@inproceedings{chen-etal-2024-hotvcom,
    title = "{HOTVCOM}: Generating Buzzworthy Comments for Videos",
    author = "Chen, Yuyan  and
      Yan, Songzhou  and
      Guo, Qingpei  and
      Jia, Jiyuan  and
      Li, Zhixu  and
      Xiao, Yanghua",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Author-page-Marten-During-lu/2024.findings-acl.130/",
    doi = "10.18653/v1/2024.findings-acl.130",
    pages = "2198--2224",
    abstract = "In the era of social media video platforms, popular {\textquotedblleft}hot-comments{\textquotedblright} play a crucial role in attracting user impressions of short-form videos, making them vital for marketing and branding purpose. However, existing research predominantly focuses on generating descriptive comments or {\textquotedblleft}danmaku{\textquotedblright} in English, offering immediate reactions to specific video moments. Addressing this gap, our study introduces HOTVCOM, the largest Chinese video hot-comment dataset, comprising 94k diverse videos and 137 million comments. We also present the ComHeat framework, which synergistically integrates visual, auditory, and textual data to generate influential hot-comments on the Chinese video dataset. Empirical evaluations highlight the effectiveness of our framework, demonstrating its excellence on both the newly constructed and existing datasets."
}