“Who’s Watching TV?” by Ms. Jessica M. Clark
Ms. Jessica M. Clark
PhD Candidate in Information Systems
NYU Stern School of Business
Understanding the demographics of TV shows’ audiences is of vital concern to advertisers and other stakeholders. Such knowledge is traditionally learned using data sources such as Nielsen which measure individuals’ viewership using small, opt-in panels and report aggregate numbers. Massive viewership data available at the individual Set-Top Box (STB) level has led to new estimation methods, but there is a crucial weakness in how viewers are measured: it is impossible to tell with certainty which person is the one watching TV in multi-person households. This work introduces and formulates the problem of estimating which person is watching, which to our knowledge has not been addressed in the existing literature. We address this problem through four main contributions. First, we develop a novel method for estimating the likelihood that each individual in a multi-person household is watching. This method has characteristics of both multi-instance learning and domain adaptation, and adapts probabilities learned in single-person STBs to the multi-person STB setting. A core difficulty of the problem is that there are no ground truth labels telling who is actually watching; therefore, we derive a set of tasks at which models must succeed in order to demonstrate that they have succeeded at the core problem of interest. Our second contribution is thus the derivation of a set of tasks at which models must succeed in order to demonstrate that they have solved the core problem, since there are no ground-truth labels. Third, we evaluate our new method as well as two current state-of-the-art heuristic methods. The heuristic methods each fail at at least two of the necessary tasks, but the novel method we develop succeeds at all of them. Fourth, we conduct some example analyses of viewership in the context of living with others. Our solution has implications for researchers interested in understanding television viewing behavior by individuals and groups, as well as broad applications within the television advertis- ing industry and to any situation where multiple people share the same device or account but individual inferences are desired. A major TV provider is planning on deploying this method for use in their TV ad-targeting system.