Earticle

초록

영어

Social media has become one of the most popular channels to keep updated with daily news because it can quickly and easily access information. This advantage is used by malicious people to spread fake news widely. Since the COVID-19 pandemic, fake news has become a huge social problem, causing people to panic and misunderstand how to cure or protect themselves from the virus. So, the goal of this research is to use deep learning as the Recurrent Neural Network (RNN) model to find fake news about COVID-19 in the Thai language on social media and help filter information by classifying real and fake news.

목차

ABSTRAC
Ⅰ. Introduction
Ⅱ. Literature Review
2.1. Recurrent Neural Network (RNN)
2.2. Long Short-Term Memory (LSTM)
2.3. Gated Recurrent Unit (GRU)
2.4. Word2Vec
2.5. Related Works
Ⅲ. Method
3.1. Data Pre-processing
3.2. Model
3.3. Result
Ⅳ. Conclusion
Acknowledgments

키워드

저자

• Rutchaneewan Kowirat [ Master's Degree Student, Department of Mathematics, Faculty of Science, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand ] Corresponding Author
• Laor Boongasame [ Lecturer, Department of Mathematics, Faculty of Science, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  Asia Pacific Journal of Information Systems

• 간기

  계간

• pISSN

  2288-5404

• eISSN

  2288-6818

• 수록기간

  1990~2026

• 등재여부

  KCI 등재,SCOPUS

• 십진분류

  KDC 325 DDC 658