Year: 2026
Type: conference-paper
Abstract: 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 indomain 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.
Institution: University of North Florida
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
SDG: Zero hunger
Open Access status: gold
Funder: University of North Florida
OpenAlex ID: W7166832091