NLP-Based Verification of Message Reliability Using Semantic Network Analysis

This article focuses on methods and approaches for constructing semantic networks for textual (news) messages in media streams to identify potential sources of disinformation. The main objective is to develop a comprehensive methodology for building such networks, using key terms as the foundation f...

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Bibliographic Details
Published in:Cybernetics and systems analysis Vol. 61; no. 3; pp. 364 - 374
Main Authors: Zgurovsky, M. Z., Boldak, A. O., Yefremov, K. V., Stus, O. V., Dmytrenko, O. O.
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.05.2025
Springer Nature B.V
Subjects:
Artificial Intelligence
Content analysis
Control
Data analysis
Information science
Information sources
Instant messaging
Mathematics
Mathematics and Statistics
Messages
Natural language processing
Network analysis
Network reliability
News
Processor Architectures
Semantic analysis
Semantic relations
Semantics
Similarity
Software Engineering/Programming and Operating Systems
Systems Analysis
Systems Theory
text analysis
Frobenius measure
semantic network
verification of message reliability
Horizontal Visibility Graph algorithm
Directed Weighted Network of Terms
ISSN:1060-0396, 1573-8337
Online Access:Get full text
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Summary:This article focuses on methods and approaches for constructing semantic networks for textual (news) messages in media streams to identify potential sources of disinformation. The main objective is to develop a comprehensive methodology for building such networks, using key terms as the foundation for semantic modeling. The authors examine various text processing techniques, including preliminary computational processing, key term extraction, and identifying semantic relations between terms. The central contribution is the development of a metric for measuring semantic similarity between information messages represented as semantic networks. The proposed metric is based on the Frobenius norm and enables efficient evaluation of similarity and interconnection between texts. It enhances the accuracy of semantic content analysis, uncovers latent semantic relations, and facilitates information structuring. Using the Frobenius-based metric, the article proposes an approach for identifying reliable and unreliable information sources, supporting the validation of facts presented in news content. This approach improves the efficiency of information analysis, reveals emerging trends, and enables forecasting developments within the media space. Most importantly, it supports the detection of information influences, contributing to the maintenance of information security and the strengthening of national resilience against external threats.
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ISSN:1060-0396
1573-8337
DOI:10.1007/s10559-025-00775-x