Zavira@ D ravidian L ang T ech 2024: T elugu hate speech detection using LSTM (2024)

Abstract: Hate speech is communication, often oral or written, that incites, stigmatizes, or incites violence or prejudice against individuals or groups based on characteristics such as race, religion, ethnicity, gender, sexual orientation, or other protected characteristics. This usually involves expressions of hostility, contempt, or prejudice and can have harmful social consequences.Among the broader social landscape, an important problem and challenge facing the medical community is related to the impact of people’s verbal expression. These words have a significant and immediate effect on human behavior and psyche. Repeating such phrases can even lead to depression and social isolation.In an attempt to identify and classify these Telugu text samples in the social media domain, our research LSTM and the findings of this experiment are summarized in this paper, in which out of 27 participants, we obtained 8th place with an F1 score of 0.68.

## introduction while hate speech (hs) legislation varies among different countries, it is generally conceptualized as encompassing expressions of hostility or derogation directed at an individual or a group based on attributes such as race, color, national origin, sex, disability, religion, or sexual orientation @xcite . on platforms such as twitter, facebook, and various other social media outlets, hateful comments manifest as expressions containing abusive language directed towards individuals (including cyber-bullying, politicians, celebrities, or products) or specific groups (such as countries, the lgbt community, religions, genders, organizations, etc) @xcite numerous intricate challenges are currently evident in applications related to speech, vision, and text, all aimed at enhancing accuracy. the pioneering work of @xcite . in 2017 marks the initial exploration of neural architectures for detecting hate speech. the advancement of natural language processing (nlp) @xcite technology has spurred considerable investigation into the automated detection of textual hate speech in recent years. notable competitions such as semeval-2019 @xcite and 2020 @xcite , as well as germeval-2018 @xcite , have organized diverse events aimed at seeking improved solutions for automated hate speech detection. in response, researchers have compiled extensive datasets from various sources, fostering significant progress in the field. numerous studies have addressed hate speech in multiple non-english languages and online communities, prompting exploration and comparison of different processing pipelines. this includes the examination of feature sets and machine learning (ml) methods @xcite , encompassing supervised, unsupervised, and semi-supervised approaches, as well as various classification algorithms such as naive bayes, logistic regression (lr), convolutional neural network (cnn) @xcite , long short-term memory (lstm), bert deep learning @xcite architectures, among others. the pervasive issue of abusive language is both common and troubling. offensive language takes many forms, depending on the target group and the specific target, such as hate speech, cyberbullying, adult content, trolling, abuse, racism, or profanity. in recent advancements, transformer-based models @xcite , such as bert, have significantly impacted the detection and understanding of hate speech. hate speech, a particularly alarming category of abusive language, involves the intentional intimidation of a target group or individual with the intent of causing harm, violence, or social disruption @xcite so there are subtle distinctions between different types of offensive language. the targeting of lgbt+ people with hate speech is a deep-rooted issue with far-reaching consequences, including the potential for substance abuse disorders @xcite and racism @xcite . the rest of the paper is organized as follows: the related work and methodology are discussed in section 2 and 3 respectively followed by results in section 4. ## related work balouchzahi et al. address the ongoing challenge of hate speech (hs) by emphasizing the limitations of conventional identification and blocking methods. thay advocate the development of systems that are capable of not only identifying but also profiling hs content contaminants. using a vote classifier (vc) contributes to the hate speech broadcaster detection task organized by pan 2021 @xcite , which focuses on the profiles of hs broadcasters in english and spanish on twitter. the proposed model uses a combination of traditional character and word n-gram along with syntactic n-grams as features for classification. using a support vector machine (svm), logistic regression (lr) and random forest (rf) vote classifier, the models achieve commendable accuracies of 73% and 83% for english and spanish, respectively. in the bichat @xcite research, an innovative deep learning @xcite model, combining bilstm with deep cnn and hierarchical attention, is employed to acquire tweet representations for the detection of hate speech. the proposed model undergoes a process of mining, training, and evaluation using three benchmark datasets from twitter. these datasets include hd1, introduced by @xcite , hd2, derived from the kaggle foot_0 competition dataset, and hd3 with statistics presented in in the publication @xcite , an examination was conducted on 16,000 tweets employing three neural network models (cnn and bowl, lstm) and various methodologies, includ-ing gbdt, tf_idf, and random embedding. the dataset originates from the @xcite . the study demonstrated that combining embeddings acquired from deep neural network models with gradient-boosted decision trees yields the highest accuracy values. specifically, the combination of lstm+random embedding+gbdt achieved an f1-score of 0.930. in this study @xcite , the method is based on a dataset of 16,000 tweets collected by @xcite and colleagues. this dataset, which includes a total of 136,052 tweets, was annotated by the researchers, and 16,914 tweets were specifically flagged. of these, 3,383 tweets containing sexual content were identified, originating from 613 users. additionally, 1,972 tweets were flagged for racist content and contributed by 9 users, while the remaining 11,559 tweets were deemed non-sexist or racist. the analysis of hate speech comments included a thorough review of the features used, with the aim of determining those that yielded the most effective detection performance. notably, examination of the features influencing hate speech recognition in the dataset revealed that, despite potential variations in geographic distribution and word length, these factors did not consistently improve performance and rarely outperformed personality-level features. an exception to this trend can be seen with gender-related characteristics, as detailed in ## result during the sharing task competition that focused on detecting hate and offensive language in telugu mixed code text, our main goal was to determine the f1-score for the given data set. using the previously trained lstm model, we fed the entire test data into the model and obtained a prediction that yielded significant results. in a single performance evaluation, we scored an admirable 0.68%, placing 8th out of 27 participating teams. for an overview of the results achieved by all participating teams, please refer to ## conclusion hate speech that incites violence or prejudice against individuals or groups based on different characteristics is an important challenge in contemporary society. the damaging effects of such expressions, including hostility and prejudice, go beyond immediate social consequences and can contribute to deep psychological effects such as depression and social isolation. this research deals with the important issue of hate speech in the context of telugu mixed code text on social media platforms. using natural language processing (nlp), specifically using shortterm memory (lstm) neural networks, we aimed to identify and classify hate speech in telugu text samples. in a competitive environment of 27 participants, our lstm-based model achieved eighth place with an f1 (large) score of 0.68 the significance of our research lies in the effective application of nlp techniques to combat hate speech in multilingual contexts, contributing valuable insights and solutions to a pervasive social problem. the balanced dataset, consisting of 4000 training tweets and 500 test tweets, provides a strong foundation for training and evaluating the model's performance. our findings underscore the potential of advanced technologies, such as deep learning models, in addressing complex social issues. the results of the competition presented in