Earticle

초록

영어

The development of social media is beneficial for users to quickly access various types of information online. However, this can be a risky for teenagers under the age of 18 years because they may become exposed to information that is unsuitable for them. It is important to classify restricted and unrestricted content to protect teenagers’ online safety because teenagers are more likely to be negatively affected by biased and harmful content than adults are. We suggest a strategy for classifying restricted and unrestricted content in this study by examining content comments. We collected and cleaned comments obtained from YouTube. Word2vec was used to display comments as vectors, and the classifier was established using convolutional neural network and long short-term memory. We hope our works can contribute to making the social media environment more secure to protect the physical and mental health of teenagers.

목차

Abstract
Introduction
Literature review
Background of the deep learning model
Word2vec
Convolutional neural network model (CNN)
Long Short-Term Memory (LSTM)
Research methodology
The Proposed Hybrid CNN-LSTM model
CNN Layer
LSTM Layer
Experiments
Datasets
Experimental Setting
Results and discussion
Conclusion
References

키워드

저자

• 장수평 [ 순천향대학교 경영학과 ]
• 최재원 [ 순천향대학교경영학과 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국경영정보학회 정기 학술대회 [KMIS Conference]

• 간기

  반년간

• 수록기간

  1990~2025

• 십진분류

  KDC 325 DDC 658