Deciphering Hate: Identifying Hateful Memes and Their Targets (2024)

Abstract: Internet memes have become a powerful means for individuals to express emotions, thoughts, and perspectives on social media. While often considered as a source of humor and entertainment, memes can also disseminate hateful content targeting individuals or communities. Most existing research focuses on the negative aspects of memes in high-resource languages, overlooking the distinctive challenges associated with low-resource languages like Bengali (also known as Bangla). Furthermore, while previous work on Bengali memes has focused on detecting hateful memes, there has been no work on detecting their targeted entities. To bridge this gap and facilitate research in this arena, we introduce a novel multimodal dataset for Bengali, BHM (Bengali Hateful Memes). The dataset consists of 7,148 memes with Bengali as well as code-mixed captions, tailored for two tasks: (i) detecting hateful memes, and (ii) detecting the social entities they target (i.e., Individual, Organization, Community, and Society). To solve these tasks, we propose DORA (Dual cO-attention fRAmework), a multimodal deep neural network that systematically extracts the significant modality features from the memes and jointly evaluates them with the modality-specific features to understand the context better. Our experiments show that DORA is generalizable on other low-resource hateful meme datasets and outperforms several state-of-the-art rivaling baselines.

## introduction in recent years, social media has brought a distinct form of multimodal entity: memes, providing a means to express ideas and emotions. memes are compositions of images coupled with concise text. while memes are often amusing, they can also spread hate by incorporating socio-political elements. these hateful memes pose a significant threat to social harmony as they have the potential to harm individuals or specific groups based on figure 1 : example of hateful memes with associated targets. the first meme directly refers to a telecom organization as a bandit, and the second one deliberately attacks a religious community. despite being the seventh most widely spoken language, having 210 million speakers globally, bengali is considered one of the notable resourceconstrained languages @xcite . moreover, it is the official language of bangladesh and holds recognition as one of the official languages in the constitution of india. so, developing resources in bengali is important to build more inclusive language technologies. statistics indicate that over 45 million users engage with bengali on various social media platforms daily @xcite . recently, memes have gained significant traction in social media, reaching a broad audience and influencing public sentiment. many of these memes contain hateful content targeting various social entities. the limited availability of the benchmark datasets primarily constrains the identification of such hateful memes. only two prior works @xcite developed hateful meme datasets in bengali. however, both of them overlook the targets associated with the hateful memes. for instance, the first meme in figure 1 is hateful towards an organization because it depicts a company (i.e., grameenphone) as a robber. similarly, the second meme propagates hate towards a specific religious community (i.e., muslim) by highlighting that they produce many children. therefore, identifying targets of hateful memes is crucial for 1) understanding targeted groups and developing interventions to counter hate speech and 2) personalizing content filters, ensuring that users are not exposed to hateful content directed at them or their communities. to bridge this research gap, we develop a novel bengali meme dataset encompassing the targeted entities of hate. the captions in the dataset contain code-mixed (bengali + english) and codeswitched text (written bengali dialects in english alphabets). this makes the dataset more distinctive and challenging compared to previous studies in the field. on the technical front, prior research on hateful meme detection @xcite revealed that off-the-shelf multimodal systems, which often perform well on a range of visual-linguistic tasks, struggle when applied to memes. besides, most of the existing visiolinguistic models @xcite @xcite are primarily trained on image-text pairs of english languages, thus limiting their capability on low-resource languages. moreover, the existing state-of-the-art multimodal models @xcite for hateful meme detection can not be replicated because several components of their architectures are not available in low-resource languages (i.e., bengali). to tackle these issues, we developed a multimodal framework, and our major contributions are as follows: we also perform extensive experiments on bhm dataset and show that dora outperforms nine state-of-the-art unimodal and multimodal baselines in terms of all the evaluation measures. we further establish the generalizability and transferability of dora on two existing benchmark hateful meme datasets in bengali and hindi. hateful memes dataset: over the past few years, several meme datasets have been developed regard some works have also been conducted concerning low-resource languages. @xcite developed a dataset of 4,004 memes for detecting hate speech in greek. @xcite developed an offensive memes dataset consisting of 7,417 hindi memes. recently, das and mukherjee (2023) introduced a dataset comprising 4,043 samples for detecting the bengali abusive memes. two prior studies focused on hateful meme detection in the bengali language. @xcite introduced a synthetic hate speech dataset comprising around 4,500 bengali memes. similarly, @xcite developed another bengali hateful memes dataset having 4,158 memes labeled hateful or not-hateful. however, none of these datasets annotated the targets of hateful memes. multimodal hateful meme detection: over the years, various approaches have been employed for detecting hate speech using multimodal learning. earlier researchers used conventional fusion (i.e., early and late) @xcite produce producing multimodal representation. later, some researchers employed bilinear pooling @xcite while others developed transformer-based multimodal architectures such as mmbt @xcite , vilbert @xcite , and visual-bert @xcite . besides, some works attempted to use disentangled learning @xcite , incorporate additional caption @xcite , and add external knowledge @xcite to improve the hateful memes detection performance. recently, @xcite applied prompting techniques for hateful meme detection in english. differences with existing studies: though many studies have been conducted on hateful meme detection, only a few studies have focused on bengali. we point out several drawbacks in the existing research on bengali. firstly, the existing hateful memes datasets are small. they framed the task as a binary (hateful or not-hateful) classification problem, overlooking the social entities (i.e., individuals, society) that a hateful meme can target. our dataset provides both levels of annotation. the richness of our dataset contributes to a comprehensive understanding of the dynamics of bengali memes. only one particular work @xcite studied the targets of abusive memes in bengali. their work is completely different from ours as we attempt to identify targeted social entities of hateful memes, which are more explicit than abuse. secondly, the current research overlooked the memes containing crosslingual captions, while many internet memes are written in code-mixed and code-switched manner. lastly, none of the works provided any model that can be generalized across low-resource datasets. ## bhm: a new benchmark dataset as per our exploration, no dataset in bengali currently focuses on capturing hateful memes that target specific entities. to fill this gap, we develop a novel multimodal hateful meme dataset. we followed the guidelines outlined by @xcite and @xcite to develop the dataset. this section will provide a detailed discussion of the dataset development steps, including the data collection and annotation process and relevant statistics. ## methodology this section describes the proposed multimodal framework for detecting hateful memes and their targeted entities. figure 2 shows the overall architecture of the proposed system. ## experiments this section discusses the baselines and their performance comparison with the proposed method (dora) and its variants. we developed several state-of-the-art computational models, including only visual, textual, and multimodal models pretrained on both modalities. we used weighted f1 scores as the primary evaluation metrics. other metrics, such as precision and recall, are also reported for the comparison. appendix a provides the details of the experimental settings. ## conclusion this paper introduced a new large-scale multimodal dataset of 7,148 memes for detecting bengali hateful memes and their targeted social entities. we also proposed dora, a multimodal deep neural network for solving the tasks. experiments on our dataset demonstrate the efficacy of dora, which outperformed nine state-of-art baselines for two tasks. we further demonstrated the generalizability and transferability of dora across other datasets of different languages. we plan to extend the dataset for more domains and languages in the future.