In this paper we propose a novel method for quality assessment of crowdsourced data.
It computes user reputation scores without requiring ground truth; instead, it is based on the consistency among users.
In this pilot study, we perform some explorative data analysis on two real crowdsourcing projects by the New York Public Library:
extracting building footprints as polygons from historical insurance atlases, and geolocating historical photographs.
We show that the computed reputation scores are plausible and furthermore provide insight into user behavior.
%0 Conference Paper
%1 Barz18:Reputation
%A Barz, Björn
%A van Dijk, Thomas C.
%A Spaan, Bert
%A Denzler, Joachim
%B 2nd ACM SIGSPATIAL Workshop on Geospatial Humanities
%D 2018
%K historical-maps myown vgiscience
%R 10.1145/3282933.3282937
%T Putting User Reputation on the Map: Unsupervised Quality Control for Crowdsourced Historical Data
%U https://doi.org/10.1145/3282933.3282937
%X In this paper we propose a novel method for quality assessment of crowdsourced data.
It computes user reputation scores without requiring ground truth; instead, it is based on the consistency among users.
In this pilot study, we perform some explorative data analysis on two real crowdsourcing projects by the New York Public Library:
extracting building footprints as polygons from historical insurance atlases, and geolocating historical photographs.
We show that the computed reputation scores are plausible and furthermore provide insight into user behavior.
%@ 978-1-4503-6032-6/18/11
@inproceedings{Barz18:Reputation,
abstract = {In this paper we propose a novel method for quality assessment of crowdsourced data.
It computes user reputation scores without requiring ground truth; instead, it is based on the consistency among users.
In this pilot study, we perform some explorative data analysis on two real crowdsourcing projects by the New York Public Library:
extracting building footprints as polygons from historical insurance atlases, and geolocating historical photographs.
We show that the computed reputation scores are plausible and furthermore provide insight into user behavior.},
added-at = {2018-11-19T11:27:29.000+0100},
author = {Barz, Björn and van Dijk, Thomas C. and Spaan, Bert and Denzler, Joachim},
biburl = {https://www.bibsonomy.org/bibtex/2d2d8ec076aaedcfc2f4bd9bc7e8fdc4b/thomasd},
booktitle = {2nd ACM SIGSPATIAL Workshop on Geospatial Humanities},
doi = {10.1145/3282933.3282937},
interhash = {b0bb5a586494433d85f12ba9643b2c3c},
intrahash = {d2d8ec076aaedcfc2f4bd9bc7e8fdc4b},
isbn = {978-1-4503-6032-6/18/11},
keywords = {historical-maps myown vgiscience},
timestamp = {2019-03-11T10:16:26.000+0100},
title = {Putting User Reputation on the Map: Unsupervised Quality Control for Crowdsourced Historical Data},
url = {https://doi.org/10.1145/3282933.3282937},
venue = {Seattle, WA, USA},
year = 2018
}