26 Mar
2018
26 Mar
'18
12:08 p.m.
My apologies everyone! Please find the corrected title and abstract for
Michelle's talk below
*Title:* * Understanding visual messages: visual framing and the Bag of
Visual Words *
*Abstract:* Political communication is a central element of several
political dynamics. Its visual component is crucial in understanding the
origin, characteristics and consequences of the messages sent between
political figures, media and citizens. However, visual features have been
largely overlooked in Political Science. In this project, I implement
computer vision and image retrieval techniques to measure and understand
messages conveyed in pictures. More specifically, the article focuses on
the analysis of the content and structure of images of Black Lives Matter
movement (BLM) protests. For this purpose, the article presents and details
the implementation of a Bag of (Visual) Words (BoVW). This method drawn
from the field of Computer Science allows researchers to build an
Image-Visual Word matrix that emulates the Document-Term matrix in text
analysis in order to feed models and classifiers that can provide insights
about the content of visual material. Preliminary results from the
application of a Structural Topic Model to a corpus of images posted by
U.S. newspapers show that conservative outlets tend to include “darker"
elements in their depictions of protests: they show more nocturnal events
and features like smoke, fire and police patrols than liberal outlets.
Overall, the article sheds light on the characteristics and consequences of
visual means of communication and persuasion, and provides a useful
technique for an accurate analysis and measurement of messages in pictures.
-- Dana Higgins
On Mon, Mar 26, 2018 at 11:59 AM, Dana Higgins <danahiggins(a)fas.harvard.edu>
wrote:
Hi everyone!
This week at the Applied Statistics Workshop we will be welcoming *Michelle
Torres*, a graduate student in Political Science and Statistics at
Washington University in St Louis. She will be presenting work entitled *Understanding
visual messages: visual framing and the Bag of Visual Words*. 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:* * Understanding visual messages: visual framing and the Bag of
Visual Words *
*Abstract:* How should one perform matching in observational studies
when the units are text documents? The lack of randomized assignment of
documents into treatment and control groups may lead to systematic
differences between groups on high-dimensional and latent features of text
such as topical content and sentiment. Standard balance metrics, used to
measure the quality of a matching method, fail in this setting. We present
a framework for matching documents that decomposes matching methods into
two parts: (1) a text representation, and (2) a distance metric. We
consider various methods that can be used at each step and conduct a
systematic multifactor evaluation experiment using human subjects to
identify the methods that dominate. We also show that our framework can be
used to produce matches with higher subjective match quality than current
state-of-the-art techniques. We then apply our chosen method to a
substantive debate in the study of media bias using a novel data set of
front page news articles from thirteen news sources. Media bias is composed
of topic selection bias and presentation bias; using our matching method to
control for topic selection, we find that both components contribute
significantly to media bias, though some news sources rely on one component
more than the other.