Clustering of media content from social networks using bigdata technology
The article deals with one of the key problems of the social network analysis – the problem of classifying accounts based on media content uploaded by users. The main difficulties are the content heterogeneity (both in format and subject) and the large volumes of data, which leads to excessive compu...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Samara National Research University
2018-10-01
|
| Series: | Компьютерная оптика |
| Subjects: | |
| Online Access: | http://computeroptics.smr.ru/KO/PDF/KO42-5/420524.pdf |
| _version_ | 1811278983096958976 |
|---|---|
| author | Igor Rytsarev Dmitriy Kirsh Alexandr Kupriyanov |
| author_facet | Igor Rytsarev Dmitriy Kirsh Alexandr Kupriyanov |
| author_sort | Igor Rytsarev |
| collection | DOAJ |
| description | The article deals with one of the key problems of the social network analysis – the problem of classifying accounts based on media content uploaded by users. The main difficulties are the content heterogeneity (both in format and subject) and the large volumes of data, which leads to excessive computational complexity of its processing and often to the complete inefficiency of traditional analysis methods. In the article, we discuss an approach to the clustering of media content from social networks based on textual annotation using BigData technology – a modern and efficient tool that allows to solve the problem of large data volume processing. To carry out computational experiments, a large sample of heterogeneous images (photographs, paintings, postcards, etc.) was collected from real Twitter accounts. The results confirmed the high quality of media content clustering, the average error was around 5 %. |
| first_indexed | 2024-04-13T00:46:46Z |
| format | Article |
| id | doaj.art-85d560358d4646f4b16357aa74db1453 |
| institution | Directory Open Access Journal |
| issn | 0134-2452 2412-6179 |
| language | English |
| last_indexed | 2024-04-13T00:46:46Z |
| publishDate | 2018-10-01 |
| publisher | Samara National Research University |
| record_format | Article |
| series | Компьютерная оптика |
| spelling | doaj.art-85d560358d4646f4b16357aa74db14532022-12-22T03:09:59ZengSamara National Research UniversityКомпьютерная оптика0134-24522412-61792018-10-0142592192710.18287/2412-6179-2018-42-5-921-927Clustering of media content from social networks using bigdata technologyIgor Rytsarev0Dmitriy Kirsh1Alexandr Kupriyanov2Samara National Research University, Moskovskoye shosse, 34, Samara, RussiaSamara National Research University, Moskovskoye shosse, 34, Samara, Russia; IPSI RAS – Branch of the FSRC “Crystallography and Photonics” RAS, Molodogvardeyskaya 151, 443001, Samara, RussiaSamara National Research University, Moskovskoye shosse, 34, Samara, Russia; IPSI RAS – Branch of the FSRC “Crystallography and Photonics” RAS, Molodogvardeyskaya 151, 443001, Samara, RussiaThe article deals with one of the key problems of the social network analysis – the problem of classifying accounts based on media content uploaded by users. The main difficulties are the content heterogeneity (both in format and subject) and the large volumes of data, which leads to excessive computational complexity of its processing and often to the complete inefficiency of traditional analysis methods. In the article, we discuss an approach to the clustering of media content from social networks based on textual annotation using BigData technology – a modern and efficient tool that allows to solve the problem of large data volume processing. To carry out computational experiments, a large sample of heterogeneous images (photographs, paintings, postcards, etc.) was collected from real Twitter accounts. The results confirmed the high quality of media content clustering, the average error was around 5 %.http://computeroptics.smr.ru/KO/PDF/KO42-5/420524.pdfcluster analysisBigData technologytext annotationsocial networksmedia content analysisk-means clusteringGoogLeNet |
| spellingShingle | Igor Rytsarev Dmitriy Kirsh Alexandr Kupriyanov Clustering of media content from social networks using bigdata technology Компьютерная оптика cluster analysis BigData technology text annotation social networks media content analysis k-means clustering GoogLeNet |
| title | Clustering of media content from social networks using bigdata technology |
| title_full | Clustering of media content from social networks using bigdata technology |
| title_fullStr | Clustering of media content from social networks using bigdata technology |
| title_full_unstemmed | Clustering of media content from social networks using bigdata technology |
| title_short | Clustering of media content from social networks using bigdata technology |
| title_sort | clustering of media content from social networks using bigdata technology |
| topic | cluster analysis BigData technology text annotation social networks media content analysis k-means clustering GoogLeNet |
| url | http://computeroptics.smr.ru/KO/PDF/KO42-5/420524.pdf |
| work_keys_str_mv | AT igorrytsarev clusteringofmediacontentfromsocialnetworksusingbigdatatechnology AT dmitriykirsh clusteringofmediacontentfromsocialnetworksusingbigdatatechnology AT alexandrkupriyanov clusteringofmediacontentfromsocialnetworksusingbigdatatechnology |