Findings suggest that profiles which self-identify as Pro-ED express disordered eating patterns through tweets and have an audience of followers, many of whom also reference ED in their own profiles. ED socialization on Twitter might provide social support, but in the Pro-ED context this activity might also reinforce an ED identity.

Purpose Current trends suggest adolescents and young adults typically maintain a social media “portfolio” of several sites including Facebook and Twitter, but little is known regarding how an individual chooses to display risk behaviors across these different sites. The purpose of this study was to investigate college students’ displayed alcohol references on both Facebook and Twitter. Methods Among a larger sample of college students from two universities, we identified participants who maintained both Facebook and Twitter profiles. Data collection included evaluation of 5 months of participants’ Facebook and Twitter posts for alcohol references, number of social connections (i.e. friends or followers) and number of posts. Phone interviews assessed participants’ frequency of Facebook and Twitter use and self-reported alcohol use. Analyses included Fisher’s exact test, Wilcoxon matched pair sign test, Freidman rank-sum tests and logistic regression. Results Of 112 eligible participants, 94 (RR=84.8%) completed the study. Participants were more likely to display alcohol references on Facebook compared to Twitter (76% versus 34%, p=0.02). Participants reported more social connections on Facebook versus Twitter (average 801.2 friends versus 189.4 followers, p<0.001), and were more likely to report daily use of Facebook versus Twitter (94.6% versus 50%, p<0.001). Current alcohol use was predictive of both Facebook and Twitter displayed alcohol references, but mediators differed in each model. Discussion College students were more likely to display alcohol references on Facebook compared to Twitter. Understanding these patterns and predictors may inform prevention and intervention efforts directed at particular social media sites.

Introduction To develop and validate the PRIUSS-3 screening scale, a short scale to screen for Problematic Internet Use. Methods This scale development study applied standard processes using separate samples for training and testing dtatasets. We recruited participants from schools and colleges in 6 states and 2 countries. We selected 3 initial versions of a PRIUSS-3 using correlation to the PRIUSS-18 score. We evaluated these 3 potential screening scales for conceptual coherence, factor loading, sensitivity and specificity. We selected a 3-item screening tool and evaluated it in two separate testing sets using receiver operating curves (ROCs). Results Our study sample included 1079 adolescents and young adults. The PRIUSS-3 included 3 items addressing: 1) anxiety when away from the internet, 2) loss of motivation when on the internet, and 3) feelings of withdrawal when away from the internet. This screening scale had a sensitivity of 100% and specificity of 69%. A score of 3 or greater on the PRIUSS-3 was the threshold to follow-up with the PRIUSS-18. Discussion Similar to other clinical screens, the PRIUSS-3 can be administered quickly in a clinical or research setting. Positive screens should be followed by administering the full PRIUSS-18. Given the pervasive presence of the internet in youth's lives, screening and counseling for PIU can be facilitated by use of this validated screening tool.

Overweight individuals, and especially women, are disparaged as immoral, unhealthy, and low class. These negative conceptions are not intrinsic to obesity; they are the tainted fruit of cultural learning. Scholars often cite media consumption as a key mechanism for learning cultural biases, but it remains unclear how this public culture becomes private culture. Here we provide a computational account of this learning mechanism, showing that cultural schemata can be learned from news reporting. We extract schemata about obesity from New York Times articles with word2vec, a neural language model inspired by human cognition. We identify several cultural schemata that link obesity to gender, immorality, poor health, and low socioeconomic class. Such schemata may be subtly but pervasively activated by our language; thus, language can chronically reproduce biases (e.g., about weight and health). Our findings also reinforce ongoing concerns that machine learning can encode, and reproduce, harmful human biases.

Gender stereotypes have important consequences for boys’ and girls’ academic outcomes. In this article, we apply computational word embeddings to a 200-million-word corpus of American print media (1930-2009) to examine how these stereotypes changed as women’s educational attainment caught up with and eventually surpassed men’s. This transformation presents a rare opportunity to observe how stereotypes change alongside the reversal of an important pattern of stratification. We track six stereotypes that prior work has linked to academic outcomes. Our results suggest that stereotypes of socio-behavioral skills and problem behaviors—attributes closely tied to the core stereotypical distinction between women as communal and men as agentic—remained unchanged. The other four stereotypes, however, became increasingly gender-polarized: as women’s academic attainment increased, school and studying gained increasingly feminine associations, whereas both intelligence and unintelligence gained increasingly masculine ones. Unexpectedly, we observe that trends in the gender associations of intelligence and studying are near-perfect mirror opposites, suggesting that they may be connected. Overall, the changes we observe appear consistent with contemporary theoretical accounts of the gender system that argue that it persists partly because surface stereotypes shift to reinterpret social change in terms of a durable hierarchical distinction between men and women.

Public culture is a powerful source of cognitive socialization; for example, media language is full of meanings about body weight. Yet it remains unclear how individuals process meanings in public culture. We suggest that schema learning is a core mechanism by which public culture becomes personal culture. We propose that a burgeoning approach in computational text analysis – neural word embeddings – can be interpreted as a formal model for cultural learning. Embeddings allow us to empirically model schema learning and activation from natural language data. We illustrate our approach by extracting four lower-order schemas from news articles: the gender, moral, health, and class meanings of body weight. Using these lower-order schemas we quantify how words about body weight “fill in the blanks” about gender, morality, health, and class. Our findings reinforce ongoing concerns that machine-learning models (e.g., of natural language) can encode and reproduce harmful human biases.

Computational models to detect mental illnesses from text and speech could enhance our understanding of mental health while offering opportunities for early detection and intervention. However, these models are often disconnected from the lived experience of depression and the larger diagnostic debates in mental health. This article investigates these disconnects, primarily focusing on the labels used to diagnose depression, how these labels are computationally represented, and the performance metrics used to evaluate computational models. We also consider how medical instruments used to measure depression, such as the Patient Health Questionnaire (PHQ), contribute to these disconnects. To illustrate our points, we incorporate mixedmethods analyses of 698 interviews on emotional health, which are coupled with selfreport PHQ screens for depression. We propose possible strategies to bridge these gaps between modern psychiatric understandings of depression, lay experience of depression, and computational representation.

In this article, we apply computational word embeddings to a 200-million-word corpus of American print media (1930–2009) to examine how education-relevant gender stereotypes changed as women’s educational attainment caught up with and eventually surpassed men’s. This case presents a rare opportunity to observe how cultural components of the gender system transform alongside the reversal of an important pattern of stratification. We track six stereotypes that prior work linked to academic outcomes. Our results suggest that stereotypes most closely tied to the core stereotypical distinction between women as communal and men as agentic remained unchanged. The other stereotypes we tracked, however, became increasingly gender polarized: as school and studying gained feminine associations, intelligence and unintelligence gained masculine ones. Unexpectedly, we observe that trends in the gender associations of intelligence and studying are near-perfect mirror opposites, suggesting an interrelationship. We use these observations to further elaborate contemporary theoretical accounts of the gender system, arguing that this system persists partly because stereotypes shift to reinterpret social change in terms of a durable hierarchical distinction between women and men.