Recent work has explored the use of personal information in the form of persona sentences or self-disclosures to improve modeling of individual characteristics and prediction of annotator labels for subjective tasks. The volume of personal information has historically been restricted and thus little exploration has gone into understanding what kind of information is most informative for predicting annotator labels. In this work, we categorize self-disclosures and use them to build annotator models for predicting judgments of social norms. We perform several ablations and analyses to examine the impact of the type of information on our ability to predict annotation patterns. Contrary to previous work, only a small number of comments related to the original post are needed. Lastly, a more diverse sample of annotator self-disclosures did not lead to the best performance. Sampling from a larger pool of comments without filtering still yields the best performance, suggesting that there is still much to uncover in terms of what information about an annotator is most useful for verdict prediction.

Most existing work on mental health prediction from language focuses on isolated posts, overlooking temporal dynamics in longitudinal timelines. We present McMaster NLP’s system for the CLPsych 2026 Shared Task, which centers on modeling mental health dynamics in social media timelines using the MIND framework~\cite{atzil_slonim_2025_mind}. The task comprises: (1) identifying adaptive and maladaptive self-state components within posts, (2) detecting moments of change in well-being, and (3) generating structured summaries. For self-state prediction, we leverage LLM-generated archetypal representations of language use as semantic anchors within a dual-encoder architecture, enabling interpretable prediction of subelements and their intensities through alignment with prototypical expressions of psychological states. For temporal dynamics, we use BiLSTM-based sequence models to detect moments of change. For summarization, we employ a prompt-based LLM to generate grounded, structured summaries emphasizing causal interactions and temporal progression of self-states. Finally, we analyze model failure modes with respect to human evaluation and identify directions for reconciling the MIND framework with how state-assessment models encode meaning.