  This paper explores the correlation between linguistic diversity, sentiment
analysis and transformer model architectures. We aim to investigate how
different English variations impact transformer-based models for irony
detection. To conduct our study, we used the EPIC corpus to extract five
diverse English variation-specific datasets and applied the KEN pruning
algorithm on five different architectures. Our results reveal several
similarities between optimal subnetworks, which provide insights into the
linguistic variations that share strong resemblances and those that exhibit
greater dissimilarities. We discovered that optimal subnetworks across models
share at least 60% of their parameters, emphasizing the significance of
parameter values in capturing and interpreting linguistic variations. This
study highlights the inherent structural similarities between models trained on
different variants of the same language and also the critical role of parameter
values in capturing these nuances.
