Multidisciplinary Fusion Perspective Analysis Method for False Information Recognition

Abstract

Combating misinformation is one of the urgent social crises. Much research has shown that disinformation can lead to social panic and adversely affect society. It is crucial to promptly detect and counteract misinformation to reduce its adverse effects. Although progress in text-based fact verification has been made, the community needs further exploration into the user-oriented results. To address this gap, we integrate theories from linguistics, journalism, psychology, and cognitive science. We propose a disinformation detection algorithm based on multidimensional content analysis. This algorithm combines human factors and user perception in text interactive media to establish six dimensions for comprehensive content analysis. We have proposed a quantitative calculation method corresponding to six dimensions to detect misinformation. The average accuracy of the proposed model test on four datasets is 95.28%. The results show that this algorithm can effectively analyze from a multidisciplinary theoretical perspective and effectively identify misinformation in Chinese and English.

Keywords

• artificial intelligence
• machine learning
• support vector machines
• social computing
• natural language processing