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.