Repository logo
Communities & Collections
All of DSpace
  • English
  • العربية
  • বাংলা
  • Català
  • Čeština
  • Deutsch
  • Ελληνικά
  • Español
  • Suomi
  • Français
  • Gàidhlig
  • हिंदी
  • Magyar
  • Italiano
  • Қазақ
  • Latviešu
  • Nederlands
  • Polski
  • Português
  • Português do Brasil
  • Srpski (lat)
  • Српски
  • Svenska
  • Türkçe
  • Yкраї́нська
  • Tiếng Việt
Log In
New user? Click here to register. Have you forgotten your password?
  1. Home
  2. Browse by Author

Browsing by Author "Osaulenko O."

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Results Per Page
  • Sort Options
  • No Thumbnail Available
    Item
    Innovative Technologies in Military Analytics
    (Національна академія статистики, обліку та аудиту, 2025) Osaulenko O.; Gorobets O.
  • No Thumbnail Available
    Item
    Social Media Data in the Big Data Environment
    (Національна академія статистики, обліку та аудиту, 2021) Osaulenko O.; Horobets O.
    The article contains results of a study of social media data (SMD) which, being distinct from conventional data by their origin, require special methods for collection, processing and analysis. As shown by a literature review, in spite of great many research publications devoted to social media research and big data analysis, the SMD potential as a big data component still remains inadequately explored. Two approaches to research and analysis of SMD were highlighted in course of the study, in which SMD are addressed as an object of Internet statistics and an object of big data. When SMD are explored as an object of Internet statistics, collection of anonymized data is performed using the services that have network protocols for collection and analysis of data on social media customers using statistical methods. When SMD are explored as an object of big data, the collection is performed mostly by artifi cial intellect, whereas the storage and processing is operated by databases designed for large scopes of data and software with statistical data processing applications. The social media most popular with users in 2020 were identifi ed in the study. Statistical indicators for assessment of users’ feedback, available now for statistical assessments of social media communities, are given. The study revealed several problems which solutions would require, apart from a multifaceted and complex approach to collection and processing, highly competent teams of specialists in various subject fi elds, including experts in computations, experts in machine learning and statisticians.

DSpace software and PP Obriy copyright © 2002-2025 LYRASIS

  • Privacy policy
  • End User Agreement
  • Send Feedback