When resilience is not enough: Imagining novel approaches to supporting Black youth navigating racism.

The narrative surrounding the impact racism has had on the well-being of Black youth has shifted across sociocultural and historical context. Early discourse around these topics were problem-saturated, focusing on deficits "within" Black youth. Over time, an important narrative shift occurred: greater attention was paid to the inherent assets of Black youth, their families, and communities, including how racial-ethnic protective factors such as racial socialization afforded them resilience. What resulted was decades of research seeking to understand the mechanisms that allow Black youth to bounce back in spite of racism-related adversity. Notwithstanding the viable practice and policy implications that have emerged from such inquiry, at what point does our focus on the resilience of Black youth - whether individual or multisystemic - fall short? It is with this question in mind that this paper challenges those committed to the optimal development of Black youth to consider yet another narrative shift: one that stands upon the legacy of cultural ecological frameworks and the seminal models underlying resilience research, and calls us toward not supporting Black youth's adaptation to racism, but toward collective efforts to transform our approach, pushing back against the perniciousness of racism.

AI-based analysis of social media language predicts addiction treatment dropout at 90 days.

The reoccurrence of use (relapse) and treatment dropout is frequently observed in substance use disorder (SUD) treatment. In the current paper, we evaluated the predictive capability of an AI-based digital phenotype using the social media language of patients receiving treatment for substance use disorders (N = 269). We found that language phenotypes outperformed a standard intake psychometric assessment scale when predicting patients' 90-day treatment outcomes. We also use a modern deep learning-based AI model, Bidirectional Encoder Representations from Transformers (BERT) to generate risk scores using pre-treatment digital phenotype and intake clinic data to predict dropout probabilities. Nearly all individuals labeled as low-risk remained in treatment while those identified as high-risk dropped out (risk score for dropout AUC = 0.81; p < 0.001). The current study suggests the possibility of utilizing social media digital phenotypes as a new tool for intake risk assessment to identify individuals most at risk of treatment dropout and relapse.

Linguistic predictors from Facebook postings of substance use disorder treatment retention versus discontinuation.

Background: Early indicators of who will remain in - or leave - treatment for substance use disorder (SUD) can drive targeted interventions to support long-term recovery.Objectives: To conduct a comprehensive study of linguistic markers of SUD treatment outcomes, the current study integrated features produced by machine learning models known to have social-psychology relevance.Methods: We extracted and analyzed linguistic features from participants' Facebook posts (N = 206, 39.32% female; 55,415 postings) over the two years before they entered a SUD treatment program. Exploratory features produced by both Linguistic Inquiry and Word Count (LIWC) and Latent Dirichlet Allocation (LDA) topic modeling and the features from theoretical domains of religiosity, affect, and temporal orientation via established AI-based linguistic models were utilized.Results: Patients who stayed in the SUD treatment for over 90 days used more words associated with religion, positive emotions, family, affiliations, and the present, and used more first-person singular pronouns (Cohen's d values: [-0.39, -0.57]). Patients who discontinued their treatment before 90 days discussed more diverse topics, focused on the past, and used more articles (Cohen's d values: [0.44, 0.57]). All ps < .05 with Benjamini-Hochberg False Discovery Rate correction.Conclusions: We confirmed the literature on protective and risk social-psychological factors linking to SUD treatment in language analysis, showing that Facebook language before treatment entry could be used to identify the markers of SUD treatment outcomes. This reflects the importance of taking these linguistic features and markers into consideration when designing and recommending SUD treatment plans.

Getting "clean" from nonsuicidal self-injury: Experiences of addiction on the subreddit r/selfharm.

Background & Aims: Previous studies have shown that nonsuicidal self-injury (NSSI) has addictive features, and an addiction model of NSSI has been considered. Addictive features have been associated with severity of NSSI and adverse psychological experiences. Yet, there is debate over the extent to which NSSI and substance use disorders (SUDs) are similar experientially. Methods: To evaluate the extent that people who self-injure experience NSSI like an addiction, we coded the posts of users of the subreddit r/selfharm (n = 500) for each of 11 DSM-5 SUD criteria adapted to NSSI. Results: A majority (76.8%) of users endorsed at least two adapted SUD criteria in their posts, indicative of mild, moderate, or severe addiction. The most frequently endorsed criteria were urges or cravings (67.6%), escalating severity or tolerance (46.7%), and NSSI that is particularly hazardous. User-level addictive features positively predicted number of methods used for NSSI, number of psychiatric disorders, and particularly hazardous NSSI, but not suicidality. We also observed frequent use of language and concepts common in SUD recovery circles like Alcoholics Anonymous. Discussion & Conclusion: Our findings support previous work describing the addiction potential of NSSI and associating addictive features with clinical severity. These results suggest that NSSI and SUD may share experiential similarities, which has implications for the treatment of NSSI. We also contribute to a growing body of work that uses social media as a window into the subjective experiences of stigmatized populations.

Black Community Conversations About Opposing Ethnically Targeted Marketing of Unhealthy Foods and Beverages.

Efforts to reduce disparities in obesity prevalence affecting Black Americans are having limited success. One reason for this may be the disproportionate, ethnically targeted marketing of foods and beverages high in fat and sugar (FBHFS) to Black consumers. Such marketing promotes high consumption of FBHFS, leading to excess caloric intake and unintentional weight gain. We convened focus groups with Black men and women (total n = 57) in collaboration with community groups in three localities to elicit their views, as consumers and parents/caregivers, about targeted FBHFS marketing and potential ways to combat it. At each location, trained community members facilitated two sets of focus groups: one for adults aged 18 to 25 years and another for adults aged 26 to 55 years who had a 3-to-17-year-old child at home. Each group met twice to discuss food and beverage marketing practices to Black communities and reviewed a booklet about ethnically targeted marketing tactics in between. A directed content analysis of participant comments identified and explored salient themes apparent from initial summarization of results. Results show how parents are concerned with and critical of pervasive FBHFS marketing. In particular, comments emphasize the involvement of Black celebrities in FBHFS marketing-how and why they engage in such marketing and whether this could be shifted towards healthier foods. These findings suggest a potential role for counter marketing efforts focused on Black celebrity endorsements of FBHFS, possibly with a youth focus. They also underscore the need for additional, qualitative exploration of Black consumer views of ethnically targeted FBHFS marketing more generally.

Head versus heart: social media reveals differential language of loneliness from depression.

We study the language differentially associated with loneliness and depression using 3.4-million Facebook posts from 2986 individuals, and uncover the statistical associations of survey-based depression and loneliness with both dictionary-based (Linguistic Inquiry Word Count 2015) and open-vocabulary linguistic features (words, phrases, and topics). Loneliness and depression were found to have highly overlapping language profiles, including sickness, pain, and negative emotions as (cross-sectional) risk factors, and social relationships and activities as protective factors. Compared to depression, the language associated with loneliness reflects a stronger cognitive focus, including more references to cognitive processes (i.e., differentiation and tentative language, thoughts, and the observation of irregularities), and cognitive activities like reading and writing. As might be expected, less lonely users were more likely to reference social relationships (e.g., friends and family, romantic relationships), and use first-person plural pronouns. Our findings suggest that the mechanisms of loneliness include self-oriented cognitive activities (i.e., reading) and an overattention to the interpretation of information in the environment. These data-driven ecological findings suggest interventions for loneliness that target maladaptive social cognitions (e.g., through reframing the perception of social environments), strengthen social relationships, and treat other affective distress (i.e., depression).