We present UNF-BMI system for SemEval-2026 Task 3, Track A, Subtask 1 (Dimensional Aspect Sentiment Regression, DimASR), which focuses on predicting continuous Valence–Arousal (VA) scores for aspects in text. Our approach integrates psychologically grounded affective signals inspired by the Research Domain Criteria (RDoC) framework. We investigate two complementary methods: first, an in-context learning framework using Mistral-7B-Instruct with semantically retrieved few-shot examples augmented by lexicon-derived RDoC valence and arousal cues; second, a supervised multi-task learning model based on RoBERTa, where VA regression is the primary objective and RDoC-based positive/negative signal prediction serves as an auxiliary task to regularize shared representations. Experiments on english laptop and restaurant review datasets demonstrate that incorporating RDoC-inspired affective priors reduces RMSE compared to baselines, particularly in low-signal text where explicit sentiment cues are sparse.

Accurate linguistic annotation is crucial for creating high-quality datasets in specialized domains, yet manual labeling is often slow, expensive, and inconsistent. We present a reproducible workflow for evaluating the effectiveness of large language models (LLMs) as annotators of domain-specific health misinformation on social media. Using a data set of 169 Instagram posts on seed oils, expert nutritionists provided gold-standard labels (71% positives), which we compared against the outputs of five open-source LLMs. We introduce a hierarchical error taxonomy that categorizes LLM misclassifications according to the direction, mechanism, and contributing factors of the error, providing interpretable insights into model failures. Our analysis reveals systematic error patterns, including misinterpretation of nuanced claims and overconfidence in predictions, highlighting conditions under which LLM annotations do not align with expert judgment. Although the data set is modest in size and exhibits class imbalance, it reflects real-world distributions of nutrition-related Instagram content and motivates the need for a careful evaluation of the robustness of the LLM annotation. This study has implications for the development of frameworks for automated LLM-based annotators in the health and nutrition domains, as well as LLM developers in general.

Nutrition misinformation on social media often arises from selective interpretation of scientific evidence rather than outright falsehoods, making it difficult to detect. We introduce a curated, expert-annotated Instagram dataset focused on seed oils and omega-6, two domains characterized by contested dietary claims. We evaluate feature-based, embedding-based, and transformer-based models under in-domain and cross-domain settings. Results show strong in-domain performance across all models, with Sentence-BERT achieving the highest AUPRC (up to 0.96). However, performance drops substantially under cross-domain transfer, indicating limited robustness to topic shift. Analysis suggests that while contextual embeddings capture strong in-domain semantic signals, linguistically and psychologically grounded features are more stable under distribution shift. These findings highlight the value of combining semantic and interpretable linguistic signals for robust misinformation detection.