Marginalized socio-political movements have become focal points of online discourse, polarizing public opinion and attracting attention through controversial or humorous content. Memes, play a powerful role in shaping this discourse both as tools of empowerment, and as vessels for ridicule or hate. The ambiguous and highly contextual nature of these memes presents a unique challenge for computational systems. In this work we try to identify these trends. Our approach leverages the BERT+ResNet(BERTRES) model to classify the multimodal content into different categories based on different tasks for the Shared Task on Multimodal Detection of Hate Speech, Humor, and Stance in Marginalized SocioPolitical Movement Discourse at CASE 2025. The task is divided into four sub-tasks: subtask A focuses on detection of hate speech, subtask B focuses on classifying the targets of hate speech, subtask C focuses on classification of topical stance and subtask D focuses on detection of intended humor. Our approach obtained a 0.73 F1 score in subtask A, 0.56 F1 score in subtask B, 0.6 F1 score in subtask C, 0.65 F1 score in subtask D.

Memes, a multimodal form of communication, have emerged as a popular mode of expression in online discourse, particularly among marginalized groups. With multiple meanings, memes often combine satire, irony, and nuanced language, presenting particular challenges to machines in detecting hate speech, humor, stance, and the target of hostility. This paper presents a comparison of unimodal and multimodal solutions to address all four subtasks of the CASE 2025 Shared Task on Multimodal Hate, Humor, and Stance Detection. We compare transformer-based text models (BERT, RoBERTa) with CNN-based vision models (DenseNet, EfficientNet), and multimodal fusion methods, such as CLIP. We find that multimodal systems consistently outperform the unimodal baseline, with CLIP performing the best on all subtasks with a macro F1 score of 78% in sub-task A, 56% in sub-task B, 59% in sub-task C, and 72% in sub-task D.

In recent years, memes have developed as popular forms of online satire and critique, artfully merging entertainment, social critique, and political discourse. On the other side, memes have also become a medium for the spread of hate speech, misinformation, and bigotry, especially towards marginalized communities, including the LGBTQ+ population. Solving this problem calls for the development of advanced multimodal systems that analyze the complex interplay between text and visuals in memes. This paper describes our work in the CASE@RANLP 2025 shared task. As a part of that task, we developed systems for hate speech detection, target identification, stance classification, and humor recognition within the text of memes. We investigate two multimodal transformer-based systems, ResNet-18 with BERT and SigLIP2, for these sub-tasks. Our results show that SigLIP-2 consistently outperforms the baseline, achieving an F1 score of 79.27 in hate speech detection, 72.88 in humor classification, and competitive performance in stance 60.59 and target detection 54.86. Through this study, we aim to contribute to the development of ethically grounded, inclusive NLP systems capable of interpreting complex sociolinguistic narratives in multi-modal content.

The multimodal ambiguity of text-embedded images (memes), particularly those pertaining to marginalized communities, presents a significant challenge for natural language and vision processing. The subtle interaction between text, image, and cultural context makes it challenging to develop robust moderation tools. This paper tackles this challenge across four key tasks: (A) Hate Speech Detection, (B) Hate Target Classification, (C) Topical Stance Classification, and (D) Intended Humor Detection. We demonstrate that the nuances of these tasks demand a departure from a ‘onesize-fits-all’ approach. Our central contribution is a task-specific methodology, where we align model architecture with the specific challenges of each task, all built upon a common CLIP-ViT backbone. Our results illustrate the strong performance of this task-specific approach, with multiple architectures excelling at each task. For Hate Speech Detection (Task A), the Co-Attention Ensemble model achieved a top F1-score of 0.7929; for Hate Target Classification (Task B), our Hierarchical CrossAttention Transformer achieved an F1-score of 0.5777; and for Stance (Task C) and Humor Detection (Task D), our Two-Stage Multiplicative Fusion Framework yielded leading F1-scores of 0.6070 and 0.7529, respectively. Beyond raw results, we also provide detailed error analyses, including confusion matrices, to reveal weaknesses driven by multimodal ambiguity and class imbalance. Ultimately, this work provides a blueprint for the community, establishing that optimal performance in multimodal analysis is achieved not by a single superior model, but through the customized design of specialized solutions, supported by empirical validation of key methodological choices.

The Devanagari script, an Indic script used by a diverse range of South Asian languages, presents a significant challenge in Natural Language Processing (NLP) research. The dialect and language variation, complex script features, and limited language-specific tools make development difficult. This shared task aims to address this challenge by bringing together researchers and practitioners to solve three key problems: Language identification, Hate speech detection, and Targets of Hate speech identification. The selected languages- Hindi, Nepali, Marathi, Sanskrit, and Bhojpuri- are widely used in South Asia and represent distinct linguistic structures. In this work, we explore the effectiveness of both machine-learning models and transformer-based models on all three sub-tasks. Our results demonstrate strong performance of the multilingual transformer model, particularly one pre-trained on domain-specific social media data, across all three tasks. The multilingual RoBERTa model, trained on the Twitter dataset, achieved a remarkable accuracy and F1-score of 99.5% on language identification (Task A), 88.3% and 72.5% on Hate Speech detection (Task B), and 68.6% and 61.8% on Hate Speech Target Classification (Task C).