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).

中文翻译：

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大数据维度与社交网络分析的调查


社交网络 (SN) 的普遍扩散产生了前所未有的异构数据量。因此，传统方法由于其固有特性：大量用户生成的数据（文本、视频、图像和音频）、数据异构性和高速生成率，很快就变得不切实际于现实生活应用。更详细地说，通过流行的社交网络（即 Facebook (https://www.facebook.com/)、Twitter (https://www.twitter.com/)、Instagram (https:// www.instagram.com/)、LinkedIn (https://www.linkedin.com/)) 为研究和行业社区在分析用户行为、用户交互、链接演变、意见传播等方面提出了相当有趣的挑战。其他重要方面。本次调查将重点关注过去二十年对大数据范式（所谓的大数据 6 V）定义的维度的此类数据进行的分析。