Social media memes have become a powerful form of digital communication, combining images and text to convey humor, social commentary, and sometimes harmful content. This paper presents a multimodal approach using a fine-tuned CLIP model to analyze textembedded images in the CASE 2025 Shared Task. We address four subtasks: Hate Speech Detection, Target Classification, Stance Detection, and Humor Detection. Our method effectively captures visual and textual signals, achieving strong performance with precision of 80% for the detection of hate speech and 76% for the detection of humor, while stance and target classification achieved a precision of 60% and 54%, respectively. Detailed evaluations with classification reports and confusion matrices highlight the ability of the model to handle complex multimodal signals in social media content, demonstrating the potential of vision-language models for computational social science applications.

Devanagari script, encompassing languages such as Nepali, Marathi, Sanskrit, Bhojpuri and Hindi, involves challenges for identification due to its overlapping character sets and lexical characteristics. To address this, we propose a method that utilizes Continuous Bag of Words (CBOW) embeddings integrated with attention-enhanced Bidirectional Long Short-Term Memory (BiLSTM) network. Our methodology involves meticulous data preprocessing and generation of word embeddings to better the model’s ability. The proposed method achieves an overall accuracy of 99%, significantly outperforming character level identification approaches. The results reveal high precision across most language pairs, though minor classification confusions persist between closely related languages. Our findings demonstrate the robustness of the CBOW-BiLSTM model for Devanagari script classification and highlights the importance of accurate language identification in preserving linguistic diversity in multilingual environments. Keywords: Language Identification, Devanagari Script, Natural Language Processing, Neural Networks