An AI-based System to Assist Human Fact-Checkers for Labeling Cantonese Fake News on Social Media

Zi Hen Lin; Ziwei Wang; Minzhu Zhao; Yunya Song; Liang Lan

doi:10.1109/BigData55660.2022.10020949

An AI-based System to Assist Human Fact-Checkers for Labeling Cantonese Fake News on Social Media

Zi Hen Lin, Ziwei Wang, Minzhu Zhao, Yunya Song, Liang Lan^*

^*Corresponding author for this work

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

2 Citations (Scopus)

Abstract

Preventing the spread of fake news is one of the most challenging issues in the age of social media. Traditional manual fact-checking (i.e., expert-based and crowd-sourced fact-checking) is time-consuming and labor-extensive, which cannot scale up with the unprecedented amount of dis- and mis-information on social media. Automated fact-checking based on machine learning is a promising strategy to address the scalability issues. Nevertheless, an end-to-end full automated fact-checking system without human supervision is still impractical. A more realistic solution will be developing an Artificial Intelligence (AI)-based system to facilitate the human fact-checkers during the fact-checking process. Therefore, this paper proposes a novel annotation system to facilitate human fact-checkers. With our designed procedures and schema, our developed system can help to improve the efficiency and effectiveness of human fact-checkers by automatically identifying worth-to-check news. We conduct a real-case study to demonstrate that our system can effectively identify worth-to-check news and ease the annotation process with the help of several automatic detection functions.

Original language	English
Title of host publication	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
Editors	Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
Publisher	IEEE
Pages	6766-6768
Number of pages	3
ISBN (Electronic)	9781665480451
ISBN (Print)	9781665480468
DOIs	https://doi.org/10.1109/BigData55660.2022.10020949
Publication status	Published - Dec 2022
Event	2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan Duration: 17 Dec 2022 → 20 Dec 2022 https://ieeexplore.ieee.org/xpl/conhome/10020192/proceeding

Publication series

Name	Proceedings - IEEE International Conference on Big Data

Conference

Conference	2022 IEEE International Conference on Big Data, Big Data 2022
Country/Territory	Japan
City	Osaka
Period	17/12/22 → 20/12/22
Internet address	https://ieeexplore.ieee.org/xpl/conhome/10020192/proceeding

Scopus Subject Areas

Modelling and Simulation
Computer Networks and Communications
Information Systems
Information Systems and Management
Safety, Risk, Reliability and Quality
Control and Optimization

User-Defined Keywords

Automatic fact-checking
Fake news detection
News annotation process and schema

Access to Document

10.1109/BigData55660.2022.10020949

Cite this

Lin, Z. H., Wang, Z., Zhao, M., Song, Y., & Lan, L. (2022). An AI-based System to Assist Human Fact-Checkers for Labeling Cantonese Fake News on Social Media. In S. Tsumoto, Y. Ohsawa, L. Chen, D. Van den Poel, X. Hu, Y. Motomura, T. Takagi, L. Wu, Y. Xie, A. Abe, & V. Raghavan (Eds.), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 (pp. 6766-6768). (Proceedings - IEEE International Conference on Big Data). IEEE. https://doi.org/10.1109/BigData55660.2022.10020949

Lin, Zi Hen ; Wang, Ziwei ; Zhao, Minzhu et al. / An AI-based System to Assist Human Fact-Checkers for Labeling Cantonese Fake News on Social Media. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. editor / Shusaku Tsumoto ; Yukio Ohsawa ; Lei Chen ; Dirk Van den Poel ; Xiaohua Hu ; Yoichi Motomura ; Takuya Takagi ; Lingfei Wu ; Ying Xie ; Akihiro Abe ; Vijay Raghavan. IEEE, 2022. pp. 6766-6768 (Proceedings - IEEE International Conference on Big Data).

@inproceedings{0913b43748964abd9949305c2384616f,

title = "An AI-based System to Assist Human Fact-Checkers for Labeling Cantonese Fake News on Social Media",

abstract = "Preventing the spread of fake news is one of the most challenging issues in the age of social media. Traditional manual fact-checking (i.e., expert-based and crowd-sourced fact-checking) is time-consuming and labor-extensive, which cannot scale up with the unprecedented amount of dis- and mis-information on social media. Automated fact-checking based on machine learning is a promising strategy to address the scalability issues. Nevertheless, an end-to-end full automated fact-checking system without human supervision is still impractical. A more realistic solution will be developing an Artificial Intelligence (AI)-based system to facilitate the human fact-checkers during the fact-checking process. Therefore, this paper proposes a novel annotation system to facilitate human fact-checkers. With our designed procedures and schema, our developed system can help to improve the efficiency and effectiveness of human fact-checkers by automatically identifying worth-to-check news. We conduct a real-case study to demonstrate that our system can effectively identify worth-to-check news and ease the annotation process with the help of several automatic detection functions.",

keywords = "Automatic fact-checking, Fake news detection, News annotation process and schema",

author = "Lin, {Zi Hen} and Ziwei Wang and Minzhu Zhao and Yunya Song and Liang Lan",

note = "Funding Information: The research was supported by a General Research Fund grant (HKBU 12605520) of the Research Grant Council of Hong Kong, the Public Policy Research Funding Scheme (2021.A2.047.21B) from Policy Innovation and Co-ordination Office of the Hong Kong Special Administrative Region Government, and the Interdisciplinary Research Clusters Matching Scheme (IRCMS/19-20/D04), the Initiation Grant for Faculty Niche Research Areas (RC-FNRA-IG/21-22/COMF/01) of HKBU, and the AIS Scheme (AIS 21-22/01) and CMC Scheme (CMC/21-22/003) from School of Communication, HKBU, and the Natural Science Foundation Council of China (61906161). Funding Information: The research was supported by a General Research Fund grant (HKBU 12605520) of the Research Grant Council of Hong Kong, the Public Policy Research Funding Scheme (2021.A2.047.21B) from Policy Innovation and Co-ordination Offce of the Hong Kong Special Administrative Region Government, and the Interdisciplinary Research Clusters Matching Scheme (IRCMS/19-20/D04), the Initiation Grant for Faculty Niche Research Areas (RC-FNRA-IG/21-22/COMF/01) of HKBU, and the AIS Scheme (AIS 21-22/01) and CMC Scheme (CMC/21-22/003) from School of Communication, HKBU, and the Natural Science Foundation Council of China (61906161). Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",

year = "2022",

month = dec,

doi = "10.1109/BigData55660.2022.10020949",

language = "English",

isbn = "9781665480468",

series = "Proceedings - IEEE International Conference on Big Data",

publisher = "IEEE",

pages = "6766--6768",

editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",

booktitle = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",

address = "United States",

url = "https://ieeexplore.ieee.org/xpl/conhome/10020192/proceeding",

}

Lin, ZH, Wang, Z, Zhao, M, Song, Y & Lan, L 2022, An AI-based System to Assist Human Fact-Checkers for Labeling Cantonese Fake News on Social Media. in S Tsumoto, Y Ohsawa, L Chen, D Van den Poel, X Hu, Y Motomura, T Takagi, L Wu, Y Xie, A Abe & V Raghavan (eds), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Proceedings - IEEE International Conference on Big Data, IEEE, pp. 6766-6768, 2022 IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, 17/12/22. https://doi.org/10.1109/BigData55660.2022.10020949

An AI-based System to Assist Human Fact-Checkers for Labeling Cantonese Fake News on Social Media. / Lin, Zi Hen; Wang, Ziwei; Zhao, Minzhu et al.
Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. ed. / Shusaku Tsumoto; Yukio Ohsawa; Lei Chen; Dirk Van den Poel; Xiaohua Hu; Yoichi Motomura; Takuya Takagi; Lingfei Wu; Ying Xie; Akihiro Abe; Vijay Raghavan. IEEE, 2022. p. 6766-6768 (Proceedings - IEEE International Conference on Big Data).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

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AU - Zhao, Minzhu

AU - Song, Yunya

AU - Lan, Liang

N1 - Funding Information: The research was supported by a General Research Fund grant (HKBU 12605520) of the Research Grant Council of Hong Kong, the Public Policy Research Funding Scheme (2021.A2.047.21B) from Policy Innovation and Co-ordination Office of the Hong Kong Special Administrative Region Government, and the Interdisciplinary Research Clusters Matching Scheme (IRCMS/19-20/D04), the Initiation Grant for Faculty Niche Research Areas (RC-FNRA-IG/21-22/COMF/01) of HKBU, and the AIS Scheme (AIS 21-22/01) and CMC Scheme (CMC/21-22/003) from School of Communication, HKBU, and the Natural Science Foundation Council of China (61906161). Funding Information: The research was supported by a General Research Fund grant (HKBU 12605520) of the Research Grant Council of Hong Kong, the Public Policy Research Funding Scheme (2021.A2.047.21B) from Policy Innovation and Co-ordination Offce of the Hong Kong Special Administrative Region Government, and the Interdisciplinary Research Clusters Matching Scheme (IRCMS/19-20/D04), the Initiation Grant for Faculty Niche Research Areas (RC-FNRA-IG/21-22/COMF/01) of HKBU, and the AIS Scheme (AIS 21-22/01) and CMC Scheme (CMC/21-22/003) from School of Communication, HKBU, and the Natural Science Foundation Council of China (61906161). Publisher Copyright: © 2022 IEEE.

PY - 2022/12

Y1 - 2022/12

N2 - Preventing the spread of fake news is one of the most challenging issues in the age of social media. Traditional manual fact-checking (i.e., expert-based and crowd-sourced fact-checking) is time-consuming and labor-extensive, which cannot scale up with the unprecedented amount of dis- and mis-information on social media. Automated fact-checking based on machine learning is a promising strategy to address the scalability issues. Nevertheless, an end-to-end full automated fact-checking system without human supervision is still impractical. A more realistic solution will be developing an Artificial Intelligence (AI)-based system to facilitate the human fact-checkers during the fact-checking process. Therefore, this paper proposes a novel annotation system to facilitate human fact-checkers. With our designed procedures and schema, our developed system can help to improve the efficiency and effectiveness of human fact-checkers by automatically identifying worth-to-check news. We conduct a real-case study to demonstrate that our system can effectively identify worth-to-check news and ease the annotation process with the help of several automatic detection functions.

AB - Preventing the spread of fake news is one of the most challenging issues in the age of social media. Traditional manual fact-checking (i.e., expert-based and crowd-sourced fact-checking) is time-consuming and labor-extensive, which cannot scale up with the unprecedented amount of dis- and mis-information on social media. Automated fact-checking based on machine learning is a promising strategy to address the scalability issues. Nevertheless, an end-to-end full automated fact-checking system without human supervision is still impractical. A more realistic solution will be developing an Artificial Intelligence (AI)-based system to facilitate the human fact-checkers during the fact-checking process. Therefore, this paper proposes a novel annotation system to facilitate human fact-checkers. With our designed procedures and schema, our developed system can help to improve the efficiency and effectiveness of human fact-checkers by automatically identifying worth-to-check news. We conduct a real-case study to demonstrate that our system can effectively identify worth-to-check news and ease the annotation process with the help of several automatic detection functions.

KW - Automatic fact-checking

KW - Fake news detection

KW - News annotation process and schema

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DO - 10.1109/BigData55660.2022.10020949

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T3 - Proceedings - IEEE International Conference on Big Data

SP - 6766

EP - 6768

BT - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

A2 - Tsumoto, Shusaku

A2 - Ohsawa, Yukio

A2 - Chen, Lei

A2 - Van den Poel, Dirk

A2 - Hu, Xiaohua

A2 - Motomura, Yoichi

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A2 - Wu, Lingfei

A2 - Xie, Ying

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PB - IEEE

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Lin ZH, Wang Z, Zhao M, Song Y , Lan L. An AI-based System to Assist Human Fact-Checkers for Labeling Cantonese Fake News on Social Media. In Tsumoto S, Ohsawa Y, Chen L, Van den Poel D, Hu X, Motomura Y, Takagi T, Wu L, Xie Y, Abe A, Raghavan V, editors, Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. IEEE. 2022. p. 6766-6768. (Proceedings - IEEE International Conference on Big Data). doi: 10.1109/BigData55660.2022.10020949

An AI-based System to Assist Human Fact-Checkers for Labeling Cantonese Fake News on Social Media

Abstract

Publication series

Conference

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User-Defined Keywords

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