--- title: "Multimodal Identification of Vaccine Content Stance on Social" description: "The task offers a public benchmark for multimodal analysis of public-health misinformation and points to… 1 reference, cited by 9 papers here." authors: ["Surendrabikram Thapa", "Shuvam Shiwakoti", "Siddhant Bikram Shah", "Kritesh Rauniyar", "Laxmi Thapa", "Surabhi Adhikari", "Kristina T. Johnson", "Ali Hürriyetoğlu", "Hristo Tanev", "Usman Naseem"] year: 2026 venue: "Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026)" source: "https://aclanthology.org/2026.eeuca-1.3/" doi: "10.18653/v1/2026.eeuca-1.3" licence: "CC BY 4.0 (ACL Anthology)" licence_url: "https://creativecommons.org/licenses/by/4.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/thapa-2026-multimodal-identification-vaccine-content-stance/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/thapa-2026-multimodal-identification-vaccine-content-stance/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/2/) / Multimodal Identification of Vaccine Content Stance on Social Media # Multimodal Identification of Vaccine Content Stance on Social Media **Authors:** [Surendrabikram Thapa](https://md.research.elamur.ai/free/en/authors/surendrabikram-thapa/), [Shuvam Shiwakoti](https://md.research.elamur.ai/free/en/authors/shuvam-shiwakoti/), [Siddhant Bikram Shah](https://md.research.elamur.ai/free/en/authors/siddhant-bikram-shah/), [Kritesh Rauniyar](https://md.research.elamur.ai/free/en/authors/kritesh-rauniyar/), [Laxmi Thapa](https://md.research.elamur.ai/free/en/authors/laxmi-thapa/), [Surabhi Adhikari](https://md.research.elamur.ai/free/en/authors/surabhi-adhikari/), [Kristina T. Johnson](https://md.research.elamur.ai/free/en/authors/kristina-t-johnson/), [Ali Hürriyetoğlu](https://md.research.elamur.ai/free/en/authors/ali-hurriyetoglu/), [Hristo Tanev](https://md.research.elamur.ai/free/en/authors/hristo-tanev/) and [Usman Naseem](https://md.research.elamur.ai/free/en/authors/usman-naseem/) · **Year:** 2026 · **Venue:** [Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026)](https://md.research.elamur.ai/free/en/venues/eeuca/) ([Workshop on Event Extraction and Understanding: Challenges and Applications 2026](https://md.research.elamur.ai/free/en/venues/eeuca/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/) **Links:** [Publisher's page](https://aclanthology.org/2026.eeuca-1.3/) · [PDF](https://aclanthology.org/2026.eeuca-1.3.pdf) · [DOI 10.18653/v1/2026.eeuca-1.3](https://doi.org/10.18653/v1/2026.eeuca-1.3) · [OpenAlex](https://openalex.org/W7166838229) **Citations:** 0 citations (OpenAlex, 2026-10-14) · [9](https://md.research.elamur.ai/free/en/papers/thapa-2026-multimodal-identification-vaccine-content-stance/cited-by/) in our library, of the 149,439 papers whose bibliographies we read ## Summary This paper reports on a shared task, held at the EEUCA 2026 workshop co-located with ACL 2026, on automatically classifying the vaccination stance of social media memes. Memes are hard to analyse because their meaning often comes from how an image interacts with its overlaid text, and they frequently use sarcasm, irony and cultural allusions. The organizers used the VaxMeme dataset, a collection of English Twitter memes manually labelled as Vaccine-critical, Neutral or Pro-vaccine. They split it into training, validation and test portions and ranked submissions by macro-averaged F1. Submitted systems included fine-tuned transformer encoders, CLIP- and ViLT-based fusion models, ensembles and instruction-tuned LLMs such as Qwen and Llama. The top system reached a macro F1 of 0.8494. Some text-only models were surprisingly competitive, which suggests that OCR text carries much of the stance signal. The organizers argue that visual context still helps in implicit or ironic cases. The task offers a public benchmark for multimodal analysis of public-health misinformation and points to future work on multilingual data, explainability and robustness to sarcasm. ## Key points - **VaxMeme** contains 10,244 image-plus-text memes collected via the Twitter API between October 2020 and April 2021. Annotator agreement was high (Fleiss' κ = 0.85). - The class totals are 3,157 Vaccine-critical, 3,104 Neutral and 3,983 Pro-vaccine, divided 80/10/10 into train, validation and test sets. - Participation: 77 registrants and 25 teams with evaluated systems. The best macro F1 was 0.8494. - Team wangkongqiang placed 12th (macro F1 0.8153) with a fine-tuned Qwen2-1.5B, outperforming larger LLMs in its own experiments. In another team's experiments, a text-only BERT-base-uncased (0.8102, 14th) beat the same team's CLIP ViT-B/32 + BERT fusion models. - The authors list three main limitations: English-only data from the COVID-19 period, noisy OCR text, and the fact that leaderboard F1 does not capture robustness or fairness. *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 - [Columbia University](https://md.research.elamur.ai/free/en/universities/columbia-university/) — University, New York, United States · 241 works · 54,053 citations - [Northeastern University](https://md.research.elamur.ai/free/en/universities/northeastern-university/) — University, Boston, United States · 57 works · 6,533 citations - [Macquarie University](https://md.research.elamur.ai/free/en/universities/macquarie-university/) — University, Sydney, Australia · 42 works · 5,776 citations - [Virginia Tech](https://md.research.elamur.ai/free/en/universities/virginia-tech/) — University, Blacksburg, United States · 36 works · 43,036 citations - [European Commission](https://md.research.elamur.ai/free/en/government/european-commission/) — Government body, Brussels, Belgium · 5 works · 974 citations - [Joint Research Centre](https://md.research.elamur.ai/free/en/government/joint-research-centre/) — Government body, Ispra, Italy · 5 works · 974 citations - [Wageningen University & Research](https://md.research.elamur.ai/free/en/universities/wageningen-university-research/) — University, Wageningen, Netherlands · 2 works · 12 citations - [O. P. Jindal Global University](https://md.research.elamur.ai/free/en/universities/o-p-jindal-global-university/) — University, Sonīpat, India · 3 works · 22 citations 📚 Shelves (5): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 16,667 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 16,194 works · [🤝 Social Sciences](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · 1,772 works · [🤝 Sociology and Political Science](https://md.research.elamur.ai/free/en/shelves/social-sciences/sociology-and-political-science/) · 577 works · [🤝 Health](https://md.research.elamur.ai/free/en/shelves/social-sciences/health/) · 5 works 🧭 Topics (1): [🖼️ Vision & language](https://md.research.elamur.ai/free/en/topics/multimodal/) · 657 works ## Cite Cite as: Surendrabikram Thapa, Shuvam Shiwakoti, Siddhant Bikram Shah, Kritesh Rauniyar, Laxmi Thapa, Surabhi Adhikari, Kristina T. Johnson, Ali Hürriyetoğlu, Hristo Tanev and Usman Naseem. 2026. Multimodal Identification of Vaccine Content Stance 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. ```bibtex @inproceedings{thapa2026multimodal, title = {{Multimodal Identification of Vaccine Content Stance on Social Media}}, author = {Surendrabikram Thapa, Shuvam Shiwakoti, Siddhant Bikram Shah, Kritesh Rauniyar, Laxmi Thapa, Surabhi Adhikari, Kristina T. Johnson, Ali Hürriyetoğlu, Hristo Tanev and Usman Naseem}, booktitle = {Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026)}, year = {2026}, doi = {10.18653/v1/2026.eeuca-1.3}, url = {https://doi.org/10.18653/v1/2026.eeuca-1.3} } ``` - [← previous MTR-Bench: A Comprehensive Benchmark for Multi-Turn Reasoning Evaluation](https://md.research.elamur.ai/free/en/papers/li-2026-mtr-bench-comprehensive-benchmark-multi/) - [next → Nemotron-CrossThink: Scaling Self-Learning beyond Math Reasoning](https://md.research.elamur.ai/free/en/papers/akter-2026-nemotron-crossthink-scaling-self-learning/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/2/) ## References 1 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Darwin Acharya and Sunil Regmi. 2026. Linus@eeuca 2026: Multimodal and text-only approaches to vaccine-critical meme detection. In Proceedings of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA). \#\# Conclusion [find](https://scholar.google.com/scholar?q=Darwin+Acharya+and+Sunil+Regmi%2E+2026%2E+Linus%40eeuca+2026%3A+Multimodal+and+text%2Donly+approaches+to+vaccine%2Dcritical+meme+detection%2E+In+Proceedings+of+the+9th+Workshop+on+Event+Extraction+and+Understanding) *Updated 1 November 2026*