Vaccination-related memes on social media play an increasingly influential role in shaping public perception of immunization, often spreading both supportive messaging and vaccine-critical narratives through multimodal communication. Detecting such content is challenging due to the combined use of images, embedded text, sarcasm, humor, and cultural references. This paper presents an overview of the Shared Task on Multimodal Identification of Vaccine Critical Content on Social Media, organized as part of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026) at ACL 2026. The task is based on the VaxMeme dataset, a large-scale collection of vaccination-related memes annotated into three classes: Vaccine-critical, Neutral, and Pro-vaccine. A total of 77 participants registered for the competition, with 25 teams submitting systems for evaluation. Participating approaches included transformer-based multimodal architectures, vision-language models, ensemble methods, and instruction-tuned large language models. The best-performing system achieved a macro F1-score of 0.8494. This shared task provides insights into the strengths and limitations of current multimodal approaches for vaccine stance detection and highlights future directions for robust public health misinformation analysis.

Online gaming communities are increasingly affected by toxic communication, including harassment, threats, hate speech, and extremist content. Detecting such behavior is challenging due to the short, noisy, multilingual, and highly imbalanced nature of gaming chat data. To advance research in this area, we organized the Shared Task on Fine-Grained Toxicity Detection in Online Gaming at EEUCA 2026, co-located with ACL 2026. The task is based on the GameTox dataset, containing approximately 53,000 annotated chat utterances from World of Tanks across six toxicity categories. A total of 102 participants took part, and 35 teams submitted systems exploring approaches such as domain-adaptive pretraining, multilingual transfer learning, contrastive learning, LLM-based augmentation, and ensemble methods. Systems were evaluated using macro-averaged F1-score, with the top system achieving 0.7041 Macro F1. This paper presents an overview of the shared task, dataset, evaluation framework, participant methods, and key findings.

This paper presents an overview of the 9th Workshop on Event Extraction and Understanding: Challenges and Applications (EEUCA 2026), held in conjunction with ACL 2026. Formerly known as CASE, the workshop continues its mission of bringing together researchers from natural language processing, machine learning, computational social science, and related disciplines to advance research on event extraction and understanding. This year’s edition particularly emphasized the growing influence of large language models (LLMs), multimodal learning, and weakly supervised methodologies in event extraction research. The workshop featured six regular research papers covering topics such as low-resource event extraction, reflective multi-agent architectures, symbolic auditing of procedural events, geopolitical event extraction, and generative event extraction strategies. In addition, EEUCA 2026 hosted two shared tasks focusing on toxicity detection in gaming communities and multimodal vaccine-critical meme analysis, attracting broad international participation and encouraging research on socially impactful applications of AI. The workshop highlights current advances, emerging challenges, and future directions in multilingual, multimodal, and socially aware event extraction systems.

This paper presents an overview of the Shared Task on Multimodal Hate and Sentiment Understanding in Low-Resource Memes, organized as part of the Second Workshop on Challenges in Processing South Asian Languages (CHiPSAL 2026) at LREC 2026. The task addresses automated content understanding in low-resource settings by focusing on monolingual Nepali memes written in Devanagari script. Built upon the NeMeme dataset, the task comprises two subtasks: (1) binary hate speech detection and (2) three-class sentiment analysis. The competition attracted 23 teams for hate detection and 13 teams for sentiment analysis. Participating teams employed diverse strategies, including late-fusion multimodal architectures combining multilingual text encoders with vision models, caption-based approaches using large vision-language models, and ensemble techniques. The top-performing system achieved macro-F1 scores of 80.52% on hate detection and 68.81% on sentiment analysis using a late-fusion hybrid architecture with discriminative learning rates. Our analysis reveals that multimodal fusion consistently outperforms unimodal baselines, sentiment analysis poses greater challenges than hate detection due to increased semantic nuance, and the scarcity of Devanagari-centric pretrained models remains a significant bottleneck. This shared task establishes a benchmark for multimodal understanding in low-resource South Asian languages and provides insights for developing inclusive content moderation systems.

The proliferation of health misinformation in Low-Resource Languages (LRLs) poses a severe threat to public health, yet automated detection remains critically under-studied due to the scarcity of high-quality benchmarks. We address this gap by introducing Nep-Health-Misinfo, a novel human-verified corpus for health misinformation identification in Nepali. The dataset was developed by adapting four foundational benchmarks (Monkeypox-V1, Monkeypox-V2, COVID-19, and CoAID) through a systematic Machine Translation Post-Editing (MTPE) protocol involving native experts. Our evaluation of Neural Machine Translation (NMT) systems reveals a significant translation asymmetry: while state-of-the-art (SOTA) systems achieve a BLEU score of 43.21 on factual health data, performance degrades sharply on deceptive narratives, with BLEU and TER scores dropping to 19.11 and 62.42, respectively. To establish robust baselines, we benchmark seven recent open-weight Large Language Models (LLMs), including Qwen2.5-7B-Instruct, Gemma-3-4B-IT, and Ministral-8B-Instruct, across zero-shot and few-shot settings. For the few-shot evaluation, we compare stochastic sampling against a K-means centroid-based approach for semantically representative exemplar selection. Experimental results indicate that Qwen2.5-7B-Instruct achieves a peak Macro F1-score of 0.8488, improving over its zero-shot performance (0.7188) on the same dataset. Our findings demonstrate that while few-shot prompting effectively mitigates distribution shifts in low-resource medical contexts, performance remains highly sensitive to the semantic density of exemplars. This work provides the first human-verified Nepali health misinformation corpus. All code and resources are available at https://github.com/SUJAL390/Nep-Health-Misinfo-CHIPSAL-LREC.