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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Annotator Rationales for Labeling Tasks in Crowdsourcing
Mücahid Kutlu, Tyler McDonnell, Matthew Lease, et al.
Journal of Artificial Intelligence Research (2020) Vol. 69, pp. 143-189
Open Access | Times Cited: 29

Showing 1-25 of 29 citing articles:

Data-centric Artificial Intelligence: A Survey
Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, et al.
ACM Computing Surveys (2025)
Open Access | Times Cited: 7
Teach Me to Explain: A Review of Datasets for Explainable NLP.
Sarah Wiegreffe, Ana Marasović
arXiv (Cornell University) (2021)
Closed Access | Times Cited: 63
The state of human-centered NLP technology for fact-checking
Anubrata Das, Houjiang Liu, Venelin Kovatchev, et al.
Information Processing & Management (2022) Vol. 60, Iss. 2, pp. 103219-103219
Open Access | Times Cited: 38
ProsocialDialog: A Prosocial Backbone for Conversational Agents
Hyunwoo Kim, Youngjae Yu, Liwei Jiang, et al.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2022)
Open Access | Times Cited: 36
On the effect of relevance scales in crowdsourcing relevance assessments for Information Retrieval evaluation
Kevin Roitero, Eddy Maddalena, Stefano Mizzaro, et al.
Information Processing & Management (2021) Vol. 58, Iss. 6, pp. 102688-102688
Closed Access | Times Cited: 35
Comprehensive Viewpoint Representations for a Deeper Understanding of User Interactions With Debated Topics
Tim Draws, Oana Inel, Nava Tintarev, et al.
(2022), pp. 135-145
Open Access | Times Cited: 26
The Annotated Mozart Sonatas: Score, Harmony, and Cadence
Johannes Hentschel, Markus Neuwirth, Martin Rohrmeier
Transactions of the International Society for Music Information Retrieval (2021) Vol. 4, Iss. 1, pp. 67-80
Open Access | Times Cited: 26
Pseudo-labeling with Large Language Models for Multi-label Emotion Classification of French Tweets
Usman Malik, Simon Bernard, Alexandre Pauchet, et al.
IEEE Access (2024) Vol. 12, pp. 15902-15916
Open Access | Times Cited: 3
What People Think AI Should Infer From Faces
Severin Engelmann, Chiara Ullstein, Orestis Papakyriakopoulos, et al.
2022 ACM Conference on Fairness, Accountability, and Transparency (2022)
Open Access | Times Cited: 12
The Challenge of Variable Effort Crowdsourcing and How Visible Gold Can Help
Danula Hettiachchi, Mike Schaekermann, Tristan McKinney, et al.
Proceedings of the ACM on Human-Computer Interaction (2021) Vol. 5, Iss. CSCW2, pp. 1-26
Open Access | Times Cited: 15
Quality aspects of annotated data
Jacob Beck
AStA Wirtschafts- und Sozialstatistisches Archiv (2023) Vol. 17, Iss. 3-4, pp. 331-353
Open Access | Times Cited: 5
On the Diversity and Limits of Human Explanations
Chenhao Tan
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2022)
Open Access | Times Cited: 8
Robust Data-centric Graph Structure Learning for Text Classification
Jun Zhuang
(2024), pp. 1486-1495
Open Access | Times Cited: 1
Human-annotated rationales and explainable text classification: a survey
Elize Herrewijnen, Dong Nguyen, Floris Bex, et al.
Frontiers in Artificial Intelligence (2024) Vol. 7
Open Access | Times Cited: 1
A Neural Model to Jointly Predict and Explain Truthfulness of Statements
Erik Brand, Kevin Roitero, Michael Soprano, et al.
Journal of Data and Information Quality (2022) Vol. 15, Iss. 1, pp. 1-19
Closed Access | Times Cited: 7
ProtoTEx: Explaining Model Decisions with Prototype Tensors
Anubrata Das, Chitrank Gupta, Venelin Kovatchev, et al.
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2022)
Open Access | Times Cited: 7
Fair compensation of crowdsourcing work: the problem of flat rates
Joni Salminen, Ahmed Kamel, Soon‐gyo Jung, et al.
Behaviour and Information Technology (2022) Vol. 42, Iss. 16, pp. 2871-2892
Open Access | Times Cited: 6
Collect, Measure, Repeat: Reliability Factors for Responsible AI Data Collection
Oana Inel, Tim Draws, Lora Aroyo
Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (2023) Vol. 11, Iss. 1, pp. 51-64
Open Access | Times Cited: 3
In Search of Ambiguity: A Three-Stage Workflow Design to Clarify Annotation Guidelines for Crowd Workers
Vivek Krishna Pradhan, Mike Schaekermann, Matthew Lease
Frontiers in Artificial Intelligence (2022) Vol. 5
Open Access | Times Cited: 5
Investigating Self-Rationalizing Models for Commonsense Reasoning
Fanny Rancourt, Paula Vondrlik, Diego Maupomé, et al.
Stats (2023) Vol. 6, Iss. 3, pp. 907-919
Open Access | Times Cited: 2
Combining human intelligence and machine learning for fact-checking: Towards a hybrid human-in-the-loop framework
David La Barbera, Kevin Roitero, Stefano Mizzaro
Intelligenza Artificiale (2023) Vol. 17, Iss. 2, pp. 163-172
Closed Access | Times Cited: 2
Using a cognitive model to understand crowdsourced data from citizen scientists
Alex Thorpe, Oliver Kelly, Alex Callen, et al.
Behavior Research Methods (2023) Vol. 56, Iss. 4, pp. 3589-3605
Closed Access | Times Cited: 2
It's All Relative! A Method to Counter Human Bias in Crowdsourced Stance Detection of News Articles
Ehsan-Ul Haq, Yang Lu, Pan Hui
Proceedings of the ACM on Human-Computer Interaction (2022) Vol. 6, Iss. CSCW2, pp. 1-25
Open Access | Times Cited: 4
Teach Me to Explain: A Review of Datasets for Explainable Natural Language Processing
Sarah Wiegreffe, Ana Marasović
Neural Information Processing Systems (2021)
Closed Access | Times Cited: 4
E-BART: Jointly Predicting and Explaining Truthfulness.
Erik Brand, Kevin Roitero, Michael Soprano, et al.
(2021), pp. 18-27
Closed Access | Times Cited: 3
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