Algorithm Design to Judge Fake News based on Bigdata and Artificial Intelligence




The clear and specific objective of this study is to design a false news discriminator algorithm for news articles transmitted on a text-based basis and an architecture that builds it into a system (H/W configuration with Hadoop-based in-memory technology, Deep Learning S/W design for bigdata and SNS linkage). Based on learning data on actual news, the government will submit advanced “fake news” test data as a result and complete theoretical research based on it. The need for research proposed by this study is social cost paid by rumors (including malicious comments) and rumors (written false news) due to the flood of fake news, false reports, rumors and stabbings, among other social challenges. In addition, fake news can distort normal communication channels, undermine human mutual trust, and reduce social capital at the same time. The final purpose of the study is to upgrade the study to a topic that is difficult to distinguish between false and exaggerated, fake and hypocrisy, sincere and false, fraud and error, truth and false.


1. Introduction
2. Related Works
2.1 Artificial Intelligence
2.2 Data Mining
3. Research Design of Judge Fake News based on Artificial Intelligence
4. Algorithm Design of Judge Fake News based on Artificial Intelligence
5. Conclusions


  • Jangmook Kang Department of Bigdata & Industry Security, Namseoul University
  • Sangwon Lee Department of Computer & Software Engineering, Wonkwang University


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