MemeDetoxNet: Balancing Toxicity Reduction and Context Preservation (2025.findings-acl)
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| Challenge: | Toxic memes spread harmful and offensive content and pose a significant challenge in online environments. |
| Approach: | They propose a framework to mitigate toxicity in toxic memes by leveraging a set of pre-trained models that can interpret the visual and textual components of memes. |
| Outcome: | The proposed framework reduces toxicity on publicly available meme datasets by 10-20% compared to the previous methods. |
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| Challenge: | Existing studies on content moderation of toxic memes focus on text-based content . current research neglects the widespread influence of multimodal content like memes . |
| Approach: | They propose a framework leveraging Large Language Models and Visual Language Model (VLMs) for meme intervention. |
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DISARM: Detecting the Victims Targeted by Harmful Memes (2022.findings-naacl)
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| Challenge: | DISARM is a framework that uses named-entity recognition and person identification to detect all entities a meme is referring to and then incorporates a novel contextualized deep neural network to classify whether the meme intends to harm these entities. |
| Approach: | They propose a framework that uses named-entity recognition and person identification to detect all entities a meme is referring to and incorporates a novel contextualized deep neural network to classify whether the meme intends to harm them. |
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Enhancing LLM-based Hatred and Toxicity Detection with Meta-Toxic Knowledge Graph (2025.findings-acl)
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| Challenge: | Existing methods to address toxicity issues with large language models are inadequate . lack of domain-specific knowledge leads to false negatives and excessive sensitivity to toxic speech limits freedom of speech. |
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Beneath the Surface: Unveiling Harmful Memes with Multimodal Reasoning Distilled from Large Language Models (2023.findings-emnlp)
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| Challenge: | Existing methods for harmful meme detection ignore in-depth cognition of meme text and image . authors propose a framework for learning reasonable thoughts from LLMs for better multimodal fusion . |
| Approach: | They propose to use large language models to learn reasonable thoughts from LLMs for better multimodal fusion and lightweight fine-tuning. |
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MOMENTA: A Multimodal Framework for Detecting Harmful Memes and Their Targets (2021.findings-emnlp)
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Shraman Pramanick, Shivam Sharma, Dimitar Dimitrov, Md. Shad Akhtar, Preslav Nakov, Tanmoy Chakraborty
| Challenge: | a growing number of harmful memes are being used for trolling, cyberbullying and abuse . a new approach to detect harmful meme images and texts is emerging . |
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Detecting Harmful Memes and Their Targets (2021.findings-acl)
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Shraman Pramanick, Dimitar Dimitrov, Rituparna Mukherjee, Shivam Sharma, Md. Shad Akhtar, Preslav Nakov, Tanmoy Chakraborty
| Challenge: | a growing body of research on meme analysis has focused on detecting harmful memes and their social entities . a meme is a form of content that is often harmless and designed to look funny . but its multimodal nature and camouflaged semantics make its analysis challenging . |
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ModelCitizens: Representing Community Voices in Online Safety (2025.emnlp-main)
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Ashima Suvarna, Christina A Chance, Karolina Naranjo, Hamid Palangi, Sophie Hao, Thomas Hartvigsen, Saadia Gabriel
| Challenge: | Existing toxic language detection models are trained on annotations that collapse diverse perspectives into a single ground truth. |
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MemeIntel: Explainable Detection of Propagandistic and Hateful Memes (2025.emnlp-main)
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| Challenge: | Existing methods for label detection and explanation generation have been limited in understanding complex issues . identifying propaganda and hate in memes is essential for combating misinformation and minimizing harm . |
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Unintended Bias Detection and Mitigation in Misogynous Memes (2024.eacl-long)
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| Challenge: | Existing models that detect misogyny are not able to detect unintended biases in memes, perpetuating harmful stereotypes and reinforcing negative attitudes. |
| Approach: | They propose to measure and mitigate unintentional bias in misogynous memes detection models by using a contextualized scene graph-based multimodal network (CTXSGMNet) they also evaluate their generalizability by evaluating their performance on a few benchmark meme datasets. |
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Meme-ingful Analysis: Enhanced Understanding of Cyberbullying in Memes Through Multimodal Explanations (2024.eacl-long)
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| Challenge: | Recent laws like “right to explanations” have spurred research in developing interpretable models . a recent study has shown that multimodal explanations improve performance in generating textual justifications . |
| Approach: | They propose to use visual and textual modalities to explain why a given meme is cyberbullying . they use a Contrastive Language-Image Pretraining approach to generate textual justifications . |
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