Consumption of cancer causing products (CCPs) is the root cause of many of the cancers and chronic
diseases we aim to cure with translational medicine. Tobacco and alcohol consumption is a function of highly
successful, coordinated global business models that promote consumption. The marketing of CCPs can be
seen as a disease vector that drives CCP consumption and worthy of surveillance. The present project
addresses the marketing of tobacco and alcohol to young people in the current media environment, in which
youths increasingly stream their entertainment in a relatively commercial free environment. To address
decreasing reach of traditional advertising, companies have sought to embed their brands in the entertainment
itself. Alcohol companies actively seek entertainment placement deals. While cigarette companies have
agreed not to pay for product placement, they may evade current restrictions by burying placements in the
massive streamed entertainment environment. E-cigarette companies have no such restrictions and advertise
on television. Youths obtain exposure to brands that spend more on advertising (e.g., Budweiser); these are
also the most commonly consumed brands by youth. Product placement exposures may put adolescents at
greater risk for CCP consumption. This study represents the first step in assessing that risk--to obtain
infoveillance on CCP brand prevalence in streamed entertainment media. Aim 1 involves a partnership with
machine learning researchers at the Jet Propulsion Laboratory (JPL) to develop and validate a computer
recognition system for major cigarette, e-cigarette, and alcohol brands in streamed entertainment media. Aim 1
relies on an existing, large Dartmouth media training library that includes detailed timing for tobacco and
alcohol brands from over 2000 contemporary movies. JPL researchers will use the Dartmouth media library,
combined with a large corpus of brand logos retrieved from the web, to train and validate an automated Video
Content Coding (VCC) system that identifies content associated with key alcohol and tobacco brands. The
VCC system will leverage recent techniques to construct robust object detection systems using Convolutional
Neural Networks built on large image databases, along with text extraction methods to identify brand names in
advertising logos. Second, we will deploy the automated recognition system on the Dartmouth computer
cluster to assess the frequency of major cigarette, e-cigarette, and alcohol brand placements from a large
sample of streamed/cable and movie entertainment. This proposal involves a unique multidisciplinary team of
Dartmouth behavioral scientists and NASA machine learning scientists who will leverage new technologies to
study a significant CCP marketing platform. The novel approach offers unprecedented opportunities to conduct
surveillance on CCP marketing in large media samples. Our project will set the stage for future research on
exposure to this type of marketing and its relation to CCP consumption in youths and lead to a better
understanding of the risks CCP brand placements pose to health.
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