Pattern discovery, validation, and online experiments: a methodology for discovering television shows for public health announcements.

Objective: Public Health Announcements (PHAs) on television are a means of raising awareness about risk behaviors and chronic conditions. PHAs' scarce airtime puts stress on their target audience reach. We seek to help health campaigns select television shows for their PHAs about smoking, binge drinking, drug overdose, obesity, diabetes, STDs, and other conditions using available statistics.

Predicting Presidential Election Outcomes from What People Watch.

In a recent article by Barfar and Padmanabhan (2015), we demonstrated how television viewership data could predict presidential election outcomes in the United States. In this article, we examine predictive models using a snapshot of Nielsen's national data on television viewership. The study is conducted with high-dimensional low sample size (HDLSS) data, whereby we conduct a comparative analysis with and without feature reduction on the data from the 2012 elections.

Does Television Viewership Predict Presidential Election Outcomes?

The days of surprise about actual election outcomes in the big data world are likely to be fewer in the years ahead, at least to those who may have access to such data. In this paper we highlight the potential for forecasting the Unites States presidential election outcomes at the state and county levels based solely on the data about viewership of television programs. A key consideration for relevance is that given the infrequent nature of elections, such models are useful only if they can be trained using recent data on viewership.