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:

Reactive Supervision: A New Method for Collecting Sarcasm Data
Boaz Shmueli, Lun‐Wei Ku, Soumya Ray
(2020)
Open Access | Times Cited: 15

Showing 15 citing articles:

SemEval-2022 Task 6: iSarcasmEval, Intended Sarcasm Detection in English and Arabic
Ibrahim Abu Farha, Silviu Oprea, Steven Lloyd Wilson, et al.
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) (2022)
Open Access | Times Cited: 73

Sarcasm Detection Using Deep Learning With Contextual Features
Md Saifullah Razali, Alfian Abdul Halin, Lei Ye, et al.
IEEE Access (2021) Vol. 9, pp. 68609-68618
Open Access | Times Cited: 61

Periodic Insight: Multilingual Reputation Generation System through Daily Opinion Mining Analysis
Achraf Boumhidi, Abdessamad Benlahbib, Erik Cambria, et al.
Results in Engineering (2025), pp. 104619-104619
Open Access

Adversarial Multi-task Model for Emotion, Sentiment, and Sarcasm Aided Complaint Detection
Apoorva Singh, Arousha Nazir, Sriparna Saha
Lecture notes in computer science (2022), pp. 428-442
Closed Access | Times Cited: 12

Multi-perspective contrastive learning framework guided by sememe knowledge and label information for sarcasm detection
Zhiyuan Wen, Rui Wang, Xuan Luo, et al.
International Journal of Machine Learning and Cybernetics (2023) Vol. 14, Iss. 12, pp. 4119-4134
Closed Access | Times Cited: 6

Perceived and Intended Sarcasm Detection with Graph Attention Networks
Joan Plepi, Lucie Flek
(2021), pp. 4746-4753
Open Access | Times Cited: 13

Sarcasm Detection is Way Too Easy! An Empirical Comparison of Human and Machine Sarcasm Detection
Ibrahim Abu Farha, Steven Lloyd Wilson, Silviu Oprea, et al.
(2022), pp. 5284-5295
Open Access | Times Cited: 4

Deep Sarcasm Detection with Sememe and Syntax Knowledge
Zhiqiang Zhang, Jiajun Shan, Haiyan Wu, et al.
Lecture notes in computer science (2024), pp. 411-431
Closed Access

TUG-CIC at SemEval-2021 Task 6: Two-stage Fine-tuning for Intended Sarcasm Detection
Jason Angel, Segun Aroyehun, Alexander Gelbukh
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022) (2022), pp. 951-955
Open Access | Times Cited: 2

Twitter Data-based Sarcastic Sentiment Analysis using Deep Learning Framework
S. G. Shaila, D Vinod, Joycelita Dias, et al.
(2022), pp. 326-330
Closed Access | Times Cited: 2

Perceived and Intended Sarcasm Detection with Graph Attention Networks
Joan Plepi, Lucie Flek
(2021), pp. 97-105
Open Access | Times Cited: 2

Review on Sentiment Analysis and Polarity Classification of Sarcastic Sentences using Deep Learning in Social Media
Amit Kumar Bhadra, S. G. Shaila, M. K. Banga
Lecture notes in networks and systems (2022), pp. 225-237
Closed Access | Times Cited: 1

Sarcasm Relation to Time: Sarcasm Detection with Temporal Features and Deep Learning
Md Saifullah Razali, Alfian Abdul Halin, Yang-Wai Chow, et al.
Lecture notes in computer science (2023), pp. 287-297
Closed Access

Perceived and Intended Sarcasm Detection with Graph Attention Networks
Joan Plepi, Lucie Flek
Empirical Methods in Natural Language Processing (2021), pp. 4746-4753
Closed Access

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