Impact of Artificial Intelligence News Source Credibility Identification System on Effectiveness of Media Literacy Education

Tosti H.C. Chiang*, Chih Shan Liao, Wei Ching Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

During presidential elections and showbusiness or social news events, society has begun to address the risk of fake news. The Sustainable Development Goals 4 for Global Education Agenda aims to “ensure inclusive and equitable quality education and promote lifelong learning opportunities for all” by 2030. As a result, various nations have deemed media literacy education a required competence in order for audiences to maintain a discerning attitude and to verify messages rather than automatically believing them. This study developed a highly efficient message discrimination method using new technology using artificial intelligence and big data information processing containing general news and content farm message data on approximately 938,000 articles. Deep neural network technology was used to create a news source credibility identification system. Media literacy was the core of the experimental course design. Two groups of participants used different methods to perform message discrimination. The results revealed that the system significantly expanded the participants’ knowledge of media literacy. The system positively affected the participants’ attitude, confidence, and motivation towards media literacy learning. This research provides a method of identifying fake news in order to ensure that audiences are not affected by fake messages, thereby helping to maintain a democratic society.

Original languageEnglish
Article number4830
JournalSustainability (Switzerland)
Volume14
Issue number8
DOIs
Publication statusPublished - 2022 Apr 1

Keywords

  • artificial intelligence
  • learning attitude
  • learning effectiveness
  • media literacy education
  • news source credibility identification

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Environmental Science (miscellaneous)
  • Energy Engineering and Power Technology
  • Management, Monitoring, Policy and Law

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