This document describes a new method called ScholarOne Reviewer Locator that aims to reduce reviewer fatigue for journal editors by automatically recommending new potential reviewers. It does this by generating "fingerprints" from manuscript metadata and submitted papers and matching them against a database of 5 years of published research papers. Preliminary results show the tool has recommended over 428,000 potential reviewers for 1674 journals, with 43.5% of selected reviewers coming from those recommendations. The response rate of recommended reviewers agreeing to review is comparable to reviewers from the journals' existing databases. This suggests the automated tool can effectively identify new suitable reviewers and help avoid overburdening the current reviewer pool.

1. A METHOD TO REDUCE REVIEWER FATIGUE AND FIND NEW REVIEWERS
Ian Potter1, Chris Heid2, Chris Carwile2, Josh Dahl2
1Thomson Reuters, London, UK, 2Thomson Reuters, Charlottesville, VA, USA
HOW SCHOLARONE REVIEWER LOCATOR WORKS
WEB OF SCIENCE™
JOURNAL FINGERPRINT
refines results based on
publications & their scope
JOURNAL FINGERPRINT
AUTHOR FINGERPRINT AUTHOR FINGERPRINT
finds related authors
PAPER FINGERPRINT
matches similar papers
PAPER FINGERPRINT
SCHOLARONE™
Journal of Reviewer Selection Studies
A New Method to Reduce
Reviewer Fatigue and Find
Reviewers
Ian Potter, Chris Carwile, Chris Heid,
and Josh Dahl
Thomson Reviewers
Keywords: reviewer fatigue,
discovery tools, automation
Abstract: Overuse of an existing
reviewer pool is a significant issue in
peer review. Avoiding reviewer
fatigue and finding new, suitable
reviewers is a big time-sink for busy
editors and staff, often involving
manual database searches and then
adding those individuals to the peer
review system.
AUTHOR
FINGERPRINT
their research
interests
JOURNAL
FINGERPRINT
publication field
and scope
PAPER
FINGERPRINT
subject areas and
keywords
generates document fingerprints from
manuscript metadata
fingerprint matching with 5 years
of Web of Science data
SCHOLARONE REVIEWER LOCATOR KEY FEATURES
SCHOLARONE™ recommendations are automatically listed for editors to select from — results
can be refreshed at any time
RESULTS
up to 30 matches
in relevancy order
RELEVANT
WORKS
key matching
papers
METRICS
for any matching
accounts (based
on email or ORCID
iD), headline
reviewer metrics
are shown
ADD
individuals can
added directly to
the list of selected
reviewers with an
opportunity to
supplement
account details
ARTICLE
DETAILS
available via DOI
or through the
Web of Science
INTRODUCTION
As the number of manuscript submissions grow, journals face on ongoing challenge
to find new reviewers with suitable expertise, or risk overburdening their current
reviewers. Searches of external databases may find suitable candidates, but searches
can be complex and their relevancy hard to judge, and copying data between
systems creates an administrative burden for staff and editors.
In this poster, we look at the utility of using the ScholarOne Reviewer Locator.
Reviewer Locator combines a text analysis tool and a novel fingerprinting process to
return reviewer recommendations from an established dataset of published research.
Our text analysis
tool ATLAS
analyses, extracts
and creates
‘fingerprints’ from
the metadata of
documents
submitted using
ScholarOne
Manuscripts. These
fingerprints are
compared with the
previous five years of
published content in
the Web of Science™
Core Collection.
This dataset
comprises ~7.5
million individuals
from 12,500
journals, 150,000
conference
proceedings, and
62,000 book
chapters.
RESULTS
from ScholarOne Reviewer Locator usage through June 30 2015.
CONCLUSION
The ScholarOne Reviewer Locator debuted at the end of 2013 and is currently in
use across 1674 journals with 43.5% of selected reviewers coming from the list of
recommended reviewers. Of those 428,861 potential reviewers, 153,049 agreed.
In total, 15.5% of reviewers who agreed came from the recommendations.
Responsiveness is comparable with reviewers in the journal’s existing database,
with 35.7% of recommendations
agreeing to complete a review
(compared with 37.4% for those in
the journal’s own database). These
results show that an automated
reviewer suggestion tool can
contribute significant numbers of
new reviewers to a journal and help
to avoid reviewer fatigue.
USAGE
166,010 MANUSCRIPTS
with Reviewer Locator recommendations
986,555 REVIEWERS SELECTED
428,861 REVIEWER LOCATOR
recommendations used
44%
1674 JOURNALS
using Reviewer Locator
COMPARISON
0%
15%
30%
45%
60%
% SELECTED % INVITED % RESPONDED % AGREED % COMPLETED
13.3%
15.5%
32.7%
38.6%
43.5%
15.4%
21.2%
43.5%
47.3%
56.5%
NUMBER OF REVIEWER INVITES
NON REVIEWER
LOCATOR
REVIEWER
LOCATOR
SELECTED 557,694 428,861
SENT 466,961 380,479
RECEIVED 428,702 322,642
ACCEPTED 208,797 153,049
OVERALL
SELECTION
RATE
for Reviewer Locator
recommendations
PERCENTAGE OF SELECTED REVIEWERS IN
EACH CATEGORY (NON-RL VS RL)
RESPONSE NON REVIEWER
LOCATOR
REVIEWER
LOCATOR
% INVITED 83.7% 88.7%
% RESPONSE 76.9% 75.2%
% AGREE 37.4% 35.7%
percentage of total selected reviewers