V erba V isor@Multimodal Hate Speech Event Detection 2023: Hate Speech Detection using Transformer Model (2023)

Abstract: Hate speech detection has emerged as a critical research area in recent years due to the rise of online social platforms and the proliferation of harmful content targeting individuals or specific groups.This task highlights the importance of detecting hate speech in text-embedded images.By leveraging deep learning models,this research aims to uncover the connection between hate speech and the entities it targets.

## introduction hate speech detection plays a crucial role in fostering a safer and more inclusive digital landscape. in today's interconnected world, where social media and online platforms dominate communication, the spread of hate speech can have far-reaching and detrimental consequences. detecting and addressing hate speech helps protect vulnerable communities from harm, prevents the escalation of conflicts, and promotes constructive dialogue.moreover, the integration of multimodal techniques, combining both textual and visual information, has further enhanced hate speech detection systems. in recent years, the detection of hate speech has witnessed significant advancements, driven by the rapid progress in natural language processing (nlp) and computer vision technologies. machine learning algorithms, particularly deep learning models, have revolutionized the field, allowing for more accurate and efficient hate speech detection. by analyzing text-embedded images and their associated textual content, algorithms can uncover hidden patterns and better identify hateful content targeted at specific entities or communities. this research paper introduces an investigation into the detection of hate speech and identifying hate speech targets by employing nlp transformers, specifically the albert base model. in @xcite the authors introduce a new dataset called iehate, comprising 11,457 manually annotated hindi tweets related to the indian assembly election campaign. they perform a comprehensive analysis of hate speech prevalence and its various forms in political discourse. the dataset is benchmarked using machine learning, deep learning, and transformer-based algorithms. among the models, roberta (multilingual) and bert (ham) achieved the highest f1-scores of 0.725 and 0.706, respectively. transformer-based models outperformed machine learning and deep learning models. tit ¸a and zubiaga (2021) research paper focuses on hate speech detection in a cross-lingual setting, emphasizing the importance of addressing this issue on global online platforms. the study utilizes fine-tuned altered multi-lingual transformer models (mbert, xlm-roberta) with cross-lingual training between english and french and within each language independently. the results indicate that multi-lingual bert outperforms xlm-roberta in two out of three language pairs, showing significantly higher macro average scores for both english-only and french-only data. however, the fine-tuned altered xlm-roberta performs poorly in the monolingual setting, with scores less than 0.5. the findings highlight the importance of selecting appropriate models and training strategies for effective hate speech detection in cross-lingual contexts. ## methodologies used in this study, we employed the deep learning model transformers, specifically the albert (a lite bert) base v1 and artificial neural network(ann). ## conclusion we constructed an albert base model to perform hate speech detection. preprocessing all the models with nltk was considered essential in creating a robust model. however, accurately gauging the emotion of social media posts depends on individual perception, making it challenging for conventional models to achieve high accuracy. another contributing factor to reduced accuracy is the imbalanced data distribution among the output class labels. to address these challenges, we plan to explore various transformer models and data augmentation techniques to enhance the performance of our hate speech detection system.