TY - GEN
T1 - Beyond fear go viral
T2 - 14th International Conference on Knowledge and Smart Technology, KST 2022
AU - Thaipisutikul, Tipajin
AU - Shih, Timothy K.
AU - Enkhbat, Avirmed
AU - Aditya, Wisnu
AU - Shih, Huang Chia
AU - Mongkolwat, Pattanasak
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - With the restrictions in our daily life activities under the current situation of the covid-19 pandemic worldwide, billions of people rely on social media platforms to share and obtaining covid-19 related news information. This made social media platforms easily be used as a source of myths and disinformation, which can cause severe public risks. It is thus of vital importance to constraint the spread of misinformation to the public. Although many works have shown promising results on the misinformation detection problem, only a few studies focus on the infodemic detection during the covid-19 pandemic, especially in the low resource language like Thai. Therefore, in this paper, we conduct extensive experiments on the real-world social network datasets to detect misinformation about covid-19 targeting both English and Thai languages. In particular, we perform an exploratory data analysis to get the statistic and characteristics of real and fake content. Also, we evaluate a series of three feature extraction, seven traditional machine learning, and eleven deep learning methods in detecting the fabricated content on social media platforms. The experimental results demonstrate that the transformer-based model significantly outperforms other deep learning and traditional machine learning methods in all metrics, including accuracy and F-measure.
AB - With the restrictions in our daily life activities under the current situation of the covid-19 pandemic worldwide, billions of people rely on social media platforms to share and obtaining covid-19 related news information. This made social media platforms easily be used as a source of myths and disinformation, which can cause severe public risks. It is thus of vital importance to constraint the spread of misinformation to the public. Although many works have shown promising results on the misinformation detection problem, only a few studies focus on the infodemic detection during the covid-19 pandemic, especially in the low resource language like Thai. Therefore, in this paper, we conduct extensive experiments on the real-world social network datasets to detect misinformation about covid-19 targeting both English and Thai languages. In particular, we perform an exploratory data analysis to get the statistic and characteristics of real and fake content. Also, we evaluate a series of three feature extraction, seven traditional machine learning, and eleven deep learning methods in detecting the fabricated content on social media platforms. The experimental results demonstrate that the transformer-based model significantly outperforms other deep learning and traditional machine learning methods in all metrics, including accuracy and F-measure.
KW - covid-19
KW - deep learning
KW - infodemic detection
KW - machine learning
KW - social media
KW - text classification
UR - http://www.scopus.com/inward/record.url?scp=85127964589&partnerID=8YFLogxK
U2 - 10.1109/KST53302.2022.9729077
DO - 10.1109/KST53302.2022.9729077
M3 - 會議論文篇章
AN - SCOPUS:85127964589
T3 - KST 2022 - 2022 14th International Conference on Knowledge and Smart Technology
SP - 1
EP - 6
BT - KST 2022 - 2022 14th International Conference on Knowledge and Smart Technology
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 26 January 2022 through 29 January 2022
ER -