Research suggests that experiences of discrimination and life stressors are associated with negative mental-health outcomes for Muslim populations in western countries. The current study reports on two meta-analyses based on 295 correlations from 130 unique samples and 27,725 individuals, examining the associations of discrimination and life stressors, both separately and jointly, with mental health. Discrimination was significantly associated with negative mental-health outcomes ( r s = .22–.23). Between-study variability in effects sizes was explained by discrimination level, mental-health outcome, number of discrimination measure items, and refugee status. Life stressors were also significantly associated with negative mental-health outcomes ( r s = .32–.37). Between-study variability in effect sizes was explained by publication bias, sample population, number of life stressor measure items, continent, and ethnicity. Both omnibus effect-size estimates were robust to tests of publication bias, outliers, and within-study dependence. Results suggest unique associations between both discrimination and life stressors with mental health. In the current sociopolitical climate, this study is an important step to better serve the mental health needs of the growing global Muslim community.

The past decade has seen rapid growth in research linking stable psychological characteristics (i.e., traits) to digital records of online behavior in Online Social Networks (OSNs) like Facebook and Twitter, which has implications for basic and applied behavioral sciences. Findings indicate that a broad range of psychological characteristics can be predicted from various behavioral residue online, including language used in posts on Facebook (Park et al., 2015) and Twitter (Reece et al., 2017), and which pages a person ‘likes’ on Facebook (e.g., Kosinski, Stillwell, & Graepel, 2013). The present study examined the extent to which the accounts a user follows on Twitter can be used to predict individual differences in self-reported anxiety, depression, post-traumatic stress, and anger. Followed accounts on Twitter offer distinct theoretical and practical advantages for researchers; they are potentially less subject to overt impression management and may better capture passive users. Using an approach designed to minimize overfitting and provide unbiased estimates of predictive accuracy, our results indicate that each of the four constructs can be predicted with modest accuracy (out-of-sample R ’s of approximately .2). Exploratory analyses revealed that anger, but not the other constructs, was distinctly reflected in followed accounts, and there was some indication of bias in predictions for women (vs. men) but not for racial/ethnic minorities (vs. majorities). We discuss our results in light of theories linking psychological traits to behavior online, applications seeking to infer psychological characteristics from records of online behavior, and ethical issues such as algorithmic bias and users’ privacy.