{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/wildfirecan-mmd-a-multimodal-dataset-for","title":"WildFireCan-MMD: A Multimodal Dataset for Classification of User-Generated Content During Wildfires in Canada","arxiv_id":"2504.13231","date":"2025-04-17","proceeding":null,"authors":["Braeden Sherritt","Isar Nejadgholi","Marzieh Amini"],"abstract":"Rapid information access is vital during wildfires, yet traditional data sources are slow and costly. Social media offers real-time updates, but extracting relevant insights remains a challenge. We present WildFireCan-MMD, a new multimodal dataset of X posts from recent Canadian wildfires, annotated across twelve key themes. Evaluating both vision-language models and custom-trained classifiers, we show that while zero-shot prompting offers quick deployment, even simple trained models outperform them when labelled data is available. Our best-performing transformer-based fine-tuned model reaches 83% f-score, outperforming gpt4 by 23%. As a use case, we demonstrate how this model can be used to uncover trends during wildfires. Our findings highlight the enduring importance of tailored datasets and task-specific training. Importantly, such datasets should be localized, as disaster response requirements vary across regions and contexts.","url_abs":"https://arxiv.org/abs/2504.13231v2","url_pdf":"https://arxiv.org/pdf/2504.13231v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"wildfirecan-mmd-a-multimodal-dataset-for","repo_url":"https://github.com/Multimodal-Social-Media-Data-Analysis/WildfireCanMMD-Multimedia-Classification-on-user-generated-content-During-Wildfires-in-Canada","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"disaster-response","task_name":"Disaster Response"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}