TY - GEN
T1 - D-FEND
T2 - 37th ACM/SIGAPP Symposium on Applied Computing, SAC 2022
AU - Han, Soeun
AU - Ko, Yunyong
AU - Kim, Yushim
AU - Oh, Seong Soo
AU - Park, Heejin
AU - Kim, Sang Wook
N1 - Funding Information:
The work was supported by the National Research Foundation of Korea (NRF) under Project Number 2020R1A2B5B03001960 and 2018R1A5A7059549, and Institute of Information Communications Technology Planning Evaluation (IITP) under Project Number 2020-0-01373.
Publisher Copyright:
© 2022 ACM.
PY - 2022/4/25
Y1 - 2022/4/25
N2 - The social confusion caused by the recent pandemic of COVID-19 has been further facilitated by fake news diffused via social media on the Internet. For this reason, many studies have been proposed to detect fake news as early as possible. The content-based detection methods consider the difference between the contents of true and fake news articles. However, they suffer from the two serious limitations: (1) the publisher can manipulate the content of a news article easily, and (2) the content depends upon the language, with which the article is written. To overcome these limitations, the diffusion-based fake news detection methods have been proposed. The diffusion-based methods consider the difference among the diffusion patterns of true and fake news articles on social media. Despite its success, however, the lack of the diffusion information regarding to the COVID-19 related fake news prevents from studying the diffusion-based fake news detection methods. Therefore, for overcoming the limitation, we propose a diffusion-based fake news detection framework (D-FEND), which consists of four components: (C1) diffusion data collection, (C2) analysis of the data and feature extraction, (C3) model training, and (C4) inference. Our work contributes to the effort to mitigate the risk of infodemics during a pandemic by (1) building a new diffusion dataset, named CoAID+, (2) identifying and addressing the class imbalance problem of CoAID+, and (3) demonstrating that D-FEND successfully detects fake news articles with 88.89% model accuracy on average.
AB - The social confusion caused by the recent pandemic of COVID-19 has been further facilitated by fake news diffused via social media on the Internet. For this reason, many studies have been proposed to detect fake news as early as possible. The content-based detection methods consider the difference between the contents of true and fake news articles. However, they suffer from the two serious limitations: (1) the publisher can manipulate the content of a news article easily, and (2) the content depends upon the language, with which the article is written. To overcome these limitations, the diffusion-based fake news detection methods have been proposed. The diffusion-based methods consider the difference among the diffusion patterns of true and fake news articles on social media. Despite its success, however, the lack of the diffusion information regarding to the COVID-19 related fake news prevents from studying the diffusion-based fake news detection methods. Therefore, for overcoming the limitation, we propose a diffusion-based fake news detection framework (D-FEND), which consists of four components: (C1) diffusion data collection, (C2) analysis of the data and feature extraction, (C3) model training, and (C4) inference. Our work contributes to the effort to mitigate the risk of infodemics during a pandemic by (1) building a new diffusion dataset, named CoAID+, (2) identifying and addressing the class imbalance problem of CoAID+, and (3) demonstrating that D-FEND successfully detects fake news articles with 88.89% model accuracy on average.
KW - COVID-19 dataset
KW - diffusion-based detection
KW - fake news detection
UR - http://www.scopus.com/inward/record.url?scp=85130402383&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85130402383&partnerID=8YFLogxK
U2 - 10.1145/3477314.3507134
DO - 10.1145/3477314.3507134
M3 - Conference contribution
AN - SCOPUS:85130402383
T3 - Proceedings of the ACM Symposium on Applied Computing
SP - 1771
EP - 1778
BT - Proceedings of the 37th ACM/SIGAPP Symposium on Applied Computing, SAC 2022
PB - Association for Computing Machinery
Y2 - 25 April 2022 through 29 April 2022
ER -