@inproceedings{wang-etal-2026-wangkongqiang-eeuca,
title = "wangkongqiang@{EEUCA} 2026: Multimodal Identification of Vaccine Critical Content on Social Media",
author = "Wang, Kongqiang and
Zhang, Peng and
Tan, Quingli",
editor = {H{\"u}rriyeto{\u{g}}lu, Ali and
Thapa, Surendrabikram and
Tanev, Hristo},
booktitle = "Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications ({EEUCA} 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eeuca-1.12/",
pages = "112--121",
ISBN = "979-8-89176-402-6",
abstract = "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."
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T wangkongqiang@EEUCA 2026: Multimodal Identification of Vaccine Critical Content on Social Media
%A Wang, Kongqiang
%A Zhang, Peng
%A Tan, Quingli
%Y Hürriyetoğlu, Ali
%Y Thapa, Surendrabikram
%Y Tanev, Hristo
%S Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-402-6
%F wang-etal-2026-wangkongqiang-eeuca
%X 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.
%U https://aclanthology.org/2026.eeuca-1.12/
%P 112-121
Markdown (Informal)
[wangkongqiang@EEUCA 2026: Multimodal Identification of Vaccine Critical Content on Social Media](https://aclanthology.org/2026.eeuca-1.12/) (Wang et al., EEUCA 2026)
ACL