SarcHope: Detection of Sarcasm in Social Media Hope Speech

Abstract

Hope speech, which is defined by statements
of optimism and encouragement, has a positive impact
on social media conversation and is critical in boosting
individual well-being, particularly among users who
are facing adversity, stress, worry, or illness. As a
result, the automatic identification of hopeful content
has arisen as an important research topic. However,
natural language processing (NLP) systems continue to
confront substantial hurdles in accurately detecting hope,
which can range from grounded optimism to extreme
wishfulness or even scathing sarcasm. To address these
issues, we propose SarcHope, a framework for sarcasm
detection in hope speech across English and Spanish.
Our approach involves re-labeling the IbertLEF-2025
dataset and fine-tuning state-of-the-art transformer-based
models. Evaluation on an independent test set shows that
DeBERTa-V3 obtains an F1-score of 0.8532 on English
data, and S-mmBERT yields a top F1-score of 0.8327 on
Spanish data. The results advance the field of natural
language processing and provide a valuable baseline for
subsequent studies.