@inproceedings{ding-etal-2025-multi,
    title = "A Multi-Level Benchmark for Causal Language Understanding in Social Media Discourse",
    author = "Ding, Xiaohan  and
      Ping, Kaike  and
      {\c{C}}ar{\i}k, Buse  and
      Rho, Eugenia",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.emnlp-main.1464/",
    doi = "10.18653/v1/2025.emnlp-main.1464",
    pages = "28764--28778",
    ISBN = "979-8-89176-332-6",
    abstract = "Understanding causal language in informal discourse is a core yet underexplored challenge in NLP. Existing datasets largely focus on explicit causality in structured text, providing limited support for detecting implicit causal expressions, particularly those found in informal, user-generated social media posts. We introduce CausalTalk, a multi-level dataset of five years of Reddit posts (2020{--}2024) discussing public health related to the COVID-19 pandemic, among which 10,120 posts are annotated across four causal tasks: (1) binary causal classification, (2) explicit vs. implicit causality, (3) cause{--}effect span extraction, and (4) causal gist generation. Annotations comprise both gold-standard labels created by domain experts and silver-standard labels generated by GPT-4o and verified by human annotators.CausalTalk bridges fine-grained causal detection and gist-based reasoning over informal text. It enables benchmarking across both discriminative and generative models, and provides a rich resource for studying causal reasoning in social media contexts."
}