Mimicking Human Verification Behavior for News Media Credibility Evaluation
-
Published:2023-08-23
Issue:17
Volume:13
Page:9553
-
ISSN:2076-3417
-
Container-title:Applied Sciences
-
language:en
-
Short-container-title:Applied Sciences
Author:
Fan Weijian12ORCID, Wang Yongbin2ORCID, Hu Hongbin2ORCID
Affiliation:
1. School of Computer and Cyber Sciences, Communication University of China, Beijing 100024, China 2. State Key Laboratory of Media Convergence and Communication, Communication University of China, Beijing 100024, China
Abstract
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.
Funder
National Key Research and Development Program of China
Subject
Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science
Reference38 articles.
1. Mohamed, A.A., Laith, A., Alburaikan, A., and Khalifa, H.A. (2023). AOEHO: A new hybrid data replication method in fog computing for Iot application. Sensors, 23. 2. Cognition security protection about the mass: A survey of key technologies;Fan;J. Commun. Univ. China Sci. Technol.,2022 3. Ghanem, B., Rosso, P., and Rangel, F. (2018). Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), Association for Computational Linguistics. 4. Zubiaga, A., Liakata, M., and Procter, R. (2017). Proceedings of the Social Informatics: 9th International Conference, SocInfo 2017, Oxford, UK, 13–15 September 2017, Springer International Publishing. Proceedings, Part I 9. 5. Baly, R., Karadzhov, G., Alexandrov, D., Glass, J., and Nakov, P. (2018). Predicting factuality of reporting and bias of news media sources. arXiv.
|
|