The methods summarized in this video tutorial series are based on the open source Digital Methods Initiative - Twitter Capture and Analysis Toolkit (DMI-TCAT) that allows media researchers to collect tweets off the STREAM API (application programming interface) on an ongoing basis. With DMI - TCAT and the open source data visualization software Gephi, social data in the millions of units is quickly and easily sorted by algorithms to find users or items of importance on Twitter, such as in the Fig. 1 below. While these figures and the data gathered though the DMI-TCAT do not provide full firehose access to all historical tweets, they do provide a generally representative sample of tweets that is relatively proportional to the total volume of tweets being posted at any given time (Gerlitz & Rieder, 2013; Groshek & Tandoc, 2016). For more details on the DMI-TCAT and its operation, we encourage readers to visit its github page (https://github.com/digitalmethodsinitiative/dmi-tcat) and note that this cloud-based analytics program is free and customizable. The specific techniques covered in the methodology reported here in text and expanded upon in the video tutorial series include how to: • Model influence users by sizing nodes with the betweenness centrality algorithm; • Identify community groups by adding color using the modularity algorithm; • Spatialize networks through applying the openord algorithm; • Make social network graphs dynamic and interactive online.
Keywords: Algorithmic sorting and filtering; Data visualization; Network analysis.
© 2020 The Authors.