Our team was interested in content classification and labeling from multimodal meme detection of vaccine critical content on social media.We joined the shared task on Multimodal Identification of Vaccine Critical Content on Social Media@EEUCA with ACL 2026. In this task,our goal is to assign a content classification label to vaccine-related discourse (e.g., Vaccine critical, Neutral, Pro-vaccine). The objectiveis to develop systems that can classify the intent of a vaccine-related meme. The dataset for this task will have three labels: Vaccine critical (0), Neutral (1), and Pro-vaccine (2). The performance will be ranked by F1-score (Macro). This shared task is based on the VaxMeme dataset, a collection of over 10,000 manually annotated vaccination-related memes, designed to support multimodal vaccine-critical meme detection. Our group used a supervised learning method on finetuning pre-trained models and Large Language Model (LLM), including Qwen2 LLMs and Llama series LLMs based on Llama-Factory. The best result on the test set for shared task were Macro F1 score of 0.8153, Accuracy 0.8185, Precision (Macro) 0.8151, and Recall (Macro) 0.8159 from fine-tuning qwen2_1.5B LLM method, ranking 12th among all teams. The complete code of this entire project can be found at our GitHub address.

Our team was interested in content classification and labeling from toxicity detection of gaming chat logs in online gaming communities. We joined the shared task on Understanding Toxic Behavioral Intent in Gaming Chat Logs@EEUCA with ACL 2026. In this task, our goal is to assign a content classification label to player’s utterance (e.g., Hate and Harassment, Threats, Non-toxic). The objective is to develop systems that can classify the intent of a player’s utterance. The dataset for this task will have five labels: Non-toxic (0), Insults and Flaming (1), Other Offensive Texts (2), Hate and Harassment (3), Threats (4) and Extremism (5). The performance will be ranked by F1-score (Macro). The task utilizes 53,000 game chat utterances from World of Tanks. Our group used a supervised learning method on multiple pre-trained models and finetuning Qwen2 LLMs. The best result on the test set for shared task were Macro F1 score of 0.5776, Accuracy 0.9075, Precision (Macro) 0.6847, and Recall (Macro) 0.5343 from fine-tuning qwen2_7B LLM method, ranking 8th among all teams. The complete code of this entire project can be found at our GitHub address.