A Survey on Combating Hate Speech through Detection and Prevention in E nglish (2024)

Abstract: The rapid rise of social networks has brought with it an increase in hate speech, which poses a significant challenge to society, researchers, companies, and policymakers. Hate speech can take the form of text or multimodal content, such as memes, GIFs, audio, or videos, and the scientific study of hate speech from a computer science perspective has gained attention in recent years. The detection and combating of hate speech is mostly considered a supervised task, with annotated corpora and shared resources playing a crucial role. Social networks are using modern AI tools to combat hate speech, and this survey comprehensively examines the work done to combat hate in the English language. It delves into state-of-the-art methodologies for unimodal and multimodal hate identification, the role of explainable AI, prevention of hate speech through style transfer, and counternarrative generation, while also discussing the efficacy and limitations of these methods. Compared with earlier surveys, this paper offers a well-organized presentation of methods to combat hate.

## introduction the recent exponential growth of the internet, technology, and social media has revolutionized communication but also provided a platform to disseminate hateful content. the united nations strategy and plan of action on hate speech describes hate speech as any kind of communication in speech, writing, or behavior that attacks or uses pejorative or discriminatory language concerning a person or a group based on who they are, in other words, based on their religion, ethnicity, nationality, race, color, descent, gender, or identity factor. 1 . hate speech is used as a broad umbrella term for numerous user-created content intended to disparage, or dehumanize, any individual or any group based on some characteristics such as race, color, gender, nationality, ethnicity, etc. @xcite . with the advancement of natural language processing (nlp) technology, substantial research has been conducted on automatic textual hate speech detection in recent years. there are large-scale publicly available datasets collected from various social media platforms and tagged into sub-variants of hate such as aggression @xcite , @xcite hate @xcite , @xcite , @xcite ), offensive ( @xcite , @xcite , @xcite ), abusive @xcite , @xcite , @xcite , @xcite ), harassment( @xcite ) toxic ( @xcite , @xcite , @xcite , @xcite ), cyberbullying @xcite , racism @xcite , sexism @xcite ,flame @xcite misogynistic @xcite . facebook reports 510k comments/minute and x reports 350 tweets per minute 2 . recent research has focused on developing automatic systems to detect hate speech on social media platforms. these typically employ semantic content analysis techniques built on natural language processing (nlp) and machine learning (ml) methods such as support vector machine (svm) and logistic regression (lr), convolutional neural networks (cnn), long short-term memory (lstm), gated recurrent units (gru), bidirectional encoder representations from the transformer (bert), etc. the task typically involves classifying a comment into non-hate or hateful and measuring in terms of performance metrics. hate speech is disseminated via multimodal data such as memes (text superimposed within images), audio, and video. most of the work revolves around meme identification with the concepts of early fusion and late fusion. with the emergence of the large language model (llm), the employment of a vision transformer-based approach for identification has risen. however, little research focus is on audio or video identification. recently, the model has been aided with the explanability information for better classification. the multimodal approach focuses on leveraging the multimodality features @xcite , @xcite , @xcite . recently, the identification of hate span, and transforming/rephrasing the offensive into non-offensive has been in attention to counter the hate speech. the remainder of this paper is structured as follows: section 2 reviews the dataset organized for the different subtasks of hate identification; section 3 describes the features related to hate speech detection in the uni-modal identification. section 4 introduces the tasks done to solve multimodal hate prediction, section 5 presents the study to counter hate speech; and finally, section 6 is about the implementation and role of explainable ai, section 7 reports some challenges; and section 8 concludes this work and discusses future work. ## corpus details this section covers the dataset collection process, the annotator's role, available datasets, and challenges associated. ## unimodal: textual identification the distinguishing approach in the classification tasks is the usage of features. this section covers the various approaches utilized to compute the features and various methods employed to improve the performance of the classifier. the encoded features are generally applied to the machine learning or deep neural network to get the probability distribution of the classes. ## multi-modal the early works of multimodal hate identification involve the usage of meta-tweet features aided to the main tweet @xcite , @xcite . @xcite proposed an ensemble of recurrent neural network (rnn) classifiers, incorporating various features associated with userrelated information, such as users' tendency towards racism or sexism. @xcite ) @xcite utilizes a wide variety of metadata, such as tweet-based, user-based, and network-based features. the properties of bullies and aggressors were studied. @xcite derived 10 features grouped into text-based features, emotion-based features, familiarity-based features, contrast-based features, and complexitybased features @xcite leveraged the gender and demographic information, (unsvåg and gambäck, 2018) investigates the potential effects of users' features such as gender, network (number of followers and friends), activity (number of statuses and favorites), and profile information (geo-enabled, default profile, default image, and number of public lists). @xcite investigate profiling users by their past utterances as an informative prior. but in the current scenario, social media has also seen an upsurge in memes, gifs, audio, and video to propagate hate. however, most of the data are available for multimodal meme identification. memes-that have recently emerged as popular engagement tools and which, in their usual form, are image macros shared through social media platforms mainly for amusement-are also being increasingly used to spread hate and/or instigate social unrest and therefore seem to be a new form of expression of hate speech on online platforms @xcite . some of these multimodal publications are only hate speech because of the combination of the text with a certain image @xcite . multimodal hate speech detection integrates various data types, such as text, images, audio, and video, to enhance the accuracy and robustness of identifying hate speech. the next part covers the feature extractor and usage of a multimodal pretrained transformer. ## dehatify this section mainly deals with the advancement in the style transfer and counter-narrative response. preventing hate speech through style transfer entails rephrasing toxic information in neutral or positive language and using advanced nlp techniques to change the tone while preserving content. in nlp, style transfer involves adding certain stylistic attributes to text while maintaining its basic structure and meaning. it follows the concept of encoder and decoder. the model is trained using unsupervised (no parallel data) or in a supervised manner (parallel data). ## challenges degradation of datasets, non-uniform definitions of hate, non-disclosure of the annotation guidelines, annotators' bias, time-consuming annotation, men-tal illness, etc. the mental health of hate victims has also been studied. ## conclusion and future work in this survey, we provided a critical assessment of how the automatic identification of hate speech in text has advanced over the last several years. other realms of hate speech that we examined included cyberbullying, abusive language, discrimination, sexism, extremism, and radicalization. the work done in the unimodal text identification, multimodal hate identification, style transfer, counternarrative generation, and discussion on mental health is done. the future work should focus more on fine-grained hate detection, a more mathematically efficient fusion approach, adding more explainability, and the continuous learning paradigm.