Talkographics: Learning Audience Demographics and Interests from Text to Make TV Show Recommendations

This paper presents a novel recommendation system (RS) based on the user-generated content (UGC) contributed by TV viewers via Twitter, in order to demonstrate the value UGC presents for firms. In aggregate these TV viewers’ tweets enable us to calculate the affinity between TV shows and explain the similarity between TV show audiences. We present 1) a new methodology for collecting data from social media with which to generate and test affinity networks; and 2) a new privacy-friendly UGC-based RS relying on all publicly-available text from viewers, rather than on preselected keywords. This data collection method is more flexible and generalizable than previous approaches and allows for real-world validation. We coin the term talkographics to refer to descriptions of any product’s audience revealed by the words used in their Twitter messages, and show that Twitter text can represent complex, nuanced combinations of the audiences features. To demonstrate that our RS is generalizable, we apply this approach to other product domains.