SSN - NLP - ACE @Multimodal Hate Speech Event Detection 2023: Detection of Hate Speech and Targets using Logistic Regression and SVM (2023)

Abstract: In this research paper, we propose a multimodal approach to hate speech detection, directed towards the identification of hate speech and its related targets. Our method uses logistic regression and support vector machines (SVMs) to analyse textual content extracted from social media platforms. We exploit natural language processing techniques to preprocess and extract relevant features from textual content, capturing linguistic patterns, sentiment, and contextual information.

## introduction hate speech is a form of communication that expresses prejudice, hatred, or discrimination against a specific individual or group based on attributes such as race, ethnicity, religion, nationality, gender, sexual orientation, disability, or other characteristics. it is distinguished by its goal to denigrate, belittle, or encourage violence or harm against persons or groups based on perceived differences or qualities. in this ever-expanding digital landscape, the emergence and proliferation of hate speech repre-sent an alarming concern. this not only promotes prejudice and division, but it also endangers societal cohesion and individual well-being. as a result, it is now more important than ever to build effective methods for its identification and mitigation. in this paper, we delve into the crucial area of hate speech detection with a specific focus on identifying not only offensive language but also the intended targets. this dual objective addresses a critical gap in the existing literature, as understanding the context and impact of hate speech requires considering both its content and the entities it targets. to this end, we explore the effectiveness of two powerful machine learning algorithms, logistic regression and support vector machines (svm), in the field of hate speech detection. these algorithms have a rich history of success in text classification tasks and provide valuable insight into the complexity of hate speech identification. we present a novel approach to multimodal hate speech event detection, focusing on two subtasks: hate speech detection and target detection. the proposed solutions for these subtasks of multimodal hate speech event detection at case 2023 @xcite has been evaluated with the baseline score presented by the work @xcite . we were placed seventh on subtask a and eighth on subtask b. for hate speech detection, we employ support vector machines (svm), while for target detection, we utilise logistic regression. the first subtask, hate speech detection, involves distinguishing between hate speech and nonhate speech textual content. traditional approaches have primarily relied on textual analysis techniques to identify hateful language. we leverage the svm model on a diverse dataset comprising labelled instances of hate speech and non-hate speech, enabling the model to learn the underlying patterns and discriminatory characteristics of hate speech. the second subtask, target detection, aims to identify the specific targets of hate speech within three categories: community, individual, and organisation. this is crucial for understanding the impact and potential harm caused by hate speech instances. by training the logistic regression model on labelled data, we enable it to predict the target category for a given hate speech instance accurately. while the focus of this paper is on textual content analysis using svm for hate speech detection and logistic regression for target detection, we acknowledge that visual elements, such as images or videos, can also contribute valuable information in detecting and understanding hate speech events. future research could explore the integration of visual analysis techniques alongside textual analysis to further enhance the accuracy and robustness of hate speech event detection. to evaluate the effectiveness of our proposed approach, we conduct comprehensive experiments on a diverse dataset comprising hate speech instances from various domains. by comparing our results with existing state-of-the-art hate speech detection techniques, we establish the competitiveness of our methodology. beyond academic contributions, our research holds practical implications for content moderation, social media platforms, and online communities. appropriate measures can be taken to mitigate the spread of harmful content, protect targeted individuals and communities, and foster a more inclusive and respectful online environment. the subsequent sections of this research paper namely methodology and result and discussion, will provide detailed explanations of our methodology, including data collection and preprocessing, feature extraction techniques, model development using svm and logistic regression, evaluation procedures, and the interpretation of experimental results. we will also discuss the limitations of our approach and suggest potential avenues for future research in the field of multimodal hate speech event detection. ## related work flow of information is vital to a society, and now with the advent of social media, the need to process them faster, better and in any form is on the rise. multimodal learning is a type of learning which uses multiple forms of data such as text, audio and images. the obstacles and challenges are clearly articulated by @xcite and @xcite . the authors of @xcite present their insights in learning mainly multimodal learning analytics. the works of @xcite and @xcite discuss deep learning related to multimodal learning. in particular, the work @xcite deals with cross modality feature learnings.the authors of @xcite have highlighted methods to fuse learned multimodal representations in deep-learning architectures. the authors of @xcite ) have presented their model which uses multimodal learning, and also shown a comparison with other deep learning models. with the increasing amount of data, identifying hate speech has become an important task. a lot of research has taken place regarding the detection and recognition of hate speech.a survey by authors of @xcite to recognize hate speech uses natural language processing approach. the authors of the work @xcite have explored the state-of-the-art algorithms and prospects of ai in the field of machine learning and natural language processing. the work @xcite analyzes resources available, and discusses the issues and venues for improvement in the field of hate speech. hate speech recognition not only concerns a single language, but research on multilingual problems have also been undertaken worldwide. for instance, the authors of @xcite have taken up the problem of hate speech against immigrants and women in different languages, english and spanish. this work is also targeted, in the sense, it deals with hate speech against a particular community. the authors of the work @xcite have considered multi-aspect multilingual hate speech problem and applied state-of-the-art learning models on their dataset for evaluation. research has been conducted in the field of hate speech by many, among those,much lesser in number are those that relates to multimodal learning. the authors of @xcite propose a new challenge set for multimodal classification, focusing on detecting hate speech in multimodal memes. the work @xcite also deals with hate speech using multimodal learning. this paper highlights multimodal dataset and models to recognising hate speech and the targets of the directed hate. 3 dataset and task crisishatemm (dataset from the task), is multimodal and contains data related to russia-ukraine crisis. the work @xcite presented the dataset for the task. it contains social media posts, memes and infographics in the form of textembedded images which contains some information and context as mentioned above in it. the first sub task, to detect hate speech, includes a total of 4,723 entries. a hate detected image entry was labelled as 1 and no hate detected entry was labelled as 0. the second sub task, on the other hand, is to identify the target of hate, which has a total of 2,428 entries. three different classes were identified as targets namely individual, community and organization. hate directed towards a individual was labelled as 0, hate directed towards a community as 1 and hate directed towards an organization as 2. the dataset distribution is shown in ## methodology we present solutions based on classical machine learning models namely svm and logistic regression on this paper. there are advantages to utilising classical ml rather than deep learning. when compared to deep learning models, the need for data is substantially lower, and classical models are often less computationally intensive. in short, classical ml has its own set of benefits, especially when interpretability, data availability, speed or resource constraints are significant factors. the decision to use conventional ml over deep learning was based on the unique situation, data availability, computational resources and the necessity for interpretability and simplicity. in many circumstances, hybrid techniques that include parts of both classical ml and deep learning can be powerful answers. ## result and discussion the main evaluation parameter for performance was the f1-score. the training performance parameters of different subtasks are shown in in any problem, the dataset plays a major role. the imbalance in dataset could be one of the reasons for misclassification. it could also be attributed to the fact that hate directed images themselves might not be directed explicitly, thus making it hard for models to recognise and learn them. preprocessing the available forms of data always plays a significant role in learning. all the above factors indicate the need for better performing models in the field of multimodal data. ## conclusion with the rising need to process data in different forms like opinions and perspectives in social media, identification of hate speech and its targets has become vital. in this paper, we have presented solutions to the task multimodal hate speech event detection -case 2023. the paper proposes solutions to the task of detecting hate speech in multimodal dataset and identifying the target of the hate as individual,community or organization. the performance metrics includes precision, recall, accuracy with f1-score as the key parameter. although the results presented herein are good, there remains potential for improvement. future research can focus on fine-tuning parameters for hate speech recognition. additional investigation may be undertaken to enhance the performance of existing models and to choose superior models.