Apologies for cross-posting. Thought many of you would find of great interest this latest experiment, which took the Internet Archive's TV Political Ad Archive of 267 campaign ads airing on monitored television stations over the last several months, split them into a sequence of images, one per second, and ran them through Google's neural network Cloud Vision API to catalog the visual contents of each frame including major objects, activities, and themes it depicts, extract any recognizable text, estimate the geographic location it captures, and identify the presence and emotional expression of any human faces. Coupled with the live airing data compiled by the Archive (http://politicaladarchive.org/) and the fact that ads were analyzed in sequence every 1 second, you can do all kinds of analyses, from which themes were aired the most and where to trends in the sequencing of themes in ads. A few high-level trends are summarized here: https://www.washingtonpost.com/news/monkey-cage/wp/2016/02/08/what-does-arti... The full JSON output capturing the data output by the Cloud Vision API for each frame is here: http://blog.gdeltproject.org/computers-watching-ads-deep-learning-meets-camp... You can download the image frames here: http://blog.gdeltproject.org/image-frames-available-for-political-ad-image-a... ~Kalev http://www.kalevleetaru.com/ http://blog.gdeltproject.org/