  Online discussions, panels, talk page edits, etc., often contain harmful
conversational content i.e., hate speech, death threats and offensive language,
especially towards certain demographic groups. For example, individuals who
identify as members of the LGBTQIA+ community and/or BIPOC (Black, Indigenous,
People of Color) are at higher risk for abuse and harassment online. In this
work, we first introduce a real-world dataset that will enable us to study and
understand harmful online conversational content. Then, we conduct several
exploratory data analysis experiments to gain deeper insights from the dataset.
We later describe our approach for detecting harmful online Anti-LGBTQIA+
conversational content, and finally, we implement two baseline machine learning
models (i.e., Support Vector Machine and Logistic Regression), and fine-tune 3
pre-trained large language models (BERT, RoBERTa, and HateBERT). Our findings
verify that large language models can achieve very promising performance on
detecting online Anti-LGBTQIA+ conversational content detection tasks.
