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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Vydané v:	Cybernetics and systems analysis Ročník 61; číslo 3; s. 364 - 374
Hlavní autori:	Zgurovsky, M. Z., Boldak, A. O., Yefremov, K. V., Stus, O. V., Dmytrenko, O. O.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 01.05.2025 Springer Nature B.V
Predmet:	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
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Abstract	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.
AbstractList	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.
Author	Yefremov, K. V. Stus, O. V. Zgurovsky, M. Z. Dmytrenko, O. O. Boldak, A. O.
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SubjectTerms	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
Title	NLP-Based Verification of Message Reliability Using Semantic Network Analysis
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