The Statistical Methods for Analyzing Social Media Data
Due to the spread of Internet technologies, two-way communication between people is becoming stronger and spreading not only in a certain place, but also in all corners of our world. Increased use of social media generates large amounts of data and new types of data that were not previously availabl...
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Research Centre of Industrial Problems of Development of NAS of Ukraine
2020-02-01
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doaj-f226f564be06445999f125524d809f3a2020-11-25T02:59:57ZengResearch Centre of Industrial Problems of Development of NAS of UkraineBìznes Inform2222-44592311-116X2020-02-01250526627210.32983/2222-4459-2020-2-266-272The Statistical Methods for Analyzing Social Media DataTumanov Oleksii O. 0V. N. Karazin Kharkiv National UniversityDue to the spread of Internet technologies, two-way communication between people is becoming stronger and spreading not only in a certain place, but also in all corners of our world. Increased use of social media generates large amounts of data and new types of data that were not previously available. The concepts of «social networks» and «social media» are increasingly part of social discussions, organizational strategy and scientific research. The growing interest in social networks is coupled with the dissemination of widely available network data, there is a significant increase in methods and understanding of how to analyze social media data and where the results of such researches can be used. The use of this data can be of key nature in modern sociology and be important in economics, anthropology, biology, demographics, communication research, geography, history, informatics, organizational research, political science, health, social psychology, outlook studies, sociolinguistics and others. It is an present-time affordable consumer tool, so the growing interest in the analysis and modeling of social data, as well as the relevance of this topic should not be underestimated. This article presents the methods used to analyze a wide range of social media based on relationship, business cooperation, political alliances, etc. In this context, network structure and internal information structures can be decisive. In some cases, they can have a positive impact on the efficiency of economic growth, while in others, the network structure can have destructive consequences due to the existence of many links between some central actors (e.g., the spread of phenomena such as epidemics or financial crises). This article is an introduction to consideration of the statistical models designed to examine the specifics of social media data and the effects of social processes. Approaches to the analysis of the collected data are considered, as well as issues of reliability and reliability of the results are brought up for discussion.https://www.business-inform.net/export_pdf/business-inform-2020-2_0-pages-266_272.pdfanalysis methodsdata collectionsocial mediaanalysissocial networks |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Tumanov Oleksii O. |
spellingShingle |
Tumanov Oleksii O. The Statistical Methods for Analyzing Social Media Data Bìznes Inform analysis methods data collection social media analysis social networks |
author_facet |
Tumanov Oleksii O. |
author_sort |
Tumanov Oleksii O. |
title |
The Statistical Methods for Analyzing Social Media Data |
title_short |
The Statistical Methods for Analyzing Social Media Data |
title_full |
The Statistical Methods for Analyzing Social Media Data |
title_fullStr |
The Statistical Methods for Analyzing Social Media Data |
title_full_unstemmed |
The Statistical Methods for Analyzing Social Media Data |
title_sort |
statistical methods for analyzing social media data |
publisher |
Research Centre of Industrial Problems of Development of NAS of Ukraine |
series |
Bìznes Inform |
issn |
2222-4459 2311-116X |
publishDate |
2020-02-01 |
description |
Due to the spread of Internet technologies, two-way communication between people is becoming stronger and spreading not only in a certain place, but also in all corners of our world. Increased use of social media generates large amounts of data and new types of data that were not previously available. The concepts of «social networks» and «social media» are increasingly part of social discussions, organizational strategy and scientific research. The growing interest in social networks is coupled with the dissemination of widely available network data, there is a significant increase in methods and understanding of how to analyze social media data and where the results of such researches can be used. The use of this data can be of key nature in modern sociology and be important in economics, anthropology, biology, demographics, communication research, geography, history, informatics, organizational research, political science, health, social psychology, outlook studies, sociolinguistics and others. It is an present-time affordable consumer tool, so the growing interest in the analysis and modeling of social data, as well as the relevance of this topic should not be underestimated. This article presents the methods used to analyze a wide range of social media based on relationship, business cooperation, political alliances, etc. In this context, network structure and internal information structures can be decisive. In some cases, they can have a positive impact on the efficiency of economic growth, while in others, the network structure can have destructive consequences due to the existence of many links between some central actors (e.g., the spread of phenomena such as epidemics or financial crises). This article is an introduction to consideration of the statistical models designed to examine the specifics of social media data and the effects of social processes. Approaches to the analysis of the collected data are considered, as well as issues of reliability and reliability of the results are brought up for discussion. |
topic |
analysis methods data collection social media analysis social networks |
url |
https://www.business-inform.net/export_pdf/business-inform-2020-2_0-pages-266_272.pdf |
work_keys_str_mv |
AT tumanovoleksiio thestatisticalmethodsforanalyzingsocialmediadata AT tumanovoleksiio statisticalmethodsforanalyzingsocialmediadata |
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