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

Abstract

OBJECTIVE\nPublic 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.\n\n\nMATERIALS AND METHODS\nUsing Nielsen s TV viewership database for the entire US panel, we presented a novel show discovery methodology for PHAs that combined (i) pattern discovery from high-dimensional data (ii) nonparametric tests for validation, and (iii) online experiments on Facebook.\n\n\nRESULTS\nThe nonparametric tests verified the robustness of the discovered associations between the popularity of certain shows and health conditions. Findings from fifty (independent) online experiments (where our awareness messages were seen by nearly 1.5 million American adults) empirically demonstrated the value of the methodology.\n\n\nDISCUSSION\nFor 2016, the methodology identified several shows whose popularities were genuinely associated with certain health conditions, opening up the possibility of health agencies embracing both big data and large-scale experimentation to address an old problem in a new way.\n\n\nCONCLUSION\nPolicy makers can repeatedly apply the methodology as new data streams in, with perhaps different feature sets, pattern discovery techniques, and online experiments running over longer periods. The comparatively lower initial investment in the methodology can pay off by identifying several shows for a potentially national television campaign. As simply a by-product, the initial investment also results in awareness messages that might reach millions of individuals.