@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 and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.130",
pages = "2198--2224",
abstract = "In the era of social media video platforms, popular {``}hot-comments{''} 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 {``}danmaku{''} 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.",
}
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<abstract>In the era of social media video platforms, popular “hot-comments” 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 “danmaku” 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.</abstract>
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%0 Conference Proceedings
%T HOTVCOM: Generating Buzzworthy Comments for Videos
%A Chen, Yuyan
%A Yan, Songzhou
%A Guo, Qingpei
%A Jia, Jiyuan
%A Li, Zhixu
%A Xiao, Yanghua
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F chen-etal-2024-hotvcom
%X In the era of social media video platforms, popular “hot-comments” 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 “danmaku” 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.
%U https://aclanthology.org/2024.findings-acl.130
%P 2198-2224
Markdown (Informal)
[HOTVCOM: Generating Buzzworthy Comments for Videos](https://aclanthology.org/2024.findings-acl.130) (Chen et al., Findings 2024)
ACL
- Yuyan Chen, Songzhou Yan, Qingpei Guo, Jiyuan Jia, Zhixu Li, and Yanghua Xiao. 2024. HOTVCOM: Generating Buzzworthy Comments for Videos. In Findings of the Association for Computational Linguistics ACL 2024, pages 2198–2224, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.