The paper examines how four leading vision‑language models—LLaVA‑7B, Qwen‑VL, GPT‑4o mini, and Claude 3 Haiku—perform in detecting hateful content within memes. It evaluates the models under zero‑shot and few‑shot prompting, focusing not only on classification accuracy but also on the qualitative justifications they generate. The study highlights that these models often overlook contextual nuances, irony, and subtle cues essential for accurately identifying hate speech in memes.
By Muhammad Jawad Chowdhury, Adiba Hasan, Ishrak Hossain, Shahriar Ivan, Sabbir Ahmed
MexHat is a newly released video dataset aimed at improving hate‑speech detection in Mexican Spanish. It contains roughly 1,000 clips annotated for three broad categories—no negative content, offensive content, and hate‑speech—as well as a finer classification into three hate‑speech sub‑categories. The paper presents dataset statistics and baseline results, underscoring the challenges of detecting culturally and contextually nuanced hate speech in multimodal content.
By Itzel Tlelo-Coyotecatl, Hugo Jair Escalante
ArGuard is a shared task that evaluates harmful content detection in Arabic memes and LLM prompts, featuring two tracks: Track A for multimodal hate detection in memes and Track B for harmful prompt detection in Arabic LLM safety evaluation. Fifty‑eight teams registered, 35 reached the final evaluation, and 27 submitted system‑description papers, with participants experimenting with models such as AraBERT, Jais, and Qwen3‑VL. The top systems achieved macro‑F1 scores of 0.823 on A1, 0.419 on A2, 0.984 on B1, and 0.790 on B2, with fine‑grained meme classification in A2 proving the most challenging due to sparse labels and distribution shifts.
By Firoj Alam, Md. Rafiul Biswas, Mohamed Bayan Kmainasi, Ali Ezzat Shahroor, Hamdy Mubarak, George Mikros, Abul Hasnat, Wajdi Zaghouani
arXiv:2609.26907v1 Announce Type: cross
Abstract: Memes often derive their harmful, hateful, or sarcastic meaning from small but decisive visual, textual, or cross-modal cues. Existing multimodal cla...
By Akshit Sharma, Prashant W. Patil
arXiv:2606. 15307v1 Announce Type: cross Abstract: Hateful and propagandistic memes exploit the interplay between images and text to convey harmful intent that neither modality reveals alone.
By Mohamed Bayan Kmainasi, Mucahid Kutlu, Ali Ezzat Shahroor, Abul Hasnat, Firoj Alam
The paper introduces MemeMind, a large-scale dataset for detecting harmful memes that includes a detailed taxonomy and Chain-of-Thought reasoning annotations. It also proposes MemeGuard, a multimodal framework that uses a three-stage training strategy to improve visual understanding, reasoning, and discrimination of harmful content. Experiments show MemeGuard surpasses current state-of-the-art methods on MemeMind, advancing detection accuracy and interpretability.
By Hexiang Gu, Qifan Yu, Yuan Liu, Zikang Li, Saihui Hou, Jian Zhao, Zhaofeng He
arXiv:2607. 03981v1 Announce Type: cross Abstract: Memes have become influential communication tools on social media, combining viral visuals with concise messaging to convey impactful ideas.
By Fatema Tuj Johora Faria, Mukaffi Bin Moin, Md. Mahfuzur Rahman, Pronay Debnath, Asif Iftekher Fahim, Faisal Muhammad Shah
arXiv:2609.16393v1 Announce Type: new
Abstract: We introduce ParsHate, a manually annotated dataset of 10,000 Persian tweets spanning 2013-2022, representing the first decade-long benchmark for hate...
By Zahra Bokaei, Walid Magdy, Bonnie Webber
The paper explores using a large language model (LLM) to generate background context for social media posts and tests four ways to integrate this context into a Sentence-BERT (SBERT) hate‑speech detection classifier. The methods include text concatenation, embedding concatenation, hierarchical transformer fusion, and LLM‑driven text enhancement. Experiments on the Latent Hatred dataset of implicitly hateful tweets and the MAMI dataset of misogynous memes show that adding generated context can raise F1 scores by up to 3 points in textual and 6 points in multimodal settings compared to a zero‑context baseline, with embedding concatenation yielding the best results.
By Joshua Wolfe Brook, Ilia Markov
The paper proposes a training‑time explainability framework that aligns model reasoning with human‑annotated rationales to improve both classification performance and interpretability for multilingual hate speech detection. It is evaluated on HateXplain (English) and BullySent (Hinglish), datasets that capture anti‑Muslim hate in culturally coded, multilingual forms. Using methods such as LIME, Integrated Gradients, Grad‑X‑Input, and attention, the study shows that gradient‑ and attention‑based regularization boosts F‑scores, enhances plausibility and faithfulness, and captures culturally specific cues for detecting implicit anti‑Muslim hate.
By Muhammad Deedahwar Mazhar Qureshi, Sannaan Khan, Muhammad Atif Qureshi, Wael Rashwan
The paper introduces ProKDA, a progressive knowledge-to-decision alignment framework for explainable hateful meme detection. ProKDA separates explanation generation and label prediction into three sequential training stages—background knowledge learning, hatefulness detection learning, and hatefulness boundary alignment—reducing task interference. Experiments on three public benchmarks demonstrate that ProKDA achieves state‑of‑the‑art detection performance while providing accurate, evidence‑supported explanations for moderation decisions.
By Bo Xu, Chenyuan Wang, Xinyu Chen, Quanhao Zhu, Rui Lin, Liang Zhao, Hongfei Lin, Feng Xia
arXiv:2607. 15442v1 Announce Type: new Abstract: Internet memes intertwine visual cues, textual content, and cultural context, making them particularly challenging to interpret in scenarios where humor, sarcasm, and harmful intent coexist.
By Shanhong Liu, Pai Chet Ng, De Wen Soh, Malika Meghjani, Konstantinos N. Plataniotis