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大数据维度与社交网络分析的调查

A survey of Big Data dimensions vs Social Networks analysis.

作者信息

Ianni Michele, Masciari Elio, Sperlí Giancarlo

机构信息

DIMES - Department of Informatics, Modeling, Electronics and Systems, University of Calabria, 87036 Arcavacata, CS Italy.

Department of Electrical and Information Technology (DIETI), University of Naples Federico II, via Claudio 21, 80125 Naples, Italy.

出版信息

J Intell Inf Syst. 2021;57(1):73-100. doi: 10.1007/s10844-020-00629-2. Epub 2020 Nov 9.

Abstract

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).

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a278/7649712/c9aca0d91ede/10844_2020_629_Fig2_HTML.jpg

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