[gov3009-l] 10/18 Applied Stats: Christopher Lucas

Dana Higgins danahiggins at fas.harvard.edu
Mon Oct 16 13:50:14 EDT 2017


Hi everyone!

This week at the Applied Statistics Workshop we will be welcoming *Christopher
Lucas*, graduate student in the Harvard Government Department. He will be
presenting his job talk entitled *A Model for Political Video: The Audio
Video Neural Network*.  Please find the abstract below and on the Applied
Stats website here
<https://projects.iq.harvard.edu/applied.stats.workshop-gov3009>.



As usual, we will meet at noon in CGIS Knafel Room 354 and lunch will be
provided.  See you all there!

-- Dana Higgins




*Title:* *A Model for Political Video: The Audio Video Neural Network*


*Abstract:*  In the 2016 election cycle political campaigns spent over $4.4
billion on television advertisements. What type of content do campaigns use
in their efforts to influence voter behavior? Past efforts to answer
questions such as these, where media and television content are the topic
of interest, require researchers to engage in herculean efforts to manually
watch and classify video content. This is time consuming and prohibitively
expensive for most researchers. I develop a novel, general approach to
video classification, the Audio-Video Neural Network (AVNN), which is the
first contribution from political science to deep learning. The AVNN
recovers subtle categories of interest to political scientists like “fear”
and “negativity” while also successfully learning to parse more topical
classes like “political advertisement” and “cable news.” Importantly, the
model I propose can learn from both visual and audio features. I
demonstrate the AVNN by analyzing campaign advertisements in American
elections, first showing that my model can be used to separate political
campaign advertisements from cable news. Second, I show how subtle video
labels like “appeals to fear” can also be recovered. All methods described
here are implemented in easy-to-use Rand Python packages
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