NAYEL @ LT - EDI - ACL 2022: Homophobia/Transphobia Detection for Equality, Diversity, and Inclusion using SVM (2022)

Abstract: Analysing the contents of social media platforms such as YouTube, Facebook and Twitter gained interest due to the vast number of users. One of the important tasks is homophobia/transphobia detection. This paper illustrates the system submitted by our team for the homophobia/transphobia detection in social media comments shared task. A machine learning-based model has been designed and various classification algorithms have been implemented for automatic detection of homophobia in YouTube comments. TF/IDF has been used with a range of bigram model for vectorization of comments. Support Vector Machines has been used to develop the proposed model and our submission reported 0.91, 0.92, 0.88 weighted f1-score for English, Tamil and Tamil-English datasets respectively.

## introduction the internet provides a wealth of information that is immensely useful for different reasons. due to the overwhelming information available on the internet, online social media platforms inspired a new epoch of "misinformation" by spreading incorrect or misleading information to delude users. social media platforms such as youtube, facebook, twitter, and other platforms initially became popular due to the social aspects that they allow users to post and share material to share their opinions and ideas on anything at any time. youtube is a popular platform that allows users to create their accounts, upload videos, and make comments. due to the massive audience, distributing negative or uncomfortable information has become easier. there is a need for developing tools for automatic detection of different behaviours such as fake news, sentiment analysis, hate speech, aggressive content and rumours. youtube is one of the most popular social media platforms, in which any user can share data about anything without any restrictions that data may contain scandalous data such as racist, homophobic, transphobic, and antilgbt+ propaganda. @xcite since misinformation spreads faster than factual news among people, we need to classify this information whether containing lgbt+ data or not. in the proposed system the dataset used is collected from youtube comments and divided into three datasets with various languages namely; english, tamil and mixed languages tamil-english. the proposed model uses a machine learning approach integrated with text vectorization to develop a system for automatic detection of homophobic or transphobic contents. ## background pathak et al. (2021) developed a machine learning based model for hate speech and offensive language detection. they used a multilingual dataset consisting of tweets and youtube comments written in malayalam, tamil and english. tf-idf and word embeddings were used for feature extraction phase. they trained different machine learning classifiers and they used 5-fold cross-validation approach to evaluate the performance of the classifiers. multinomial naive bayes (mnb) reported the best f1-score 77% for malayalam-english dataset, while support vector machines (svms) obtained the best f1-score 87% for tamil-english dataset. nayel (2020) used tf-idf as weighting scheme with a range of n-gram for feature extraction to implement stochastic gradient descent (sgd) algorithm for automatic offensive language detection in arabic tweets. @xcite developed a model for hate speech detection in multilingual contents using svm and multi-layer perceptron (mlp). tf-idf model as vector representation of collected tweets. svm reported the best f1-score for english dataset, while mlp reported the best f1-score for german and hindi languages. though much work has been done to identify offensive content in major languages such as english @xcite , it is a challenging task to identify and flag offensive content in lowresource languages because many users prefer to write their language in english script, a practise known as code-switching or code-mixing @xcite . ## dataset the dataset given for the shared task @xcite consists of youtube comments in three languages english, tamil and the remaining codemixed tamil-english. the dataset contains some unique features that distinguish it from prior hate speech or offensive language identification datasets the extracted comments including homophobic and non-anti-lgbt+ text. these comments have been scraped using a scraper tool and were collected between august 2020 and feb 2021. ## system overview in this section, we review the phases of the proposed model. the primary aim of this work is to explore the impact of different machine learning methods on automatic homophobia/transphobia detection in social media comments. the proposed model composite of the following phases: ## results and discussion ## conclusion in this work we implemented a machine learning model using svm as a classification algorithm for homophobia/transphobia detection in text. the comments have been represented as tf-idf vectors. applying more complex systems may improve the performance of the model. deep learning based models have various structure that can enhance the output. another word representation models such as word embeddings can be used as input for better representation.