Background

Podcasts are an increasingly popular form of media consumption, and represent a rich potential source of data on consumer patterns and trends. An estimated 51% of the US population has listened to a podcast; there are over 750,000 shows totalling over 30 million episodes. Research on podcasts has generally focused on predicting popular content and making use of the show and episode descriptions for text analysis and natural language processing (Tsagkias et al. 2010). Two primary metrics are used to assess popularity in podcasts: the average rating (usually based on listener ratings), and the number of reviews; the latter is often considered more important, given that it correlates with audience size.

Case study objectives

The goal for this case study project is to explore what predicts podcast popularity, given a recent dataset on podcast ratings and some information about each show (e.g., category, host, topic summary). Each team can also seek out additional covariates or transform the existing ones, such as through text analysis methods.  

Teams will be given hourly podcast popularity data for approximately 50 days preceding the competition commencement. An additional unlabelled dataset will be provided, and once your models are built and validated, each team will also submit predicted number of reviews for the unlabelled dataset (note that the podcasts in this unlabelled dataset may or may not have appeared previously in your training data, so any custom predictors you use to build your model must be reproducible given a new set of the included covariates in order to generate predictions for this dataset).   

Data description

The podcast ratings on iTunes are the most frequently used and cited source for assessing the popularity of podcasts, as it is one of the primary sources for podast media. iTunes makes available an API for current real-time ratings of the podcasts available on iTunes, and these data are used for this case study compeition. The data were collected by automated scraping techniques designed to get a large sample of most available English-language shows that are available on iTunes. The API allows for automated scraping of up to 200 shows per main category (many of which have fewer than 200 in total). Each hourly dataset is approximately 2000 rows. We only included podcasts in English and excluded video content. For a sample view of the data, see APPENDIX 1.

The case study data sets include a training set with 1,408,901 rows and ten columns, and a prediction (unlabeled) set with approximately 10 days of data.