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THE APPROACH FOR OBTAINING INDIRECT CHARACTERISTICS OF INTERACTIONS BETWEEN ACTORS FROM OPEN DATA OF SOCIAL NETWORKS
Abstract
The number of active users of social networks according to the latest Global Digital Statshot report from We Are Social and Hootsuite has exceeded three billion. This fact makes social networks the one of perspective sources of information for carrying out analytics of various kinds. The particular interest from a scientific point of view is the identification of interactions between individual actors and social groups. Since a significant part of the interactions are personal data, which are closed for general review, there arises the problem of determining the existence of interactions between actors on indirect characteristics. To solve this problem, it is necessary to develop new approaches, methods and algorithms that allow identifying such communications based only on indirect characteristics. The indirect characteristics can be extracted from publicly published data. One of the important concomitant factors in solving this problem is the belonging of these social networks to the Big Data category and subject to the rule of three Vs (volume, variety and velocity). Accordingly, the developed approaches, methods and algorithms should be automated and implemented using modern big data processing platforms, such as Hadoop. The paper proposes the classification of indirect characteristics of actors' interaction in social networks and the approach to their extraction using ETL procedures.
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