In this study, we attempted to study the network of Twitter users and the mentions between them. Starting with a very large and incorrectly structured dataset, we used the Unix terminal (sed) and regular expressions to efficiently perform filtering and various transformations to end up with a lighter dataset. Then, using Python, we completely transformed the dataset from a linear (line by line) to a tabular format (columns), in order to load the data in iGraph. Using iGraph, we created a weighted directed graph and performed various tasks to explore the network: - Identifying basic properties of the network, such as the Number of vertices, Number of edges, Diameter of the graph, Average in-degree and Average out-degree. - Visualising the 5-day evolution of these metrics and commenting on observed fluctuations. - Identifying the important nodes of the graph, based on In-degree, Out-degree and PageRank - Performing community detections on the mention graphs, by applying fast greedy clustering, infomap clustering, and louvain clustering on the undirected versions of the 5 mention graphs. - Visualising the different communities in the mention graph.