IDIAP Submission@ LT - EDI - ACL 2022: Homophobia/Transphobia Detection in social media comments (2022)

Abstract: The increased expansion of abusive content on social media platforms negatively affects online users. Transphobic/homophobic content indicates hatred comments for lesbian, gay, transgender, or bisexual people. It leads to offensive speech and causes severe social problems that can make online platforms toxic and unpleasant to LGBT+people, endeavoring to eliminate equality, diversity, and inclusion. In this paper, we present our classification system; given comments, it predicts whether or not it contains any form of homophobia/transphobia with a Zero-Shot learning framework. Our system submission achieved 0.40, 0.85, 0.89 F1-score for Tamil and Tamil-English, English with ( 1^{st} , 1^{st} , 8^{th} ) ranks respectively. We release our codebase here: https://github.com/Muskaan-Singh/Homophobia-and-Transphobia-ACL-Submission.git .

## introduction and related work homophobic/transphobic @xcite content on social media intends to harm lesbian, gay, bi-sexual (lgb) (with labels such as 'fag', 'homo' or denigrating phrases such as 'don't be a homo,' 'that's so gay') @xcite @xcite . it is a type of abuse that involves physical violence such as killing, maiming, beating, or explicit sexual violence such as rape, molestation, penetration, or an invasion of privacy by disclosing personal information. some of the example phrases include "gays deserve to be shot dead," "someone should rape that lesbo to turn her into straight," "gays should be stoned," "you lesbos, i know where you live, i will visit you tonight," "beat the fag out of him," "you should kill yourself". online offensive language has been identified as a worldwide phenomenon diffused throughout social media platforms such as facebook, youtube, and twitter during the last decade @xcite . it is even more distressing for lesbian, gay, bisexual, transgender, and other (lgbt+) vulnerable individuals @xcite . because of who they love, how they appear, or who they are, lgbt+ people all across the globe are subjected to violence and inequity, as well as torture and even execution @xcite . sexual orientation and gender identity are essential components of our identities that should never be discriminated against or abused @xcite . however, in many countries, being identified as lgbt+ will cost lives, so the vulnerable individual goes to social media to get support or share their stories to find similar people @xcite . identifying such information from social media would eliminate the severe societal problem and prevent formulating online platforms toxic unpleasant to lgbt+ people while also attempting to eliminate equality, diversity, and inclusion. there are many rules and regulations to protect lgb persons, but they omit protection based on gender identity or expression or transgender adolescent experiences @xcite . lack of annotated data has restrained the research on homophobic and transphobic detection. @xcite find the linguistic behavior in lgbt+ for the chinese language. the research experiments present the traditional system's failure for complex dimensions to detect the gender from the text. @xcite curated lexicons in 2 shared task description in the shared task, participants are provided with comments extracted from social media foot_0 . the challenge was to predict whether or not it contains any form of homophobia/transphobia detection. the participants are provided with a seed data @xcite , sampled as in table : 1 respectively. the comments are manually annotated to show whether the text contains homophobia/transphobia. we also did reports data distribution across non-anti-lgbt+ content, homophobic, transphobic, for all the languages in ## proposed methodology this section presents the proposed methodology for classifying non-anti-lgbt+ content, homophobic, and transphobic content from social media posts. initially, we preprocess the comments for special characters, stopwords, emojis, and punctuation removal using nltk library @xcite . further, we extract the features, tokenize all the sentences and map the tokens to their word ids. for every sentence in the dataset, we follow a series of steps (i) tokenize the sentences (ii) prepend the [cls] token to the start (iii) append the [sep] token to the end (iv) map the token to their ids (v) pad or truncate the sentenced to max length (vi) mapping of attention masks for [pad] tokens. we padded and truncated the @xmath0. the generated sequence sentences are passed for encoding with its attention mask (simply differenti-ating padding from non-padding). afterward, we feed these embeddings for pretraining the xlm roberta @xcite . it significantly aims at cross-lingual transfer tasks for pre-trained multilingual language models. the model performs exceptionally well on low resource languages at a scale. the empirical analysis presents positive transfer and capacity delusion. further, the model also allows multilingual modeling without sacrificing per-language performance. it has shown competitive results with strong monolingual models on glue. after the pretraining, we fine-tune the model in the english language, and finally, we test on tamil and tamil-english languages. ## results we test our model for the dataset @xcite . the classification report for our proposed and the top-performing model over the test set can be seen in ## conclusion in this paper, we present our classification system; given comments, it predicts whether or not it contains any form of homophobia/transphobia with a zero-shot learning framework. our system submission achieved 0.40, 0.85, 0.89 f1-score for tamil and tamil-english, english with (1 st , 1 st ,8 th ) ranks respectively. we also performed a qualitative analysis. the system performs precisely on negative/positive phrases such as "fool" and "animals" rather than understanding the entire context of the comment. we intend to work on a multi-task learning framework to handle different kinds of homophobic/transphobic by capturing context in the future. we also aim to detect multilingual homophobic/transphobic comments in the code-mixing scenarios.