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Methods and Models for Monitoring Open Sources of Information in the Interests of Competitive Intelligence

Student: Sidyakina Yuliya

Supervisor: Alexander Rudchenko

Faculty: Institute of Security Studies

Educational Programme: Competitive Intelligence Analyst (Master)

Year of Graduation: 2020

In all countries of the world there are tendencies of distribution in social networks, access to the Internet. The World Wide Web makes it possible to post and study anyone from a variety of sources of information. We can observe how we observe the changing lifestyle of people around the world (the global transformation of the social behavior of citizens). Huge strata are spreading all over the world. Prohibitions and exceptions may be available to all comers (press, social networks, blogs, etc.). Meanwhile, this significant and undeniable plus is equally a drawback - the availability of publications of a wide variety of data. For untrained analytics, the indicated task can be somewhat difficult. Meanwhile, the study of man is affordable. According to the American author, US national intelligence has long and definitively been based on these facts, which contain from 60 to 98% of the final information. This work is devoted to the development and testing of methods for monitoring open sources of information that are optimal for solving the most diverse problems. The authors consider these sources in terms of reliability. The work is based on the semantic level of information. The theoretical part of the question is considered in detail in the work - these are signs, vulnerabilities that can be violated. The most interesting and informative cluster sources of information are the media, all the necessary working methods that take into account reliable evidence. The author set himself the goal of creating practically useful working methods. One of the cases was examined in detail in the work: the analyst was set the task of establishing a beneficial company, the initial data is limited by the URL. The entire analytical process with consideration in the work of the methods is visualized. The obtained result allows us to draw non-obvious, but reasonable conclusions, to eliminate the loss of time and effort. Among other things, the developed methods showed excellent results in training interns.

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