I am proud to present podTeller, a demo for predicting podcast preference. podTeller is a proof of concept system that analyzes a podcast and estimates its level of listener preference. Listener preference reflects the power of the podcast to draw audience interest and is useful for predicting if a podcast has the potential to be popular.
podTeller -- Predict Podcast Preference
podTeller is trained on 250 feeds from all 16 podcast categories in iTunes. It uses 20 easily extractable features from the podcast feed. What striked me most was when I tested the winning podcasts from Podcast Awards 2008, and podTeller estimated user preference for the winners pretty close to 100%!
Use podTeller to see how your own podcast performs, to bid for next year’s winners, or to just have fun! [0]
If you have ideas on how to improve podTeller, or comments/funny stories after your interaction with it, please don’t hesitate to share them in the comments!
[0] podTeller is a proof-of-concept system. Use it at your own risk, we are not liable for damages of any type that may occur from the use of the system. 
Manos Tsagkias, Martha Larson, and Maarten de Rijke, submitted a paper at the European Conference on Information Retrieval (ECIR) about predicting podcast preference using easily extracted features from the podcasts feeds. It is based on our previous work on Podcred: A Framework for Analyzing Podcast Preference. The data we used is pulled from Apple iTunes. The paper will be presented at ECIR 2009, held this year in Toulouse, France between 6 and 9 April 2009. The abstract follows:
Podcasts display an unevenness characteristic of domains dominated by user generated content, resulting in potentially radical variation of the user preference they enjoy. We report on work that uses easily extractable surface features of podcasts in order to achieve solid performance on two podcast preference prediction tasks: classification of preferred vs. non-preferred podcasts and ranking podcasts by level of preference. We identify features with good discriminative potential by carrying out manual data analysis, resulting in a refinement of the indicators of an existent podcast preference framework.
If you are interested, you can download Exploiting Surface Features for the Prediction of Podcast Preference(pdf), or the presentation slides (pdf).
Update: A proof-of-concept system based on this research is available; its name is podTeller.