  Our study addresses a significant gap in online hate speech detection
research by focusing on homophobia, an area often neglected in sentiment
analysis research. Utilising advanced sentiment analysis models, particularly
BERT, and traditional machine learning methods, we developed a nuanced approach
to identify homophobic content on X/Twitter. This research is pivotal due to
the persistent underrepresentation of homophobia in detection models. Our
findings reveal that while BERT outperforms traditional methods, the choice of
validation technique can impact model performance. This underscores the
importance of contextual understanding in detecting nuanced hate speech. By
releasing the largest open-source labelled English dataset for homophobia
detection known to us, an analysis of various models' performance and our
strongest BERT-based model, we aim to enhance online safety and inclusivity.
Future work will extend to broader LGBTQIA+ hate speech detection, addressing
the challenges of sourcing diverse datasets. Through this endeavour, we
contribute to the larger effort against online hate, advocating for a more
inclusive digital landscape. Our study not only offers insights into the
effective detection of homophobic content by improving on previous research
results, but it also lays groundwork for future advancements in hate speech
analysis.
