Minority Stress

Minority stress research investigates the unique psychological burdens faced by marginalized groups due to their social identities, focusing on how these stressors impact mental health and well-being. Current research employs machine learning techniques, including emotion-infused models and graph contrastive learning applied to data like social media posts and EEG hyperscanning, to better understand and detect minority stress, particularly within communities like LGBTQ individuals. These advancements aim to improve mental health interventions and inform policies by revealing the nuanced emotional and neural processes underlying minority stress and its exacerbation during crises like pandemics.