Memes, a multimodal form of communication, have emerged as a popular mode of expression in online discourse, particularly among marginalized groups. With multiple meanings, memes often combine satire, irony, and nuanced language, presenting particular challenges to machines in detecting hate speech, humor, stance, and the target of hostility. This paper presents a comparison of unimodal and multimodal solutions to address all four subtasks of the CASE 2025 Shared Task on Multimodal Hate, Humor, and Stance Detection. We compare transformer-based text models (BERT, RoBERTa) with CNN-based vision models (DenseNet, EfficientNet), and multimodal fusion methods, such as CLIP. We find that multimodal systems consistently outperform the unimodal baseline, with CLIP performing the best on all subtasks with a macro F1 score of 78% in sub-task A, 56% in sub-task B, 59% in sub-task C, and 72% in sub-task D.

Alzheimer’s Disease (AD) is a neurodegenerative disorder that affects cognitive abilities and memory, especially in older adults. One of the challenges of AD is that it can be difficult to diagnose in its early stages. However, recent research has shown that changes in language, including speech decline and difficulty in processing information, can be important indicators of AD and may help with early detection. Hence, the speech narratives of the patients can be useful in diagnosing the early stages of Alzheimer’s disease. While the previous works have presented the potential of using speech narratives to diagnose AD in high-resource languages, this work explores the possibility of using a low-resourced language, i.e., Hindi language, to diagnose AD. In this paper, we present a dataset specifically for analyzing AD in the Hindi language, along with experimental results using various state-of-the-art algorithms to assess the diagnostic potential of speech narratives in Hindi. Our analysis suggests that speech narratives in the Hindi language have the potential to aid in the diagnosis of AD. Our dataset and code are made publicly available at https://github.com/rkritesh210/DementiaBankHindi.