Coding Twitter Conversations with Communities of True Crime Podcast Listeners: A Methods Comparison

In the digital era, data collection and analysis are made more efficient using software. This abstract compares methodology used in a previous study to code online conversations of true crime podcast listeners, specifically by differentiating by-hand analysis from the use of Orange Data Mining software. With the rise in popularity of true crime podcasts and violent crime, this previous study employed tweets scraped from Twitter utilizing common hashtags of popular true crime podcasts, which were coded and analyzed for shared themes. Initially, due to time constraints placed on the study, TAGS, a Twitter hashtag scraping platform, gathered so few tweets that the researcher coded data by-hand. A secondary analysis garnered a larger sample, permitting the use of Orange. For the purpose of this comparative analysis, the researcher focuses on differences between by-hand analysis conducted by Orange, indicating that by-hand methods result in "overlooking the forest" whereas Orange assists in sorting the "trees" [1]. Additionally, by-hand coding is necessary for smaller samples whereas Orange aids in sifting through larger data sets.