SSNT ech2@ LT - EDI -2023: Homophobia/Transphobia Detection in Social Media Comments Using Linear Classification Techniques (2023)

Abstract: The abusive content on social media networks is causing destructive effects on the mental well-being of online users. Homophobia refers to the fear, negative attitudes and feeling towards homosexuality. Transphobia refer to negative attitudes, hatred and prejudice towards transsexual people. Even though, some parts of the society have started to accept homosexuality and transsexuality, there are still a large set of the population opposing it. Hate speech targeting LGBTQ+ individuals, known as homophobia/transphobia speech, has become a growing concern. This has led to a toxic and unwelcoming environment for LGBTQ+ people on online platforms. This poses a significant societal issue, hindering the progress of equality, diversity, and inclusion. The identification of homophobic and transphobic comments on social media platforms plays a crucial role in creating a safer environment for all social media users. In order to accomplish this, we built a machine learning model using SGD and SVM classifier. Our approach yielded promising results, with a weighted F1-score of 0.95 on the English dataset and we secured 4th rank in this task.

## introduction in the recent years, people have started to discover themselves and open up about homosexuality and trans-sexuality. people have the complete freedom to choose their sexuality from the different choices. the widespread use of social media platforms has revolutionized communication and provided a space for individuals to express their thoughts, opinions, and experiences. the development of social media and support by the respective communities have helped in providing the people the courage to express themselves. however, this has also led to the increase in hate speech and discriminating comments towards this set of people. even after several laws and regulations, lgbtqia+ communities are constantly facing violations against them in various forms. homophobia and transphobia, which involve negative attitudes, prejudices, and discriminatory behaviors towards lgbtqia+ individuals, have gained prominence as pressing issues within online platforms. the proliferation of such toxic comments not only inflicts harm upon the targeted individuals but also fosters an environment of fear, intolerance, and exclusion. in response to this escalating problem, the utilization of machine learning (ml) techniques has garnered significant attention. ml algorithms offer the potential to automatically detect and classify homophobic and transphobic content in social media comments, enabling swift intervention and mitigating the detrimental impact on the affected individuals. this research paper aims to provide a comprehensive study of the application of ml algorithms for identifying homophobic and transphobic comments on social media platforms. the primary objective is to develop an efficient and accurate model that can autonomously identify and flag such discriminatory content, contributing to the establishment of a safer and more inclusive online environment for lgbtqia+ individuals. this paper involves analysing different approaches for classifying the english dataset of social media comments into three categories: homophobic, transphobic, and non-anti-lgbt+ content, as part of the shared task homophobia/transphobia detection @lt-edi@ranlp-2023. furthermore, we will discuss about the various methodologies used to process the data, implement the ml model and finally we will take a look into the outcome of the model and future developments. ## related work research on online hate speech has predominantly centered around aggression, sexism, racism, offensive language, hatred, and harassment, with limited emphasis on identifying specific instances of homophobic and transphobic speech @xcite . noteworthy studies include an examination of linguistic patterns among homosexual individuals in china using a created corpus (4). emotion lexicons have been developed to discern acceptable and unacceptable discourse concerning lgbtq+ topics in languages such as english, croatian, dutch, and slovene (3). a study analyzed hate speech comments related to lgbtq+ issues on facebook, highlighting prevalent themes like repulsion towards the lgbtq+ community and discrediting of journalistic information (2). furthermore, a manually annotated corpus was compiled from youtube, encompassing homophobic and transphobic speech in multiple languages, including english, tamil, and code-mixed tamil-english, with the aim of categorizing speech at various levels @xcite . ( 12 ) used the bert model to detect offensive language as a first level of identification of abusive content. while psychological studies have examined aspects like homophobic bullying and the impact on mental health, there remains a requirement for empirical evidence and theoretical comprehension of online homophobia/transphobia and its association with internet usage (5; 7). regarding hate speech detection in code-mixed settings, researchers have started to extract codemixed data from social media platforms due to increased user engagement. however, countries like india, with multiple languages spoken, face a scarcity of pertinent data in low-resourced languages @xcite . (10) explored the significance of multitask learning for identifying offensive language and performing sentiment analysis in closely related code-mixed languages like kannada, malayalam, and tamil. sivanaiah et al. (6) used a deep learning model to identify misogynous content against women using multimodal data. biradar et al. @xcite (2022) utilized translational systems to convert texts to english and fine-tuned language models for hate speech classification in hinglish (hindi-english). however, these approaches may not fully capture contextual nuances and accurately interpret sarcasm. additionally, pretrained language models encounter challenges in capturing contextual relationships between codemixed languages @xcite . in summary, although research exists on identifying and addressing online hate speech, there is a need for more focused investigations into homophobic and transphobic speech. additionally, effectively detecting hate speech in code-mixed settings requires further attention to develop precise and context-aware classification models. ## dataset used the dataset provided in task a of the homophobia/transphobia comments detection in the lt-edi@ranlp-2023 is used in this work. the dataset consists of 3164 entries under 3 labels namely homophobia, transphobia and non-anti-lgbt+ content. the number of entries under each dataset are 179 for homophobia, 8 for transphobia and 2978 for non-anti-lgbt+ content. ## methodologies we have experimented with multi-class linear classifier ml algorithms for classifying the text into various category of the output class labels. the algorithms are explained in the following sections. ## implementation the above model implementation involves the following methodologies: the following diagram provides a visual flowchart of the implementation steps. ## conclusion and future works in this research, we conducted a comparative analysis of various models for the shared task on homophobia/transphobia detection at lt-edi@ranlp-2023. our findings revealed that the sgd classifier yielded the most favourable results for english text. the current dataset was skewed and the prediction scores were low for transphobia text. we may further train the model with enhanced datasets with more data and category labels to get more accuracy. the model was trained for monolingual text (english). extending this, a machine model to detect multilingual text can be built. we are also aiming to investigate on the data augmentation techniques for this specific case.