In recent years, the detection of harmful and socially impactful content in multimodal online data has emerged as a critical area of research, driven by the increasing prevalence of text-embedded images and memes on social media platforms. These multimodal artifacts serve as powerful vehicles for expressing solidarity, resistance, humor, and sometimes hate, especially within the context of marginalized socio-political movements. To address these challenges, this shared task introduces a comprehensive, fine-grained classification framework consisting of four subtasks: (A) detection of hate speech, (B) identification of hate speech targets, (C) classification of topical stance toward marginalized movements, and (D) detection of intended humor. By focusing on the nuanced interplay between text and image modalities, this task aims to push the boundaries of automated socio-political event understanding and moderation. Using state-of-the-art deep learning and multimodal modeling approaches, this work seeks to enable a more effective detection of complex online phenomena, thus contributing to safer and more inclusive digital environments