@inproceedings{ho-etal-2021-hidden,
title = "Hidden Advertorial Detection on Social Media in {C}hinese",
author = "Ho, Meng-Ching and
Chuang, Ching-Yun and
Hsu, Yi-Chun and
Chang, Yu-Yun",
editor = "Lee, Lung-Hao and
Chang, Chia-Hui and
Chen, Kuan-Yu",
booktitle = "Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)",
month = oct,
year = "2021",
address = "Taoyuan, Taiwan",
publisher = "The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)",
url = "https://aclanthology.org/2021.rocling-1.31",
pages = "243--251",
abstract = "Nowadays, there are a lot of advertisements hiding as normal posts or experience sharing in social media. There is little research of advertorial detection on Mandarin Chinese texts. This paper thus aimed to focus on hidden advertorial detection of online posts in Taiwan Mandarin Chinese. We inspected seven contextual features based on linguistic theories in discourse level. These features can be further grouped into three schemas under the general advertorial writing structure. We further implemented these features to train a multi-task BERT model to detect advertorials. The results suggested that specific linguistic features would help extract advertorials.",
}
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<abstract>Nowadays, there are a lot of advertisements hiding as normal posts or experience sharing in social media. There is little research of advertorial detection on Mandarin Chinese texts. This paper thus aimed to focus on hidden advertorial detection of online posts in Taiwan Mandarin Chinese. We inspected seven contextual features based on linguistic theories in discourse level. These features can be further grouped into three schemas under the general advertorial writing structure. We further implemented these features to train a multi-task BERT model to detect advertorials. The results suggested that specific linguistic features would help extract advertorials.</abstract>
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%0 Conference Proceedings
%T Hidden Advertorial Detection on Social Media in Chinese
%A Ho, Meng-Ching
%A Chuang, Ching-Yun
%A Hsu, Yi-Chun
%A Chang, Yu-Yun
%Y Lee, Lung-Hao
%Y Chang, Chia-Hui
%Y Chen, Kuan-Yu
%S Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021)
%D 2021
%8 October
%I The Association for Computational Linguistics and Chinese Language Processing (ACLCLP)
%C Taoyuan, Taiwan
%F ho-etal-2021-hidden
%X Nowadays, there are a lot of advertisements hiding as normal posts or experience sharing in social media. There is little research of advertorial detection on Mandarin Chinese texts. This paper thus aimed to focus on hidden advertorial detection of online posts in Taiwan Mandarin Chinese. We inspected seven contextual features based on linguistic theories in discourse level. These features can be further grouped into three schemas under the general advertorial writing structure. We further implemented these features to train a multi-task BERT model to detect advertorials. The results suggested that specific linguistic features would help extract advertorials.
%U https://aclanthology.org/2021.rocling-1.31
%P 243-251
Markdown (Informal)
[Hidden Advertorial Detection on Social Media in Chinese](https://aclanthology.org/2021.rocling-1.31) (Ho et al., ROCLING 2021)
ACL
- Meng-Ching Ho, Ching-Yun Chuang, Yi-Chun Hsu, and Yu-Yun Chang. 2021. Hidden Advertorial Detection on Social Media in Chinese. In Proceedings of the 33rd Conference on Computational Linguistics and Speech Processing (ROCLING 2021), pages 243–251, Taoyuan, Taiwan. The Association for Computational Linguistics and Chinese Language Processing (ACLCLP).