A survey of Big Data dimensions vs Social Networks analysis

被引：21

作者：

Ianni, Michele ^{[1
]}

Masciari, Elio ^{[2
]}

Sperli, Giancarlo ^{[2
]}

机构：

[1] Univ Calabria, Dept Informat Modeling Elect & Syst, I-87036 Arcavacata Di Rende, CS, Italy

[2] Univ Naples Federico II, Dept Elect & Informat Technol DIETI, Via Claudio 21, I-80125 Naples, Italy

来源：

JOURNAL OF INTELLIGENT INFORMATION SYSTEMS | 2021年 / 57卷 / 01期

关键词：

Big Data; Social Network; Centrality measure; Fake news; COMMUNITY STRUCTURE; CENTRALITY MEASURES; FAKE NEWS; BENCHMARKING; PROPAGATION; VARIABILITY; COMPUTATION; PREDICTION; DIFFUSION; ANALYTICS;

D O I：

10.1007/s10844-020-00629-2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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).

引用

页码：73 / 100

页数：28

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