Memes have become a popular way for people to share opinions and emotions on social media, but they are also often used to spread hate and negative sentiments. In this paper, we present our multimodal approach to the CHiPSAL 2026 shared task on multimodal hate and sentiment detection in Nepali memes, which includes two subtasks: hate detection and sentiment analysis. Since memes usually combine both text and images, we first experimented with different unimodal models for text and images separately. After identifying the top two best-performing text and image models, combined them using different fusion techniques. The results show that multimodal models outperform unimodal ones, highlighting that both textual and visual information are important for understanding the context of memes. The multi- modal model, which combines sentence-transformers/LaBSE for text and ResNet-18 for image using weighted Fusion technique, achieved a macro F1 score of 0.6614 for Subtask A and sentence-Transformers/LaBSE for text and deit- Base for image using simple Fusion technique, achieved a macro F1 score of 0.4839 for SubTask B, on the test dataset.

In the age of digital communication, social media platforms have become a medium for the spread of misinformation, with racial hoaxes posing a particularly insidious threat. These hoaxes falsely associate individuals or communities with crimes or misconduct, perpetuating harmful stereotypes and inflaming societal tensions. This paper describes the team “Hope_for_best” submission that addresses the challenge of detecting racial hoaxes in codemixed Hindi-English (Hinglish) social media content and secured the 2nd rank in the shared task (Chakravarthi et al., 2025). To address this challenge, the study employs the HoaxMix Plus dataset, developed by LT-EDI 2025, and adopts a multi-phase fine-tuning strategy. Initially, models are sensitized using the THAR dataset—targeted hate speech against religion (Sharma et al., 2024) —to adjust weights toward contextually relevant biases. Further fine-tuning was performed on the HoaxMix Plus dataset. This work employed data balancing sampling strategies to mitigate class imbalance. Among the evaluated models, Hing BERT achieved the highest macro F1-score of 73% demonstrating promising capabilities in detecting racially charged misinformation in code-mixed Hindi-English texts.