencing individuals' levels of engagement in information sharing. Emotion also acts as a mediator, connecting cognition to information sharing. This study provides insights into the sophisticated mechanisms of information sharing during crises, offering valuable implications for emergency management agencies to utilise social media for targeted public opinion guidance.

The Emotional Content of Children's Writing: A Data-Driven Approach

Emotion is closely associated with language, but we know very little about how children express emotion in their own writing. We used a large-scale, cross-sectional, and data-driven approach to investigate emotional expression via writing in children of different ages, and whether it varies for boys and girls. We first used a lexicon-based bag-of-words approach to identify emotional content in a large corpus of stories (N>100,000) written by 7- to 13-year-old children. Generalized Additive Models were then used to model changes in sentiment across age and gender. Two other machine learning approaches (BERT and TextBlob) validated and extended these analyses, converging on the finding that positive sentiments in children's writing decrease with age. These findings echo reports from previous studies showing a decrease in mood and an increased use of negative emotion words with age. We also found that stories by girls contained more positive sentiments than stories by boys. Our study shows the utility of large-scale data-driven approaches to reveal the content and nature of children's writing. Future experimental work should build on these observations to understand the likely complex relationships between written language and emotion, and how these change over development.

The Promise of Affective Language for Identifying and Intervening on Psychopathology

We are in dire need of innovative tools for reducing the global burden of psychopathology. Emerging evidence suggests that analyzing language (i.e., the words people use) can grant insight into an individual's emotional experiences, their ability to regulate their emotions, and even their current experiences of psychopathology. As such, linguistic analyses of people's everyday word use may be a diagnostic marker of emotional well-being, and manipulating the words people use could foster adaptive emotion regulation and mental health. Given the ubiquity of language in everyday life, such language-based tools for measuring and intervening in emotion and mental health can advance how we identify and treat mental illnesses at a large scale. In this paper, I outline the promise of this approach and identify key problems we must solve if we are to make it a reality. In particular, I summarize evidence connecting language, emotion, and mental health for three key constructs: sentiment (i.e., the valence of one's language), linguistic distancing (i.e., using language to separate oneself from distressing stimuli), and emotion differentiation (i.e., using words to specifically identify one's emotions). I also identify open questions in need of attention for each of these constructs and this area of research as a whole. Overall, I believe the future is bright for the application of psycholinguistic approaches to mental health detection and intervention.