Tutorial for the Looted Art Detector: Using custom indicators

Looted Art Detector: Part 2 Using custom indicators

example with : ALIU Red Flag restorers

The user can analyse provenances for any names or words that seem interesting.

The list below contains the last names of art restorers who were investigated by the OSS Art Looting Investigation Unit for their role in the art market for Nazi-looted art.

Looted Art Detector Tool: Swiss GLAMHACK2021

TRY IT!

Objective: Identify high priority artworks for provenance research

Description: Online Free Digital Tool

URL: https://artdata.pythonanywhere.com

Approach: Automatic text analysis using frequency counts

note: The frequency counts target textual indicators of UNCERTAINTY, UNRELIABILITY,  or ANONYMITY, as well as the possible presence of RED FLAG names related to NAZI-looted art, forced sales and duress sales. 

The resulting calculations do not signify that an artwork is looted. They simply quantify observations concerning the text for further analysis.

How it works: 

Let's run 1000 NEPIP provenances that contain Munich through the Looted Art Detector

In the previous post we gathered one thousand provenances of artworks listed (for the most part) by American museums on the Nazi Era Provenance Internet Portal that contain the word "Munich" or "München" in the provenance text.

In this post, using the Looted Art Detector developed at the Swiss Glamhack2020 and Glamhack2021, we rank artworks that contain a mention of Munich according to the criteria of "Uncertainty".