@inproceedings{luo-etal-2025-exploring,
    title = "Exploring and Detecting Self-disclosure in Multi-modal posts on {C}hinese Social Media",
    author = "Luo, Jingbao  and
      Liu, Ming  and
      Huo, Aoli  and
      Hu, Fujing  and
      Li, Gang  and
      Njust, Wupeng",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1173/",
    doi = "10.18653/v1/2025.findings-emnlp.1173",
    pages = "21510--21527",
    ISBN = "979-8-89176-335-7",
    abstract = "Self-disclosure can provide psychological comfort and social support, but it also carries the risk of unintentionally revealing sensitive information, leading to serious privacy concerns. Research on self-disclosure in Chinese multimodal contexts remains limited, lacking high-quality corpora, analysis, and methods for detection. This work focuses on self-disclosure behaviors on Chinese multimodal social media platforms and constructs a high-quality text-image corpus to address this critical data gap. We systematically analyze the distribution of self-disclosure types, modality preferences, and their relationship with user intent, uncovering expressive patterns unique to the Chinese multimodal context. We also fine-tune five multimodal large language models to enhance self-disclosure detection in multimodal scenarios. Among these models, the Qwen2.5-omni-7B achieved a strong performance, with a partial span F1 score of 88.2{\%}. This study provides a novel research perspective on multimodal self-disclosure in the Chinese context."
}