Multilevel Model Analysis to Detection of the Fake News

다국어 초록 (Multilingual Abstract)

Social media has changed into a platform that people can create, consume and exchange information, including fake news. With the growing influence of fake news, people need to have the ability to distinguish fake news from real news. Fake news can be defined as an article that includes inappropriate content, ranging from random misinformation to intentionally inaccurate and deceitful information. Different emotions cause different judgments, which means fake news encouraged peoples emotional dependence.
People can block fake news primarily by two approaches. The linguistic approach is to find language leakage or deceptive cue in the content of the article. The network approach is using network properties and behavior that rely on deceptive language and leakage cues to predict deception. Statistical models are hierarchical linear models (HLM) and structural equation models (SEM).
The governments must come up with regulations for combating fake news. We have to go through a fact-checking process in advance to see if the news is trustworthy or not before accepting any information. Also, we should not redistribute the news, such as sharing it with others or posting it on our social media, until it is revealed whether the news is accurate or not.

목차 (Table of Contents)

• 1. Introduction
• 2. Literature Review
• 3. Data and Method
• 4. Suggestions and Conclusion
• Reference