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

Published: 25-11-2019| Version 2 | DOI: 10.17632/5fd929tfnt.2
Contributors:
Juan Carlos Monroy Osorio,
LAURA ROJAS DE FRANCISCO,
Lina Ceballos

Description

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.

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