Detecting population-level mental health responses to global negative events through social media language remains understudied, despite its potential for public health surveillance. While pretrained language models (PLMs) have shown promise in mental health detection, their effectiveness in capturing event-driven collective psychological shifts – especially across diverse crisis contexts – is unclear. We present a prototype evaluation of three PLMs for identifying population mental health dynamics triggered by real-world negative events. We introduce two novel datasets specifically designed for this task. Our findings suggest that DistilBERT is better suited to the noisier global negative events data, while MentalRoBERTa shows the validity of the method on the Covid-19 tidier data. SHAP interpretability analysis of 500 randomly sampled posts revealed that mental-health related vocabulary (anxiety, depression, worthless) emerged as the most influential linguistic markers for mental health classification.