Investigating the Difference of Fake News Source Credibility Recognition between ANN and BERT Algorithms in Artificial Intelligence
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Misinformation and Its Impacts · Spam and Phishing Detection · Hate Speech and Cyberbullying Detection
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Graduate Institute of Mass Communication, National Taiwan Normal University, Taipei 106, Taiwan
Funding
Ministry of Science and Technology · Ministry of Education (MOE)
References (45)
Show source-provided references
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