Data Visualization of the Social Media Content of Colombiamoda in Relation to the City Image Reconstruction of Medellín

This file is a table with processed data that presents a complex network analysis of social media content related to a mega event (Colombiamoda) and city branding (the city image of Medellín). The data was collected via a web scrapping from January to December 2017 in the platforms of Facebook, LinkedIn, Twitter, and Instagram, and using the hashtags of #Colombiamoda2017, #Colombiamoda, and #Medellín. The definitions of the columns in the table are: - Label: The name of the node that is identified in the network. - Clustering (Holland & Leinhardt, 1971): In graph theory, a clustering is a measure of the degree to which nodes in a graph tend to cluster together. Evidence suggests that in most real-world networks, and in particular social networks, nodes tend to create tightly knit groups characterized by a relatively high density of ties; this likelihood tends to be greater than the average probability of a tie randomly established between two nodes. - Degree (Reinhard, 2005): In graph theory, the degree (or valency) of a vertex of a graph is the number of edges that are incident to the vertex, and in a multigraph, loops are counted twice. - Pageranks (Brin & Page, 1998): An iterative algorithm that measures the importance of each node within the network. - Eccentricity (Alexandra & Wellman, 2011): the distance from a given starting node to the farthest node from it in the network. - Closeness centrality (Sabidussi, 1966): In a connected graph, closeness centrality (or closeness) of a node is a measure of centrality in a network, calculated as the reciprocal of the sum of the length of the shortest paths between the node and all other nodes in the graph. Thus, the more central a node is, the closer it is to all other nodes. - Harmonic closeness centrality (Marchiori & Latora, 2000): In a (not necessarily connected) graph, the harmonic centrality reverses the sum and reciprocal operations in the definition of closeness centrality. - Betweeness centrality (Freeman, 1977): In graph theory, betweenness centrality is a measure of centrality in a graph based on shortest paths. For every pair of vertices in a connected graph, there exists at least one shortest path between the vertices such that either the number of edges that the path passes through (for unweighted graphs) or the sum of the weights of the edges (for weighted graphs) is minimized. The betweenness centrality for each vertex is the number of these shortest paths that pass through the vertex.本文件为一张包含经处理数据的表格，呈现了针对大型活动哥伦比亚时装周（Colombiamoda）与城市品牌塑造——即麦德林（Medellín）城市形象——相关社交媒体内容开展的复杂网络分析。 该数据于2017年1月至12月间，通过网络爬虫从Facebook、LinkedIn、Twitter及Instagram平台采集，所用话题标签为#Colombiamoda2017、#Colombiamoda与#Medellín。 表格各列的定义如下： - 标签（Label）：网络中被识别出的节点名称。 - 聚类系数（Clustering, Holland & Leinhardt, 1971）：图论中用于衡量图内节点倾向于聚集程度的指标。相关研究表明，在多数现实世界网络尤其是社交网络中，节点往往会形成联结密度相对较高的紧密群体，这种聚集的可能性高于随机选取两个节点建立联结的平均概率。 - 度（Degree, Reinhard, 2005）：图论中，顶点的度（又称价）指与该顶点相关联的边的数量；在多重图中，自环需被计数两次。 - 网页排名（Pageranks, Brin & Page, 1998）：一种用于衡量网络中各节点重要性的迭代算法。 - 离心率（Eccentricity, Alexandra & Wellman, 2011）：指网络中某一给定起始节点到距离其最远节点的距离。 - 接近中心性（Closeness centrality, Sabidussi, 1966）：在连通图中，节点的接近中心性（又称接近度）是衡量网络中心性的指标，计算方式为该节点与图内所有其他节点间最短路径长度之和的倒数。因此，节点的中心性越高，其与所有其他节点的距离就越近。 - 调和接近中心性（Harmonic closeness centrality, Marchiori & Latora, 2000）：在（不一定连通的）图中，调和中心性对接近中心性定义中的求和与求倒数操作进行了反转。 - 中介中心性（Betweenness centrality, Freeman, 1977）：图论中基于最短路径衡量网络中心性的指标。在连通图的每一对顶点之间，均存在至少一条最短路径——对于非加权图，该路径的边数最少；对于加权图，则为路径边的权重之和最小。每个顶点的中介中心性，即为经过该顶点的这类最短路径的数量。