MULTILATE : A Synthetic Dataset on AI -Generated MULTI moda L h ATE Speech (2024)

Abstract: One of the pressing challenges society faces today is the rapid proliferation of online hate speech, exacerbated by the rise of AI-generated multimodal hate content. This new form of synthetically produced hate speech presents unprecedented challenges in detection and moderation. In response to the growing presence of such harmful content across social media platforms, this research introduces a groundbreaking solution:

## introduction a prevalent sociological problem currently is online hate speech, where meta (formerly facebook) has reported removed 18 million hate content articles foot_0 in the second quarter of 2023 which is more than the 10.7 million ones it deleted during the first quarter of 2023. between april and june 2021, meta took down more than 31 million posts containing hate speech. the spread of this hateful speech results in considerable emotional anguish, particularly within vulnerable minority groups, thus normalizing prejudice @xcite . a new development to this is the continued appearance of hate speech generated by artificial intelligence (ai) @xcite , which is even more problematic regarding recognition and elimination. with the help of many new sophisticated language models, it is possible to create essentially fake texts that cannot be distinguished from the texts produced by humans, thus propagating hatred ideologies quickly and on a large scale. in addition, the complexity of ai-generated posts can outsmart existing content moderation techniques and ultimately let toxic messages spread. one egregious example involves a historical speech by adolf hitler, altered by ai to deliver antisemitic remarks in english. this manipulated video, shared by an influencer, quickly garnered over 15 million views on x (formerly twitter) in march 2024 foot_1 . such incidents underscore the growing concern among researchers and monitoring organizations about the proliferation of ai-generated hate and the urgent need for critical evaluation and robust detection mechanisms to combat this emerging threat. to cater for this, new and improved detection mechanisms must be employed on social media platforms to teach the system to detect ai-driven hate speech along with regular human moderation and partnering with organizations focusing on ai ethics and safety online. the fight against online hate thus depends on how these new technological forms are met and how a good containment approach is developed that protects threatened groups against the emerging threat of hate speech via artificial intelligence. researchers have recently emphasized the cre-ation of multimodal hate speech detection systems that can perform at large scales, especially on platforms such as meta, x, and youtube @xcite . nevertheless, the development of aigenerated hate has been limited due to the absence of large-scale cross-modal hate speech datasets specifically designed for human-generated hate. to circumvent this weakness, a novel multimodal dataset named "multilate" is proposed to facilitate the mass-scale assessment of ai-generated multimodal hate speech. this dataset consists of 2.6 million instances of text, and these textbased statements are used to generate ai images created through stable diffusion (sd) @xcite . further, every instance includes pixel-level heatmaps for visual interpretability and question-answer (qa) pairs, which incorporate "who", "what", and "why" for explainability. adversarial examples of text (morris et al., 2020) and images @xcite are also included to promote more robust multimodal hate speech detection in the field of growing significance at the interface of natural language processing (nlp) and artificial intelligence. ## related work hate speech refers to discrimination due to race, ethnic background, religion, gender, and sexual orientation. it has severe consequences, including prejudice and violence in society. the classification of hate speech has mainly involved machine learning models such as the support vector machine (svm) and random forest @xcite . however, these challenges remain like conflict or overlapping definitions of emotions, availability of datasets, and algorithmic methodology @xcite . the widespread nature of online sexism has made researchers interested in sexism classification and has subsequently led to the emergence of automated recognition technologies. in studies, sexism is identified using deep learning (dl) architectures such as convolutional neural network (cnn) and bidirectional encoder representations from transformers (bert) applied in social media conversations @xcite @xcite . the generation of datasets like toxigen @xcite helps in the improvement of toxic language detection calling for massive and uniform datasets. moreover, there are developments on sexism detection in machine learning using data augmentation methods and ensembles of state-of-the-art language embeddings like bert or roberta @xcite . furthermore, the research explores the application of dl models such as bilstms, bert, and gpt-2 in sexism classification, demonstrating promising results @xcite @xcite . challenges of resource-constrained languages such as urdu hate speech for detection; traditional models outperform dl-based approach owing to class imbalance and data scarcity, a case study @xcite . however, the concluding remarks emphasize the need for future work that addresses the challenges of improving current models' discrimination capabilities and exploring user-based features @xcite . @xcite proposed the new problem of multi-modal hate speech detection with text and image. they constructed the mmhs150k dataset for annotated tweet images. they try out textual kernel-based fusion approaches such as @xcite among other unimodal and multimodal models and showcase that images are helpful sources of information. nevertheless, it is incredibly challenging in terms of data as well as the multimodal nature of the problem. however, concurrent modelling of the textual and visual information presents a potential for detecting hate speech as a critical open area for supporting content moderation. they generally create the beginnings of multimodal hatred utterance research on the study grounds. lastly @xcite describes a five-factor, issue-based, question-answering system for a more intelligible explanation of automated fact-checking machines foot_2 . using this method, the authors develop the factify-5wqa dataset of more than 390,000 textual claims in which they label each sentence's five semantics roles and pair them with appropriate questions that can be used as queries. validated qa pairs are employed to check some elements of specific evidentiary documents for precise identification of falsity in claims. ## data this section outlines the creation of the dataset called multilate foot_3 . specifically designed to identify instances of hate speech, particularly sexism and racism, in online content, multilate is a unique dataset containing a total of 2.6 million samples extracted from 11 different datasets. the dataset includes synthetically generated images created using advanced ai models to enhance its scope and applicability. these ai-generated hate samples are particularly useful for training models that can classify and identify ai-generated hate speech in real-world settings. the dataset features labels for binary classification, such as hate" and not hate", as well as multiclass classification labels, including sexist", racist", and "neither". by providing a comprehensive and diverse collection of examples, this dataset is a valuable resource for researchers and developers working on automated techniques for detecting and mitigating hate speech online. ## baseline classification models several models were employed to establish baseline performance on the multilate dataset, covering text, image, and multimodal classification. these models include cnn-bilstm, resnet50, bert, roberta, vgg16, and a basic cnn model. ## conclusion this study introduces multilate, a comprehensive dataset designed to advance multimodal hate speech analysis by incorporating both synthetic visual content and diverse textual statements. the inclusion of adversarial examples and detailed interpretability annotations provides an essential resource for developing robust and explainable models. the dataset is constructed using rigorous data collection methods and benchmarked to assess classification performance across multiple modalities. given the substantial computational requirements for ai-generated image content, the multilate dataset will be released in batches. this phasedrelease approach is intended to manage the high resource demands effectively while ensuring accessibility and utility for future research. these efforts aim to enable significant advancements in understanding and mitigating online hate speech through multimodal analysis.