dkit@ LT - EDI -2024: Detecting Homophobia and Transphobia in E nglish Social Media Comments (2024)

Abstract: Machine learning and deep learning models have shown great potential in detecting hate speech from social media posts. This study focuses on the homophobia and transphobia detection task of LT-EDI-2024 in English. Several machine learning models, a Deep Neural Network (DNN), and the Bidirectional Encoder Representations from Transformers (BERT) model have been trained on the provided dataset using different feature vectorization techniques. We secured top rank with the best macro-F1 score of 0.4963, which was achieved by fine-tuning the BERT model on the English test set.

## introduction the increase in popularity of social media has fostered hate speech in online discourse @xcite . social media posts produce a great volume of data which can be hard to moderate manually. artificial intelligence tools have proven to be useful in combating trolling @xcite , misinformation, cyberbullying @xcite , etc. specifically, large language models (llms) such as bert, cross-lingual roberta (xlm-roberta) @xcite , and multilingual representations for indian languages (muril) @xcite have been used in recent studies to counter different types of hate speech @xcite ; @xcite . the lesbian, gay, bisexual, and transgender (lgbt+) community has been a prominent target for online hate speech in the past @xcite . homophobia is the expression of hate and negative attitudes towards people who identify as homosexuals. transphobia is the expression of negative beliefs towards people who identify as transgenders. it is imperative to filter such toxic and abusive language towards the lgbt+ community, as it can be the cause of severe psycholog-ical distress, and can silence their online voices. very few datasets are available online for homophobia and transphobia detection in code-mixed languages such as malayalam and hindi @xcite . in recent years, shared tasks have been conducted to promote research for different types of hate speech such as misogyny (automatic misogyny identification) @xcite , hate speech in lowresource languages (hate speech and offensive content identification in english and indo-aryan languages) @xcite , and code-mixed languages @xcite . in this study, we focus on our participation in the lt-edi-2024 shared task, which was was the detection of homophobia and transphobia from social media comments foot_0 . we have selected english data set for the task @xcite . the provided datasets were converted into feature vectors using techniques such as term frequency -inverse document frequency (tf-idf), count vectorizer, word2vec. machine learning and deep learning models were then trained and evaluated on the datasets using empirical metrics such as accuracy, macro-f1 score, etc. the rest of the article is structured as follows: section 2 discusses the relevant literature in sentiment analysis and hate speech detection. section 3 provides details of the dataset, and the steps involved in the experiment such as feature vectorization, model training, fine-tuning, and evaluation. section 4 discusses the results and findings of the study, and section 5 concludes the study. ## related works in this section, we will discuss the relevant literature and previous work conducted in sentiment analysis and hate speech detection. ## methodology in the section, the methodology used in the task is discussed. ## results and analysis in this section, we will discuss the results of the experiment and analyse the findings. ## conclusion in this study, homophobia and transphobia detection in english is conducted using different machine learning models, a dnn, and bert. the highest macro-f1 score achieved is 0.4963 using the bert model through simple fine-tuning. transformer-based models have outperformed traditional machine learning models in this task of homophobia and transphobia detection. further exploration can be carried out for online inclusivity through experimentation on different datasets and more complex model architectures.