<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dmytro Lande</style></author><author><style face="normal" font="default" size="100%">Ihor Subach</style></author><author><style face="normal" font="default" size="100%">Olexander Puchkov</style></author><author><style face="normal" font="default" size="100%">Artem Soboliev</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A Clustering Method for Information Summarization and Modelling a Subject Domain</style></title><secondary-title><style face="normal" font="default" size="100%">Information &amp; Security: An International Journal</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">clustering method</style></keyword><keyword><style  face="normal" font="default" size="100%">CyberAggregator</style></keyword><keyword><style  face="normal" font="default" size="100%">information summarization</style></keyword><keyword><style  face="normal" font="default" size="100%">social media monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">subject domain</style></keyword><keyword><style  face="normal" font="default" size="100%">visualization</style></keyword><keyword><style  face="normal" font="default" size="100%">words network</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2021</style></year></dates><volume><style face="normal" font="default" size="100%">50</style></volume><pages><style face="normal" font="default" size="100%">79-86 </style></pages><abstract><style face="normal" font="default" size="100%">&lt;p style=&quot;margin-left:19.85pt;&quot;&gt;The article presents a discriminant cluster analysis method used to form real-time models of subject areas and digests based on automatic analysis of a large number of messages from social networks. It is based on estimating the discriminant value of terms. Cluster analysis, like the well-known LSA algorithm, provides a matrix representation of the data. The novelty is in using the most significant discriminant values as centroids to define clusters.&lt;/p&gt;&lt;p style=&quot;margin-left:19.85pt;&quot;&gt;The algorithm is simplified; it does not involve referencing to the adjacency matrix, definition of eigenvectors. Its complexity is O(N2), where K is the number of clusters and N &amp;ndash; the number of reference terms. If it is necessary to improve the quality of the proposed approach, the defined centroids can be transferred as input data for other known algorithms. Based on the above algorithm, toolkits for the formation of a language network and digests were developed and embedded in the &amp;ldquo;CyberAggregator&amp;rdquo; system, which provides accumulation, processing, summarization of data from social networks on cybersecurity issues.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Dmytro Lande</style></author><author><style face="normal" font="default" size="100%">Igor Subach</style></author><author><style face="normal" font="default" size="100%">Alexander Puchkov</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A System for Analysis of Big Data from Social Media</style></title><secondary-title><style face="normal" font="default" size="100%">Information &amp; Security: An International Journal</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">big data</style></keyword><keyword><style  face="normal" font="default" size="100%">Cyber Aggregator</style></keyword><keyword><style  face="normal" font="default" size="100%">cyber security</style></keyword><keyword><style  face="normal" font="default" size="100%">OSINT</style></keyword><keyword><style  face="normal" font="default" size="100%">social media monitoring</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2020</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2020</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">47</style></volume><pages><style face="normal" font="default" size="100%">44-61</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;The article presents the basic principles of building and using a monitoring and analysis system of social media on cybersecurity, based on the concepts of Big Data, Data/Text Mining, Information Extraction, Complex Networks. The authors substantiate information technologies for creating a system of content monitoring, selection of relevant information from social networks, implementation of search engines for their refinement by users, saving queries as RSS feeds, and maintaining personal databases in client applications.&lt;/p&gt;&lt;p&gt;The described OSINT system is based on collection of information from open sources, its analysis, preparation and timely delivery of the final product to the customer in order to solve certain intelligence tasks. Hence, the system is the result of a systematic collection, processing and analysis of the necessary publicly available information. It is based on the application of methods and tools of information retrieval, data analysis and aggregation of information flows, and is used for social media content monitoring as a component of decision support systems for information and cybersecurity.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><section><style face="normal" font="default" size="100%">44</style></section></record></records></xml>