False narratives that manipulate racial tensions are increasingly prevalent on social media, often blending languages and cultural references to enhance reach and believability. Among them, racial hoaxes produce unique harm by fabricating events targeting specific communities, social division and fueling misinformation. This paper presents a novel approach to detecting racial hoaxes in code-mixed Hindi-English social media data. Using a carefully constructed training pipeline, we have fine-tuned the XLM-RoBERTa-base multilingual transformer for training the shared task data. Our approach has incorporated task-specific preprocessing, clear methodology, and extensive hyperparameter tuning. After developing our model, we tested and evaluated it on the LT-EDI@LDK 2025 shared task dataset. Our system achieved the highest performance among all the international participants with an F1-score of 0.75, ranking 1st on the official leaderboard.

Memes, combining visual and textual elements, have emerged as a prominent medium for both expression and the spread of harmful ideologies, including misogyny. To address this issue in Chinese online content, we present a multimodal framework for misogyny meme detection as part of the LT-EDI@LDK 2025 Shared Task. Our study investigates a range of machine learning (ML) methods such as Logistic Regression, Support Vector Machines, and Random Forests, as well as deep learning (DL) architectures including CNNs and hybrid models like BiLSTM-CNN and CNN-GRU for extracting textual features. On the transformer side, we explored multiple pretrained models including mBERT, MuRIL, and BERT- base-chinese to capture nuanced language representations. These textual models were fused with visual features extracted from pretrained ResNet50 and DenseNet121 architectures using both early and decision-level fusion strategies. Among all evaluated configurations, the BERT-base-chinese + ResNet50 early fusion model achieved the best overall performance, with a macro F1-score of 0.8541, ranking 4th in the shared task. These findings underscore the effectiveness of combining pretrained vision and language models for tackling multimodal hate speech detection.