A Multimodal Framework to Detect Target Aware Aggression in Memes (2024)

Abstract: Internet memes have gained immense traction as a medium for individuals to convey emotions, thoughts, and perspectives on social media. While memes often serve as sources of humor and entertainment, they can also propagate offensive, incendiary, or harmful content, deliberately targeting specific individuals or communities. Identifying such memes is challenging because of their satirical and cryptic characteristics. Most contemporary research on memes’ detrimental facets is skewed towards high-resource languages, often sidelining the unique challenges tied to low-resource languages, such as Bengali. To facilitate this research in low-resource languages, this paper presents a novel dataset MIMOSA (MultIMOdal aggreSsion dAtaset) in Bengali. MIMOSA encompasses 4,848 annotated memes across five aggression target categories: Political, Gender, Religious, Others, and non-aggressive. We also propose MAF (Multimodal Attentive Fusion), a simple yet effective approach that uses multimodal context to detect the aggression targets. MAF captures the selective modality-specific features of the input meme and jointly evaluates them with individual modality features. Experiments on MIMOSA exhibit that the proposed method outperforms several state-of-the-art rivaling approaches. Our code and data are available at https://github.com/shawlyahsan/Bengali-Aggression-Memes.

## introduction recently, the rise of social media has given prominence to a distinct multimodal phenomenon known as meme, a composition of an image coupled with concise textual content. while memes are often humorous, they can propagate hate, offense, and aggression by incorporating political or cultural elements. such undesired memes pose a significant threat to social harmony, as they can potentially harm individuals or specific groups based on their political philosophy, sexual orientation, religious beliefs, and more. as memes have become crucial in influencing social interactions, there has been a notable rise in research focused on meme analysis. this research includes analyzing the emotions @xcite conveyed in memes, sarcastic memes detection @xcite , and offensive memes detection @xcite . the emergence of highly toxic memes has prompted research efforts to explore their negative aspects, such as hate @xcite , offensiveness @xcite , and harm @xcite . however, most works have focused on the memes of high-resource languages while only a few studied the objectionable (i.e., hate, aggression, offense) memes of low-resource languages @xcite . bengali memes have gained significant traction recently, reaching a broad audience and influencing public opinion while promoting negativity and violence. detecting objectionable bengali memes is currently in the developing stage due to the limited availability of tools such as ocr. nonetheless, two works @xcite accomplished on detecting bengali hateful memes. research in this domain (both high-resource and low-resource) has highlighted that the exploration of the darker aspects of memes often overlooks the term 'aggression', which carries a more explicit and virulent connotation than 'harm' or 'offense'. to illustrate, consider the meme depicted in figure 1 (a);. at the same time, it may be perceived as harmful, a comprehensive analysis of its textual and visual context categorizes it as aggressive due to its explicit undermining of a religious group. moreover, aggressive meme identification requires separate analysis as it is more target-aware (i.e., religious, political, and gendered) than hate and offense. considering the pernicious impact of aggression, developing systems to identify aggressive memes and their targets is essential. with the motivation mentioned above, we develop a novel corpus of bengali memes encompassing various levels of aggression. on the technical front, prior studies reveal that state-of-the-art multimodal systems, effective in many visual-linguistic tasks, struggle with meme analysis. memes rely heavily on context and often lack a clear connection between visual and textual elements. moreover, memes contain much noise, making them distinct from other, more structured multimodal data. to tackle these issues, we develop a multimodal attentive fusion-based model to identify the targets of aggression within these memes. our significant contributions are as follows. this section demonstrates the previous studies that have already been conducted on objectionable content (i.e., hate, offense, and aggression) detection based on unimodal and multimodal content. unimodal based objectionable content detection: most research on objectionable content detection (ocd) focused on analyzing textual data. over the years, the topic has become a prominent research issue among researchers of different languages @xcite . several works focused on developing new corpus for various languages @xcite while others studied to introduce novel methods @xcite for ocd. some works were also performed concerning low-resource languages. @xcite introduced the first dataset for identifying target-aware aggression from bangla texts. likewise, two aggression datasets were introduced by @xcite and @xcite , which cover other low-resource languages like spanish, turkish, greek, and so on. various methods were employed over the years for hate, aggression, and offense detection. earlier studies used machine learning @xcite and recurrent neural network @xcite based approaches. later, transformer-based methods @xcite @xcite achieved superior performance for ocd. apart from the above research, few studies were performed for objectionable content detection from the visual data. for example, identifying the violent objects @xcite , nudity @xcite , aggression @xcite , and trolling @xcite from the images. ## mimosa: a new benchmark dataset per our exploration, no benchmark dataset is explicitly developed for identifying aggression and its targets from the multimodal data. to fill this void, we developed mimosa: a novel target-aware multimodal aggressive memes dataset in bengali. to create mimosa, we followed the guidelines provided by the @xcite . this section briefly describes the dataset development process, including data accumulation and annotation guidelines. ## methodology this section describes the proposed multimodal framework for target-aware aggression identification. the system takes memes and their corresponding caption as input. we employed state-of-the-art models to encode the memes' visual and textual information. afterward, we use an attentive fusion mechanism to create a multimodal representation by selectively focusing on the encoded visual and textual features. figure 4 shows the overall architecture of the proposed framework. ## experiments this section discusses the baselines and their performance comparison with the proposed method (maf). we will also illustrate the proposed approach's superiority by examining the errors. to experiment with mimosa, we developed several state-of-the-art computational models, including unimodal visual models, unimodal textual models, and multimodal models pre-trained on both modalities. we use two primary metrics for the evaluation: weighted f 1 -score (wf1) and macro-averaged mean absolute error (mmae) @xcite . appendix a presents the details of the experimental settings. ## conclusion this paper presented a novel multimodal dataset, mimosa, consisting of 4,848 memes, for detecting the targets of bengali aggressive memes into five classes. this research also proposed a multimodal deep neural network maf for the down-stream task. experiments on mimosa demonstrated the efficacy of maf outperformed eleven state-of-art unimodal and multimodal baselines. we plan to extend the dataset for more domains and languages. the future aim is to investigate the proposed model's performance on other datasets to enhance its generalization capabilities.