Investigating the Difference of Fake News Source Credibility Recognition between ANN and BERT Algorithms in Artificial Intelligence

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

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

1 引文 斯高帕斯(Scopus)

摘要

Fake news permeating life through channels misleads people into disinformation. To reduce the harm of fake news and provide multiple and effective news credibility channels, the approach of linguistics is applied to a word-frequency-based ANN system and semantics-based BERT system in this study, using mainstream news as a general news dataset and content farms as a fake news dataset for the models judging news source credibility and comparing the difference in news source credibility recognition between ANN and BERT. The research findings show high similarity in the highest and lowest hit rates between the ANN system and the BERT system (Liberty Time had the highest hit rate, while ETtoday and nooho.net had the lowest hit rates). The BERT system presents a higher and more stable overall source credibility recognition rate than the ANN system (BERT 91.2% > ANN 82.75%). Recognizing news source credibility through artificial intelligence not only could effectively enhance people’s sensitivity to news sources but, in the long term, could cultivate public media literacy to achieve the synergy of fake news resistance with technology.

原文英語
文章編號7725
期刊Applied Sciences (Switzerland)
12
發行號15
DOIs
出版狀態已發佈 - 2022 8月

ASJC Scopus subject areas

  • 材料科學(全部)
  • 儀器
  • 工程 (全部)
  • 製程化學與技術
  • 電腦科學應用
  • 流體流動和轉移過程

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