Detector Tool Tutorial: ranking by number of Red Flag and Restitution Case Names

This post, in the educational series on the experimental digital tool for analysing provenance texts, demonstrates Ranking by Red Flag Names (photo: Albright-Knox).

Work in progress. Feedback welcome.

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". 

DATASET: Text Analysis Challenge - Detect Looted Art - GLAMhack2020

Download  Provenance Texts Dataset: CSV 

Text Analysis Challenge: Detect Looted Art.

Help Automate Analysis, Flagging and Ranking of Museum Art Provenance Texts by the Probability of a Hidden History

The Question: How to sift through the millions of objects in museums to identify top priorities for intensive research by humans? 

The Goal: Automatically Classify and Rank 60,000+ art provenance texts by probability that further research will turn up a deliberately concealed history of looting, forced sale, theft or forgery. 

The Challenge: Analyse texts quickly for Red Flags, quantify, detect patterns, classify, rank, and learn. Whatever it takes to produce a reliable list of top suspects

For this challenge several datasets will be provided.

1) DATASET: 60,000+ art provenance texts for analysis

(example)

https://www.harvardartmuseums.org/collections/object/296887	[Pierre Matisse Gallery, New York, New York], by 1932, to M. Gutmann, 1936. Maurice Wertheim, by 1937, bequest, to Fogg Art Museum, 1951.;NOTE: Provenance derived from "Degas to Matisse: The Maurice Wertheim Collection," John O'Brian, Harry N. Abrams, New York, 1988.	1951.76
https://www.harvardartmuseums.org/collections/object/229045	Private Collector, Paris, Said to have been bought directly from artist, 1918.;Maurice Wertheim, Purchased from the Valentine Gallery, 1937, Bequest to Fogg Art Museum, 1951.	1951.52
https://www.harvardartmuseums.org/collections/object/229044	Paul Guillaume, Paris, France, 1925, 1935. By 1930, per caption of photo, Paul Guillaume dining room dated c. 1930, Georgel 2006;Maurice Wertheim, 1937, Bequest to Fogg Art Museum, 1951.	1951.51
https://www.harvardartmuseums.org/collections/object/229043	Unidentified owner, Paris, sold, [through Hôtel Drouot, Paris, June 16, 1906, no 30.]. Dr. Alfred Wolff, Munich, (1912). Sir Michael Sadler, England, (1912). [De Hauke & Co., New York], sold, to A. Conger Goodyear, New York, (1929-1937) sold, [through Wildenstein && Co., New York];to Maurice Wertheim, New York (1937-1951) bequest, to Fogg Art Museum, 1951.	1951.49

2) DATASET: 1000 Red Flag Names

(example)

Bignou, Etienne	Bignou	Etienne	Etienne Bignou
Billiet, Director	Billiet	Director	Director Billiet
Binder, Dr. Moritz Julius	Binder	Dr. Moritz Julius	Dr. Moritz Julius Binder
Bing Collection	Bing Collection		Bing Collection
Birtschansky, Zacharie	Birtschansky	Zacharie	Zacharie Birtschansky
Bisson, E.	Bisson	E.	E. Bisson
Blanc, Pierre	Blanc	Pierre	Pierre Blanc
Bleye, Willi	Bleye	Willi	Willi Bleye
Bloch, Dr. Vitale	Bloch	Dr. Vitale	Dr. Vitale Bloch
Bloch-Bauer Collection	Bloch-Bauer Collection		Bloch-Bauer Collection
Blot	Blot		Blot
Bode, Dr.	Bode	Dr.	Dr. Bode
Bodenschatz, General Karl	Bodenschatz	General Karl	General Karl Bodenschatz
Boedecker, Alfred	Boedecker	Alfred	Alfred Boedecker
Boehler, Julius, Jr.	Boehler	Julius	Julius Boehler
Boehler, Julius, Sr.	Boehler	Julius	Julius Boehler
Boehm, Dr. Franz	Boehm	Dr. Franz	Dr. Franz Boehm
Boehmer, Bernhard	Boehmer	Bernhard	Bernhard Boehmer

3) DATESET:  Red Flag Words or Phrases

(example)

flaguncertainty	flaganonymity	flagpuncutation	flagmove	flagreliability
likely	private collector	?	transfer	telephone
probably	anonymous	[	removed	to at least
possibly	art market			until at least
maybe	unidentified			by 19
?	unknown			before 19
	property of a European collector			according to
	private collection
	property of a lady
	anon.

You're the doctor and the texts are your patients! Who's in good health and who's sick? How sick? With what disease? What kind of tests and measurements can we perform on the texts to help us to reach a diagnosis? What kind of markers for should we look for? What kind of patterns? 

What digital methods can we use - and put into practice during the Hackathon - to "diagnose" these texts and prioritize them for "treatment" (ie, additional provenance research)?

• IDENTIFY Red Flag Names and Words in each Text?
• COUNT Red Flag Names and Words in each Text?
• CHARACTERIZE each Text (number of words? sentiment? completeness v gaps? other features to be identified that may be useful)?
• ANALYZE for patterns, links and networks?
• CALCULATE probability that the provenance conceals a Nazi-era history that will prove problematic if investigated in detail
• RANK according to urgency for further in-depth provenance research 

Download  Provenance Texts Dataset: CSV 

Download ALIU INDEX of Red Flag Names: CSV

(Voyant-Tools Whitelist containing names: 

keywords-14ca7131716f24c62c6529fcc143bbd2  )

Download Words Flags to be counted in provenances: CSV 

What might a successful result look like?

• A list of 50 provenances from the DATASET ranked most likely to conceal looted art
• A color-coded evaluation of each provenance (RED, ORANGE, GREEN) by likelihood of concealing looted art
• Instructions how to analyze the provenances with the tools, functions or code to use  (for example, how to use Voyant-Tools to count all the Red Flag Names and inject the result back into the spreadsheet)
• Ideas for going further....

Triage: "assignment of degrees of urgency to wounds or illnesses to decide the order of treatment of a large number of patients or casualties."

Link to Glamhack2020 project for participants
https://hack.glam.opendata.ch/project/7

Help us to test and improve and test and improve the code!

Link to Code on Github 

https://github.com/parisdata/GLAMhack2020

Issues to resolve:
- While the word list counts and general name extraction seem to work pretty well, reconciliation with the list of 1000 Red Flag Names still needs work. To test: A tighter tolerance combined with a more complete listing of alias might help.
- The extraction of transaction years after 1900 gives interesting though incomplete indicators. The decision to exclude dates in parentheses (as they are sometimes biographical dates and not transaction dates) needs to be reviewed and refined. 
(note: The texts are from multiple sources applying multiple formats and deliberately entered exactly as is)

Results RAW (with uncorrected errors for analysis)

Results CSV RAW