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Strong end-user security practices benefit both the user and hosting platform, but it is not well understood how companies communicate with their users to encourage these practices. This paper explores whether web companies and their platforms use different levels of language formality in these communications and tests the hypothesis that higher language formality leads to users’ increased intention to comply. We contribute a dataset and systematic analysis of 1,817 English language strings in web security and privacy interfaces across 13 web platforms, showing strong variations in language. An online study with 512 participants further demonstrated that people perceive differences in the language formality across platforms and that a higher language formality is associated with higher self-reported intention to comply. Our findings suggest that formality can be an important factor in designing effective security and privacy prompts. We discuss implications of these results, including how to balance formality with platform language style. In addition to being the first piece of work to analyze language formality in user security, these findings provide valuable insights into how platforms can best communicate with users about account security. 

Mental health stigma prevents many individuals from receiving the appropriate care, and social psychology studies have shown that mental health tends to be overlooked in men. In this work, we investigate gendered mental health stigma in masked language models. In doing so, we operationalize mental health stigma by developing a framework grounded in psychology research: we use clinical psychology literature to curate prompts, then evaluate the models’ propensity to generate gendered words. We find that masked language models capture societal stigma about gender in mental health: models are consistently more likely to predict female subjects than male in sentences about having a mental health condition (32% vs. 19%), and this disparity is exacerbated for sentences that indicate treatment-seeking behavior. Furthermore, we find that different models capture dimensions of stigma differently for men and women, associating stereotypes like anger, blame, and pity more with women with mental health conditions than with men. In showing the complex nuances of models’ gendered mental health stigma, we demonstrate that context and overlapping dimensions of identity are important considerations when assessing computational models’ social biases.