Towards Low-Resource Harmful Meme Detection with LMM Agents (2024)

Abstract: The proliferation of Internet memes in the age of social media necessitates effective identification of harmful ones. Due to the dynamic nature of memes, existing data-driven models may struggle in low-resource scenarios where only a few labeled examples are available. In this paper, we propose an agency-driven framework for low-resource harmful meme detection, employing both outward and inward analysis with few-shot annotated samples. Inspired by the powerful capacity of Large Multimodal Models (LMMs) on multimodal reasoning, we first retrieve relative memes with annotations to leverage label information as auxiliary signals for the LMM agent. Then, we elicit knowledge-revising behavior within the LMM agent to derive well-generalized insights into meme harmfulness. By combining these strategies, our approach enables dialectical reasoning over intricate and implicit harm-indicative patterns. Extensive experiments conducted on three meme datasets demonstrate that our proposed approach achieves superior performance than state-of-the-art methods on the low-resource harmful meme detection task.

## introduction the rise of social media has catalyzed the emergence of a new multimodal entity: the meme. typically, a meme combines a visual element with concise text, making it highly sharable and capable of quick proliferation across various online platforms. although often viewed humorously, memes can become vehicles of harm when their mix of image and text is strategically used in the context of political and socio-cultural divisions. a widely accepted definition of harmful memes 1 is "multimodal units consisting of an image and embedded text that have the potential to cause harm to an individual, an organization, a community, or society in general" @xcite . for example, during the covid-19 pandemic, a frequently shared meme shown in figure 1 (a) was created by anti-vaccination groups using a manipulated image of bill gates. the widespread dissemination of this multimodal content foot_0 , which spread fear about covid-19 vaccines, significantly harmed bill gates' personal reputation and undermined efforts to bolster public immunity. therefore, it becomes imperative to develop automatic approaches for harmful meme detection to effectively unveil the dark side of memes on social media. previous studies employing deep neural networks (dnns) have advanced harmful meme detection @xcite by utilizing data-driven multimodal models @xcite @xcite , which rely heavily on extensively annotated data for training. however, these dnn-based approaches @xcite encounter significant challenges when it comes to detecting newly emerging memes related to breaking events, since the rapid development of such harmful memes makes it impractical to swiftly annotate enough training data @xcite . moreover, the dynamic and evolving nature of in-ternet memes raises concerns about the sustained effectiveness of traditional data-driven methods for harmful meme detection in real-world scenarios. this highlights the need for more adaptive and responsive approaches in the detection of harmful memes under the low-resource regime where only limited few-shot meme annotations are available. intuitively, the key to low-resource harmful meme detection is to strengthen the adaptability to continually changing online memes, and the capacity to respond promptly to new trends and contexts: 1) we posit that the shared characteristics among similar memes could facilitate the adaptability of harmful meme detection, as memes evolve by retaining inherent patterns. for instance, as depicted in figure 1 , bill gates shown in the meme of figure 1 (a) could be further incorporated with a joker face, which is extended to create a new harmful variant of figure 1 @xcite . 2) on the other hand, rather than using gradient descent to update model parameters, we resort to imitating human learning processes that accumulate experience without modifying the model's weights, thus mitigating the risk of overfitting to sparse annotations of harmful memes. as an example, to illustrate the harmfulness of the memes in figure 1 , a human checker needs the reasoning knowledge to gather the experience that bill gates is frequently vilified in harmful memes by anti-vaccination campaigners due to his advocacy for vaccine development. thus we devise a gradient-free approach to capture common features of harmful memes and derive insights from limited annotated training meme data for better generalization in a low-resource learning context. inspired by the powerful capacity of lmms for reasoning with contextual background knowledge @xcite , we propose a novel agentic approach: lorehm, towards low-resource harmful meme detection by regarding lmms as agents. to this end, we propose capturing the harmfulness of memes by employing both outward and inward analysis with limited fewshot annotated meme samples. specifically, 1) for looking outward at a meme, we first retrieve its similar memes with labels to leverage the annotation information as explicit auxiliary signals for harmfulness preference, thereby facilitating the decisionmaking of the lmm agent. 2) in terms of looking inward at a meme, we employ the fundamental skill of human learning, positioning the lmm as a learner agent to derive well-generalized insights from its failed attempts on the limited annotated meme samples, which aims to capture the implicit harmfulness meanings not conveyed through the superficial texts and images of memes. 3) finally, we combine the strategies of looking outward and inward to enable the lmm agent to perform the final harmfulness inference. in this manner, we enhance the lmm's ability as a trustworthy agent to detect harmful content concealed in the intrinsic multimodal information in memes. our contributions are summarized as follows in three folds: ## conclusion and future work in this paper, we delved into the low-resource issue of harmful meme detection with limited few-shot annotations. to this end, we proposed an agencydriven and gradient-free approach, which seamlessly integrated the relative sample augmentation and the meme insight augmentation strategies to strengthen lmms as a trustworthy agent. comprehensive experiments and analyses confirm the advantages of our agentic framework. future efforts aim to enhance our research by further relieving the inherent bias and variation in lmms.