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wangkongqiang@EEUCA 2026: Multimodal Identification of Vaccine Critical Content on Social Media

Paper · 2026 · Kongqiang Wang, Peng Zhang and Quingli Tan

Summary

This system paper describes a team's submission to the EEUCA 2026 shared task, which concerns detecting vaccine-critical material in social media posts using multiple modalities. The task is to assign one of three intent labels to vaccination-related memes: Vaccine critical (0), Neutral (1) or Pro-vaccine (2). Systems are ranked by macro F1. The data come from the VaxMeme dataset, which contains about 10,000 manually labelled English memes.

The authors used supervised fine-tuning throughout. They tuned several transformer encoders (ALBERT, BERT, ERNIE, RoBERTa variants), tried hybrid RoBERTa architectures and bagging, and adapted decoder LLMs (Qwen2 and Llama) through Llama-Factory with parameter-efficient training. Despite the multimodal framing of the task, the authors acknowledge that their approach makes essentially no use of the meme images.

The strongest submission was a fine-tuned Qwen2 1.5B, which reached a macro F1 of 0.8153 and placed 12th out of 25 teams. It slightly beat the larger LLMs and all encoder baselines. The work offers a reproducible text-centric baseline for detecting anti-vaccine content.

Key points

  • Dataset: VaxMeme contains 10,244 memes. Of these, 3,983 are Pro-vaccine, 3,441 Vaccine critical and 2,820 Neutral. The data are also split into three time periods (T1–T3), across which the label proportions shift noticeably.
  • Best system: fine-tuned Qwen2 1.5B. Test scores were F1 (Macro) 0.8153, Accuracy 0.8185, Precision 0.8151 and Recall 0.8159. The team ranked 12th, while the winning team reached a macro F1 of 0.8494.
  • Other LLMs on the test set: Qwen2 7B scored 0.8134 F1, Llama3 8B 0.8046 and Llama2 7B 0.7998. The authors attribute the smaller model's advantage to hyperparameter sensitivity or overfitting in the larger ones.
  • Encoder baselines: the best was simcse-roberta-lstm at 0.8036 F1, followed by FacebookAI/roberta-large-mnli and bagging, both at 0.8025. These encoders were trained with batch size 4 for 5 epochs at learning rates of 1e-5 or 2e-5.
  • Limitations and code: the authors name the unused image content and constrained compute resources as the main weaknesses. Their code is published on GitHub (WangKongQiang/EEUCA2026_…).

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Yunnan UniversityKunming · China17 works · 40 citations

 Fields, topics and concepts

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksComputer Vision and Pattern Recognition · 2,869 worksSocial Sciences · 1,772 worksSociology and Political Science · 577 works

🧭 Topics 1Research subjects and languages the work is about.Vision & language · 657 works

Cite

Cite as Kongqiang Wang, Peng Zhang and Quingli Tan. 2026. wangkongqiang@EEUCA 2026: Multimodal Identification of Vaccine Critical Content on Social Media. Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.eeuca-1.12

@inproceedings{wang2026wangkongqiang,
  title = {{wangkongqiang@EEUCA 2026: Multimodal Identification of Vaccine Critical Content on Social Media}},
  author = {Kongqiang Wang, Peng Zhang and Quingli Tan},
  booktitle = {Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026)},
  year = {2026},
  doi = {10.18653/v1/2026.eeuca-1.12},
  url = {https://doi.org/10.18653/v1/2026.eeuca-1.12}
}

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