A ni MOJ ity:Detecting Hate Comments in I ndic languages and Analysing Bias against Content Creators (2022)

Abstract: Online platforms have dramatically changed how people communicate with one another, resulting in a 467 million increase in the number of Indians actively exchanging and distributing social data. This caused an unexpected rise in harmful, racially, sexually, and religiously biased Internet content humans cannot control. As a result, there is an urgent need to research automated computational strategies for identifying hostile content in academic forums. This paper presents our learning pipeline and novel model, which classifies a multilingual text with a test f1-Score of 88.6% on the Moj Multilingual Abusive Comment Identification dataset for hate speech detection in thirteen Indian regional languages. Our model, Animojity, incorporates transfer learning and SOTA pre- and post-processing techniques. We manually annotate 300 samples to investigate bias and provide insight into the hate towards creators.

## introduction with the unbridled spread of internet culture, hopes of finding an accepting community have led many racially, culturally, and sexually diverse groups to take refuge in their corner of the internet, showcasing the strength of online forums. however, those seeking to spread hateful content look for ways to circumvent the restrictions placed on social media and hinder their healthy development. due to the societal concern hate speech has garnered, there is a strong motivation to make advancements in its automatic detection. online hate speech in general, and gendered online hate speech in particular, have become an issue of growing concern in both social and professional discourses. before we delve into detecting hate speech, it is imperative to understand its definition clearly. @xcite believes that a distinct definition of hate speech can make the annotation process * equal contribution easier, leading to reliable detection of what categorizes as offensive. nevertheless, hate speech and appropriate free expression walk a fine line, making its definition not fully agreeable. we opt to build upon existing definitions laid down by @xcite , @xcite , and @xcite instead of proposing a specific definition. another issue that is not brought to light as often is that users with a diverse linguistic backgrounds tend to switch between different languages while expressing their thoughts on social media, limiting the capabilities of a monolingual model and necessitating the need for multilingualism in a model. we address this challenge by building a novel model that detects hate comments for thirteen regional languages (hindi, urdu, telegu, marathi, gujarati, malayalam, punjabi, assamese, kannada, bengali, tamil, rajasthani, haryanvi) using the moj multilingual abusive comment identification dataset. while working with multilingual data, apart from a low resource issue, there is a tendency for imbalanced sample distribution. given that there is a relatively lower number of samples categorized as hateful in less-used languages, it encourages us to adopt transfer learning, data augmentation, and other techniques in animojity. we base our study on detecting hateful comments for indian regional languages and analyzing whether they have a biased perspective in the comments towards a particular community. our main contributions are the remainder of the paper is structured as follows. section 2 looks at some of the similar works in this domain. section 3 describes the training dataset, followed by an in-depth presentation of our methodology incorporating data pre-processing and model architecture in section 4. next, in sections 5 and 6, the experimental setup and results are explored, followed by section 7, where we analyze bias in comments and provide a detailed overview of our findings. we conclude with section 8, discussing future avenues for our proposed model. ## related work this section briefly sheds light on the various methodologies adopted to tackle hate speech detection and multilingual text classification over the past few years, serving as a benchmark for our research. hate speech detection hate speech detection has been at the center of the academic community's attention due to its constantly evolving and picking up different forms with time. the problem categorizes itself as binary or multi-class classification. @xcite created a threeclass twitter dataset annotated as sexist, racist, and neutral for offensive language detection. @xcite showcased their findings on an aggression identification task discriminating 15,000 annotated facebook posts and comments in english and hindi as non-aggressive, covertly aggressive, and overly aggressive. @xcite presented a 24,000 corpus for identifying english tweets belonging to profanity, hate speech, and nonoffensive categories. @xcite gave a detailed account of offensive language identification where three datasets available for hindi, german, and english were created from twitter and facebook. @xcite and @xcite presented their results in several languages obtained from the semeval competition. multilingual text classification multilingual text classification (mtc) aims to breach the language barrier by improving monolingual models by scaling to different languages. @xcite introduced the implementation of translating documents to a universal language for classification, which was bolstered by @xcite to extract grammatical and semantic features from the translated dataset before classification. however, the noise accumulated by translation errors creates a disparity in the final results. @xcite combined two semi-supervised learning techniques, co-regularization, and consensusbased self-training, to investigate multilingual text classification on a dataset containing five different languages: english, german, french, italian, and spanish. @xcite studied mtc in spanish, italian, and english by using the ngram technique and naïve bayes to predict the language of a document in classification. (bentaallah and malki, 2012) compared two wordnet-based approaches for multilingual text categorization. one relies on the wordnet associated with each language while excluding the translation, and the other focuses on a dis-ambiguation strategy to focus on the most common meaning of the word and access wordnet using a machine translation. data augmentation ( @xcite ) and transfer learning @xcite help combat situations where there is a lack of training data, both of which are adopted to improve our training data. recently, promising techniques involving deep learning and contextual embeddings have spearheaded a dynamic shift in the approach to tackling mtc tasks. transformers became a mainstay in crosslingual tasks and ushered in mbert @xcite , a multilingual masked language model, and xlm @xcite . @xcite proposed a multilingual lm, muril, specifically built for indic languages. ## dataset the dataset utilized in this study was made available by the moj multilingual abusive comment identification challenge organizers in partnership with iiit-d as part of that challenge foot_0 . given the natural language and contextual user data, the project aims to combat abusive comments on moj, one of india's largest short-form video apps, in thirteen languages, as shown in figure 1 . figure 2 shows the distribution of which language contains the most hateful comments. ## methodology this section documents the techniques used to achieve the study's main objective. ## experimental setup this section elaborates on the baselines, modules, and functions used to construct animojity. as discussed in related work, we used m-bert @xcite as our baseline model, which has been the golden standard for multilingual text classification tasks. using m-bert as our backbone, we experimented with different architectures, among which the instances where we found promising results are seen in 1. however, the primary limitation with m-bert was the lack of support for low-resource languages, which led us towards muril @xcite since it has been trained on a wide assortment of indian regional languages to improve downstream nlp tasks. however, the most significant update xlm-roberta offers over a model confined to a limited amount of training data, like muril, is the significantly increased amount of training data, which in conjunction with the masked language modelling approach discussed in section 4.2, cements it as state of the art. we first split our task into two pipelines: learning and testing, where the training (learning) process is carried out twice before making predictions on the test set as explained in algorithm 1 below. we employ two models showing state-of-the-art results on multilingual classification, mbert, and xlm-r (conneau and lample, 2019) as baselines for our binary classification task. while working on these transformer-based models, we came across three types of inputs: input-id, token type id, and attention mask, of which we tested different combinations. the output (last-hidden-state) obtained from these models is passed as an input to the classification head. the models implemented in this study are created using python 3.10.0 with tensorflow v2.6.1 as the deep learning framework to build the architecture and train on graphical processing unit (gpu tesla p100 16gb) servers of kaggle. to evaluate the performance of our system, we conduct experiments comparing different models. we created and trained our models using tensorflow and keras after dividing the dataset into a 90/10 ratio. accuracy and f1 scores are used as the evaluation criteria. we assess the outcomes of the architecture shown in ## results in this section, we describe the evaluation results obtained after testing each language and briefly examine the performance of different models. practical findings by comparing the proposed model with benchmarks, we demonstrate the effectiveness of our architecture. theoretical findings our suggested methodology performed very well when applied to comments where a nasty word or phrase guided the user's intent after using animojity to categorize remarks as offensive or inoffensive. there were, however, a few instances where someone employing a slang phrase or colloquialism in a humorous or referential manner was mistakenly tagged as hateful because of a significant constraint while working on hate speech detection: the accurate classification of "hate." ## analysing bias in comments hate comments against content creators that are defined as defamatory statements intended to portray the artists are unfavorably within the broad category of offensive content on the internet. a statement is discriminatory if it targets a person belonging to a particular social group segment for discriminatory reasons. for instance, targeting a specific gender, color, or religion might cause bias. to shed light on these biased attacks, we annotate 300 samples described in ## conclusion & future work any negative statement based on identification (such as gender, caste, or religion) rather than comments supporting the formation of an inclusive community should be avoided. to achieve this goal, we test various deep learning approaches on the moj multilingual abusive comment identifi-cation dataset having thirteen distinct regional languages and constructed a model that outperforms our baselines. to advance our research, we manually annotate 300 data points with bias labels ( gender, clothing, age, religion, race ). a common thread that ties together other hateful comments and biases observed is the reference to clothes and how people perceive clothes as being inappropriate. suppose we follow this through-line of hate geared towards the choice of clothing. in that case, the fact that most hateful comments are targeted towards female influencers hearkens to a societal issue of objectification and dehumanization that makes women prone to attacks and libel. the model architecture can be improved in the future by testing other feature selection methods, elevating its overall performance while working with code-mixed languages. second, research has shown the importance of context for hate speech classification. certain cases arise where there is a lack of contextual information, causing our best model to misclassify specific entries where even humans would struggle. this may be mitigated by developing a more robust pipeline by incorporating steps such as co-reference analysis and sarcasm detection. third, to comprehend where the model fails, there is a need for a detailed investigation of false positives and negatives. furthermore, the research may be further carried out to analyze bias on other social media platforms.