Distant Viewing Lab

PAPERS
Distant Viewing: Analyzing Large Visual Corpora
Visual Style in Two Network-Era Sitcoms
Enriching Photography with Image Region Segmentation
DVT: Python Package for the Analysis of Visual Culture
Visualizing a Historic Photograph with a Generous Interface

SOFTWARE
Distant Viewing Toolkit (DVT)

TUTORIALS
Distant Viewing Toolkit: Demo and Example Usage
HILT2019: Image Analysis with Deep Learning

With support from:

DV Television

The Distant Viewing Television project applies computational methods to the study of television series. The project analyzes how the visual and sonic space is used by characters over a set of fourteen sitcoms from the Network Era of American Television (1952-1985), modeling a new mode of cultural analysis within TV studies.

Today, Americans spend over 1.5 hours each day, on average, streaming digital media over the internet (Nielsen 2016). Extensive scholarship in media studies has established how formal elements of moving images — such as camera angles, sound, and the construction of narrative arcs — reflect, establish, and challenge cultural norms (Mulvey 1975, Braudy 2002). In other words, moving images offer a lens into a community's ideals and values. The study of culture in the twentieth century requires, therefore, considering media as a serious source of historical evidence.

Given that long-running television series broadcast hundreds of episodes, and the major networks run dozens of series each season, previous studies of network television have had to rely on a close analysis of a subset of series, episodes, and scenes. We builds off of these with computational approaches that can analyze the contents of tens of thousands of hours of television programming. The analytical approaches offered by computer vision and what Lev Manovich has termed ``cultural analytics'' (2011) allows the project to compare and contrast within and between television series at unprecedented scale. Tools capable of applying algorithmic approaches to moving images stand to open exciting new avenues of research in digital humanities, cultural analytics, and media studies.

Bibliography