Mimicking Human Verification Behavior for News Media Credibility Evaluation

The widespread popularity of digital technology has enabled the rapid dissemination of news. However, it has also led to the emergence of "fake news" and the development of a media ecosystem with serious prejudices. If early warnings about the source of fake news are received, this provides better outcomes in preventing its spread. Therefore, the issue of understanding and evaluating the credibility of media has received increasing attention. This work proposes a model of evaluating news media credibility called MiBeMC, which mimics the structure of human verification behavior in networks. Specifically, we first construct an intramodule information feature extractor to simulate the semantic analysis behavior of human information reading. Then, we design a similarity module to mimic the process of obtaining additional information. We also construct an aggregation module. This simulates human verification of correlated content. Finally, we apply regularized adversarial training strategy to train the MiBeMC model. The ablation study results demonstrate the effectiveness of MiBeMC. For the CLEF-task4 development and test dataset, the performance of the MiBeMC over state-of-the-art baseline methods is evaluated and found to be superior.