Open AccessIntellectual analysis of information about users of social networks
Author(s) -
Timofei Shestakov,
AUTHOR_ID,
Iurii Leonov,
Александр Кузьменко,
Anna Sazonova,
Rodion Filippov,
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AUTHOR_ID,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2022
Publication title -
prikladnaâ matematika i voprosy upravleniâ
Language(s) - English
Resource type - Journals
eISSN - 2782-4500
pISSN - 2499-9873
DOI - 10.15593/2499-9873/2021.4.05
Subject(s) - computer science , relevance (law) , content analysis , tonality , social network analysis , data science , information retrieval , analytics , social network (sociolinguistics) , world wide web , social media , art , musical , social science , sociology , political science , law , visual arts
Social networks began to play an important role in the informatization of society. Experts from all over the world are researching social network data to solve various tasks, such as creating popular content, conducting advertising campaigns, meeting the information needs of society, ensuring state security, etc. The analysis of social networks is understood as the solution of such tasks as determining the tonality of the text, determining the target portrait of the audience, searching for associative rules, calculating community performance indicators and data visualization. The article considers the relevance of solving the problem, analyzes the results of previous work, examines the audience's reaction to content, builds a target portrait of subscribers of various communities, examines the relationship between user interests. The initial data of the study are social networks, or rather informational messages, opinions, subnets and communities, individual users, external nodes.The paper considers the classification of social network analysis systems (such as Brand Analytics, IQBuzz, Agorapulse, Semantic Force, Talkwalker) according to the following criteria: users, analysis methods, objects of analysis, data sources, features.To determine the audience's reaction to the content, the method of determining the tonality of the text was applied by analyzing comments to the content. The cluster analysis method was used to determine the target profile of users in a particular community. To find patterns between the user's interests in the work, the frequency analysis of sets of elements was considered. The search for associative rules was carried out using the Apriori algorithm. As a result, the works are presented in the form of graphs and diagrams. In the course of the work, an integrated approach to solving problems was used, which made it possible to create an automated information and analytical system that can be used as analytical tools in this area.