@inproceedings{zeng-etal-2024-leveraging-social,
title = "Leveraging Social Context for Humor Recognition and Sense of Humor Evaluation in Social Media with a New {C}hinese Humor Corpus - {H}umor{WB}",
author = "Zeng, Zeyuan and
Li, Zefeng and
Yang, Liang and
Lin, Hongfei",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.908",
pages = "10393--10402",
abstract = "With the development of the Internet, social media has produced a large amount of user-generated data, which brings new challenges for humor computing. Traditional humor computing research mainly focuses on the content, while neglecting the information of interaction relationships in social media. In addition, both content and users are important in social media, while existing humor computing research mainly focuses on content rather than people. To address these problems, we model the information transfer and entity interactions in social media as a heterogeneous graph, and create the first dataset which introduces the social context information - HumorWB, which is collected from Chinese social media - Weibo. Two humor-related tasks are designed in the dataset. One is a content-oriented humor recognition task, and the other is a novel humor evaluation task. For the above tasks, we purpose a graph-based model called SCOG, which uses heterogeneous graph neural networks to optimize node representation for downstream tasks. Experimental results demonstrate the effectiveness of feature extraction and graph representation learning methods in the model, as well as the necessity of introducing social context information.",
}
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<abstract>With the development of the Internet, social media has produced a large amount of user-generated data, which brings new challenges for humor computing. Traditional humor computing research mainly focuses on the content, while neglecting the information of interaction relationships in social media. In addition, both content and users are important in social media, while existing humor computing research mainly focuses on content rather than people. To address these problems, we model the information transfer and entity interactions in social media as a heterogeneous graph, and create the first dataset which introduces the social context information - HumorWB, which is collected from Chinese social media - Weibo. Two humor-related tasks are designed in the dataset. One is a content-oriented humor recognition task, and the other is a novel humor evaluation task. For the above tasks, we purpose a graph-based model called SCOG, which uses heterogeneous graph neural networks to optimize node representation for downstream tasks. Experimental results demonstrate the effectiveness of feature extraction and graph representation learning methods in the model, as well as the necessity of introducing social context information.</abstract>
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%0 Conference Proceedings
%T Leveraging Social Context for Humor Recognition and Sense of Humor Evaluation in Social Media with a New Chinese Humor Corpus - HumorWB
%A Zeng, Zeyuan
%A Li, Zefeng
%A Yang, Liang
%A Lin, Hongfei
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F zeng-etal-2024-leveraging-social
%X With the development of the Internet, social media has produced a large amount of user-generated data, which brings new challenges for humor computing. Traditional humor computing research mainly focuses on the content, while neglecting the information of interaction relationships in social media. In addition, both content and users are important in social media, while existing humor computing research mainly focuses on content rather than people. To address these problems, we model the information transfer and entity interactions in social media as a heterogeneous graph, and create the first dataset which introduces the social context information - HumorWB, which is collected from Chinese social media - Weibo. Two humor-related tasks are designed in the dataset. One is a content-oriented humor recognition task, and the other is a novel humor evaluation task. For the above tasks, we purpose a graph-based model called SCOG, which uses heterogeneous graph neural networks to optimize node representation for downstream tasks. Experimental results demonstrate the effectiveness of feature extraction and graph representation learning methods in the model, as well as the necessity of introducing social context information.
%U https://aclanthology.org/2024.lrec-main.908
%P 10393-10402
Markdown (Informal)
[Leveraging Social Context for Humor Recognition and Sense of Humor Evaluation in Social Media with a New Chinese Humor Corpus - HumorWB](https://aclanthology.org/2024.lrec-main.908) (Zeng et al., LREC-COLING 2024)
ACL