A survey of Big Data dimensions vs Social Networks analysis

The pervasive diffusion of Social Networks (SN) produced an unprecedented amount of heterogeneous data. Thus, traditional approaches quickly became unpractical for real life applications due their intrinsic properties: large amount of user-generated data (text, video, image and audio), data heteroge...

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Detalles Bibliográficos
Autores principales: Ianni, Michele, Masciari, Elio, Sperlí, Giancarlo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7649712/
https://www.ncbi.nlm.nih.gov/pubmed/33191981
http://dx.doi.org/10.1007/s10844-020-00629-2
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author Ianni, Michele
Masciari, Elio
Sperlí, Giancarlo
author_facet Ianni, Michele
Masciari, Elio
Sperlí, Giancarlo
author_sort Ianni, Michele
collection PubMed
description The pervasive diffusion of Social Networks (SN) produced an unprecedented amount of heterogeneous data. Thus, traditional approaches quickly became unpractical for real life applications due their intrinsic properties: large amount of user-generated data (text, video, image and audio), data heterogeneity and high speed generation rate. More in detail, the analysis of user generated data by popular social networks (i.e Facebook (https://www.facebook.com/), Twitter (https://www.twitter.com/), Instagram (https://www.instagram.com/), LinkedIn (https://www.linkedin.com/)) poses quite intriguing challenges for both research and industry communities in the task of analyzing user behavior, user interactions, link evolution, opinion spreading and several other important aspects. This survey will focus on the analyses performed in last two decades on these kind of data w.r.t. the dimensions defined for Big Data paradigm (the so called Big Data 6 V’s).
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spelling pubmed-76497122020-11-09 A survey of Big Data dimensions vs Social Networks analysis Ianni, Michele Masciari, Elio Sperlí, Giancarlo J Intell Inf Syst Article The pervasive diffusion of Social Networks (SN) produced an unprecedented amount of heterogeneous data. Thus, traditional approaches quickly became unpractical for real life applications due their intrinsic properties: large amount of user-generated data (text, video, image and audio), data heterogeneity and high speed generation rate. More in detail, the analysis of user generated data by popular social networks (i.e Facebook (https://www.facebook.com/), Twitter (https://www.twitter.com/), Instagram (https://www.instagram.com/), LinkedIn (https://www.linkedin.com/)) poses quite intriguing challenges for both research and industry communities in the task of analyzing user behavior, user interactions, link evolution, opinion spreading and several other important aspects. This survey will focus on the analyses performed in last two decades on these kind of data w.r.t. the dimensions defined for Big Data paradigm (the so called Big Data 6 V’s). Springer US 2020-11-09 2021 /pmc/articles/PMC7649712/ /pubmed/33191981 http://dx.doi.org/10.1007/s10844-020-00629-2 Text en © The Author(s) 2020 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Ianni, Michele
Masciari, Elio
Sperlí, Giancarlo
A survey of Big Data dimensions vs Social Networks analysis
title A survey of Big Data dimensions vs Social Networks analysis
title_full A survey of Big Data dimensions vs Social Networks analysis
title_fullStr A survey of Big Data dimensions vs Social Networks analysis
title_full_unstemmed A survey of Big Data dimensions vs Social Networks analysis
title_short A survey of Big Data dimensions vs Social Networks analysis
title_sort survey of big data dimensions vs social networks analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7649712/
https://www.ncbi.nlm.nih.gov/pubmed/33191981
http://dx.doi.org/10.1007/s10844-020-00629-2
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