TY - GEN
T1 - A Study of Cantonese Covid-19 Fake News Detection on Social Media
AU - Wang, Ziwei
AU - Zhao, Minzhu
AU - Chen, Yu
AU - Song, Yunya
AU - Lan, Liang
N1 - Funding Information:
This work was supported by the Research Grant Council of Hong Kong (RGC/HKBU12605520), the Public Policy Research Funding Scheme (2021.A2.047.21B) from Policy Innovation and Co-ordination Office of the HKSARG, the Interdisciplinary Research Clusters Matching Scheme (IRCMS/19-20/D04) and the AIS Scheme (Ref. AIS 21-22/01) from Hong Kong Baptist University, and the Natural Science Foundation Council of China (61906161).
Publisher Copyright:
© 2021 IEEE.
PY - 2021/12
Y1 - 2021/12
N2 - With the prevalence of social media, fake news has become one of the greatest challenges in journalism, which has weakened public trust in news outlets and authorities. During the COVID-19 epidemic, the widely circulated pandemic-related fake news on social media misleads or threatens the public. Recent works have investigated fake news detection on social platforms in English and Mandarin, though Cantonese fake news has been understudied. To pave the way for Cantonese COVID-19 fake news detection, we first presented an annotated COVID-19 related Cantonese fake news dataset collected from a popular local discussion forum in Hong Kong. Then, we explored the dataset by applying topic modeling to identify the topics that contain the most significant amount of fake news. Moreover, we evaluated both traditional machine learning algorithms and deep learning algorithms for Cantonese fake news detection. Our empirical results show that deep learning based methods perform slightly better than traditional machine learning methods on TF-IDF features.
AB - With the prevalence of social media, fake news has become one of the greatest challenges in journalism, which has weakened public trust in news outlets and authorities. During the COVID-19 epidemic, the widely circulated pandemic-related fake news on social media misleads or threatens the public. Recent works have investigated fake news detection on social platforms in English and Mandarin, though Cantonese fake news has been understudied. To pave the way for Cantonese COVID-19 fake news detection, we first presented an annotated COVID-19 related Cantonese fake news dataset collected from a popular local discussion forum in Hong Kong. Then, we explored the dataset by applying topic modeling to identify the topics that contain the most significant amount of fake news. Moreover, we evaluated both traditional machine learning algorithms and deep learning algorithms for Cantonese fake news detection. Our empirical results show that deep learning based methods perform slightly better than traditional machine learning methods on TF-IDF features.
KW - Cantonese Text Analytics
KW - Fake News Detection
KW - Topic Modeling
UR - http://www.scopus.com/inward/record.url?scp=85125352473&partnerID=8YFLogxK
U2 - 10.1109/BigData52589.2021.9671722
DO - 10.1109/BigData52589.2021.9671722
M3 - Conference proceeding
T3 - Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021
SP - 6052
EP - 6054
BT - IEEE International Conference on Big Data 2021
A2 - Chen, Yixin
A2 - Ludwig, Heiko
A2 - Tu, Yicheng
A2 - Fayyad, Usama
A2 - Zhu, Xingquan
A2 - Hu, Xiaohua Tony
A2 - Byna, Suren
A2 - Liu, Xiong
A2 - Zhang, Jianping
A2 - Pan, Shirui
A2 - Papalexakis, Vagelis
A2 - Wang, Jianwu
A2 - Cuzzocrea, Alfredo
A2 - Ordonez, Carlos
T2 - 2021 IEEE International Conference on Big Data, Big Data 2021
Y2 - 15 December 2021 through 18 December 2021
ER -