TY - GEN
T1 - Fighting against Fake News on Newly-Emerging Crisis
T2 - 33rd Companion of the ACM World Wide Web Conference, WWW 2023
AU - Yang, Migyeong
AU - Park, Chaewon
AU - Kang, Jiwon
AU - Lee, Daeun
AU - Choi, Daejin
AU - Han, Jinyoung
N1 - Publisher Copyright:
© 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.
PY - 2024/5/13
Y1 - 2024/5/13
N2 - As social media users can easily access, generate, and spread information regardless of its authenticity, the proliferation of fake news related to public health has become a serious problem. Since these rumors have caused severe social issues, detecting them in the early stage is imminent. Therefore, in this paper, we propose a deep learning model that can debunk fake news on COVID-19, as a case study, at the initial stage of emergence. The evaluation with a newly-collected dataset consisting of both the COVID-19 and Non-COVID-19 fake news claims demonstrates that the proposed model achieves high performance, indicating that the model can identify fake news on COVID-19 in the early stage with a small amount of data. We believe that our methodology and findings can be applied to detect fake news on newly-emerging and critical topics, which should be performed with insufficient resources.
AB - As social media users can easily access, generate, and spread information regardless of its authenticity, the proliferation of fake news related to public health has become a serious problem. Since these rumors have caused severe social issues, detecting them in the early stage is imminent. Therefore, in this paper, we propose a deep learning model that can debunk fake news on COVID-19, as a case study, at the initial stage of emergence. The evaluation with a newly-collected dataset consisting of both the COVID-19 and Non-COVID-19 fake news claims demonstrates that the proposed model achieves high performance, indicating that the model can identify fake news on COVID-19 in the early stage with a small amount of data. We believe that our methodology and findings can be applied to detect fake news on newly-emerging and critical topics, which should be performed with insufficient resources.
KW - COVID-19
KW - Early detection
KW - Fake News
UR - https://www.scopus.com/pages/publications/85194488724
U2 - 10.1145/3589335.3651506
DO - 10.1145/3589335.3651506
M3 - Conference contribution
AN - SCOPUS:85194488724
T3 - WWW 2024 Companion - Companion Proceedings of the ACM Web Conference
SP - 718
EP - 721
BT - WWW 2024 Companion - Companion Proceedings of the ACM Web Conference
PB - Association for Computing Machinery, Inc
Y2 - 13 May 2024 through 17 May 2024
ER -
