Explainable Fake News Detection With Large Language Model via Defense Among Competing Wisdom

arXiv:2405.03371v1 Announce Type: new
Abstract: Most fake news detection methods learn latent feature representations based on neural networks, which makes them black boxes to classify a piece of news without giving any justification. Existing explainable systems generate veracity justifications from investigative journalism, which suffer from debunking delayed and low efficiency. Recent studies simply assume that the justification is equivalent to the majority opinions expressed in the wisdom of crowds. However, the opinions typically contain some inaccurate or biased information since …

abstract arxiv black boxes cs.cl defense detection detection methods fake fake news feature generate giving investigative journalism journalism language language model large language large language model learn networks neural networks systems them type via