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.