@inproceedings{ma-etal-2025-explicit,
    title = "Explicit and Implicit Data Augmentation for Social Event Detection",
    author = "Ma, Congbo  and
      Wang, Yuxia  and
      Wu, Jia  and
      Yang, Jian  and
      Du, Jing  and
      Qiu, Zitai  and
      Li, Qing  and
      Wang, Hu  and
      Nakov, Preslav",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/landing_page/2025.acl-long.412/",
    pages = "8402--8415",
    ISBN = "979-8-89176-251-0",
    abstract = "Social event detection involves identifying and categorizing important events from social media, which relies on labeled data, but annotation is costly and labor-intensive. To address this problem, we propose Augmentation framework for Social Event Detection (SED-Aug), a plug-and-play dual augmentation framework, which combines explicit text-based and implicit feature-space augmentation to enhance data diversity and model robustness. The explicit augmentation utilizes LLMs to enhance textual information through five diverse generation strategies. For implicit augmentation, we design five novel perturbation techniques that operate in the feature space on structural fused embeddings. These perturbations are crafted to keep the semantic and relational properties of the embeddings and make them more diverse. Specifically, SED-Aug outperforms the best baseline model by approximately 17.67{\%} on the Twitter2012 dataset and by about 15.57{\%} on the Twitter2018 dataset in terms of the average F1 score."
}