A survey of Big Data dimensions vs Social Networks analysis

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).

Comparing workplace violence among nurses and other professionals using online articles: A social network analysis

“…Betweenness centrality is a useful indicator of the ability to control the exchange of information within a network. A high betweenness centrality score means that a word has considerable influence within a network by controlling the information between other words (Huang et al, 2014; Ianni et al, 2021; Wasserman & Faust, 1994). Eigenvector centrality and betweenness centrality use the ranking of words.…”

“…Social networks are defined as a set of words that are tied by one or more types of relations (Huang et al, 2014; Ianni et al, 2021; Wasserman & Faust, 1994). Social network analysis focuses on the structure of relationships using various indicators (Stedmon, 2014).…”

Mídias virtuais e a saúde mental durante o distanciamento social imposto pela pandemia da COVID-19

“…A escolha dessas plataformas deu-se por sua popularidade e altas taxas de dados heterogêneos que são gerados, armazenados e consumidos. As mídias sociais virtuais supracitadas possuem características peculiares, como alta produção de conteúdos diversificados, grande velocidade de compartilhamentos e outros aspectos relevantes que permitem à comunidade de pesquisa sua inserção e aprofundamento sobre as influências das interações existentes por meio da internet (Ianni, Masciari & Sperlí, 2020).…”

Collective Betweenness Centrality in Networks

“…Betweenness centrality (BC), which computes a rank for each node based on the role in communication between other nodes, is a popular measure to analyze networks [15][16][17][18]. Betweenness centrality highlights bridging nodes that have a high impact on information exchange.…”