Memes have emerged as a fast and influential way to share information online, particularly during major public health events like COVID-19 vaccination. While they can support awareness and encourage positive behavior, they are also widely used to spread misinformation and vaccine-critical views. These messages are often expressed through sarcasm and implicit meaning, which makes automatic detection difficult. To tackle this problem, EEUCA 2026 introduces a shared task based on the VaxMeme dataset for multimodal vaccine critical meme detection. The task encourages us to design models that can jointly understand both image and text, capturing the underlying context more effectively. In this work, we present our approach to this task by proposing a two-stage early fusion framework that integrates multiple transformer-based encoders. We train our model using focal loss to give more attention to difficult samples. Our experimental results show that our method performs competitively in the shared task, demonstrating its effectiveness for this problem.

In today’s digital era, individuals convey their feelings, viewpoints, and perspectives across various platforms in nuanced and intricate ways. At times, these expressions can be challenging to articulate and interpret. Emotion recognition aims to identify the most relevant emotions in a text that accurately represent the author’s psychological state. Despite its substantial impact on natural language processing (NLP), this task has primarily been researched only in high-resource languages. To bridge this gap, SemEval-2025 Task 11 introduces a multilingual emotion recognition challenge encompassing 32 languages, promoting broader linguistic inclusivity in emotion recognition. This paper presents our participation in this task, where we introduce a language-specific fine-tuned transformer-based system for emotion recognition and emotion intensity prediction. To enhance generalization, we incorporate a multi-sample dropout strategy. Our approach is evaluated across 11 languages, and experimental results demonstrate its competitive performance, achieving top-tier results in certain languages.

Food contamination and associated illnesses represent significant global health challenges, leading to thousands of deaths worldwide. As the volume of food-related incident reports on web platforms continues to grow, there is a pressing demand for systems capable of detecting food hazards effectively. Furthermore, explainability in food risk detection is crucial for building trust in automated systems, allowing humans to validate predictions. SemEval-2025 Task 9 proposes a food hazard detection challenge to address this issue, utilizing content extracted from websites. This task is divided into two sub-tasks. Sub-task 1 involves classifying the type of hazard and product, while sub-task 2 focuses on identifying precise hazard and product “vectors” to offer detailed explanations for the predictions. This paper presents our participation in this task, where we introduce a transformer-based method. We fine-tune an enhanced version of the BERT transformer to process lengthy food incident reports. Additionally, we combine the transformer’s contextual embeddings to enhance its contextual representation for hazard and product “vectors” prediction. The experimental results reveal the competitive performance of our proposed method in this task. We have released our code at https://github.com/AhmadMonirCSECU/SemEval-2025_Task9.