The high-level overview of social media content search engine

A. O. Savelev, A. Yu Karpova, D. V. Chaykovskiy, A. D. Vilnin, A. Yu Kaida, S. A. Kuznetsov, L. O. Igumnov, N. G. Maksimova

Результат исследований: Материалы для журналарецензирование


An increasing amount of social networks users-generated data is the most remarkable research challenge nowadays. Despite the progress in the field of semistructured data processing algorithms creation, even initial data collection could not be treated as issues that have been optimally solved. The paper covers a high-level overview of the automated social media content search system. The proposed structure enables to implement instruments for multisource content extraction tasks as well as supporting of identification processes of new patterns, which describe a certain type of content. Issues of Search engine organization, logically unified extracted data repository and possible content classification techniques with the appropriate knowledge base's application are considered. Under the work, existing approaches and automated web-data extraction methods have been analyzed; social media API's functions and limits, as well as ways of semistructured data storage system organization, have been studied. The planned result's application area is automation and informational support of sociological research based on the social media content analysis techniques namely a content propagation simulation in interconnected groups; social and personal anomy study; clarification of the weak linkage's strength concept.

Язык оригиналаАнглийский
Номер статьи012097
ЖурналIOP Conference Series: Materials Science and Engineering
Номер выпуска1
СостояниеОпубликовано - 20 янв 2021
Событие14th International Forum on Strategic Technology, IFOST 2019 - Tomsk, Российская Федерация
Продолжительность: 14 окт 201917 окт 2019

ASJC Scopus subject areas

  • Materials Science(all)
  • Engineering(all)

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