  The proliferation of LLMs in various NLP tasks has sparked debates regarding
their reliability, particularly in annotation tasks where biases and
hallucinations may arise. In this shared task, we address the challenge of
distinguishing annotations made by LLMs from those made by human domain experts
in the context of COVID-19 symptom detection from tweets in Latin American
Spanish. This paper presents BrainStorm @ iRELs approach to the SMM4H 2024
Shared Task, leveraging the inherent topical information in tweets, we propose
a novel approach to identify and classify annotations, aiming to enhance the
trustworthiness of annotated data.
