--- title: "Who cites Stephanie M. Lukin" description: "104 papers of the Elamur Research library cite the works of Stephanie M. Lukin, found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/authors/stephanie-m-lukin/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/stephanie-m-lukin/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Stephanie M. Lukin](https://md.research.elamur.ai/free/en/authors/stephanie-m-lukin/) / cited by # Who cites Stephanie M. Lukin 118 citations of their works from 104 papers of our library (of the 140,884 whose bibliographies we read) · 21 with a page here, the most cited first · [← Stephanie M. Lukin](https://md.research.elamur.ai/free/en/authors/stephanie-m-lukin/) 1. [**Sarcasm as Contrast between a Positive Sentiment and Negative Situation**Ellen Riloff, Ashequl Qadir, Prafulla Surve, et al.*2013 · 752 citations*](https://md.research.elamur.ai/free/en/papers/riloff-2013-sarcasm-as-contrast-between-positive/) 2. [**Fracking Sarcasm using Neural Network**Aniruddha Ghosh, Tony Veale*2016 · 350 citations*](https://md.research.elamur.ai/free/en/papers/ghosh-2016-fracking-sarcasm-neural-network/) 3. [**A Large Self-Annotated Corpus for Sarcasm**Mikhail Khodak, Nikunj Saunshi, Kiran Vodrahalli*2018 · 258 citations*](https://md.research.elamur.ai/free/en/papers/khodak-2018-large-self-annotated-corpus-sarcasm/) 4. [**Modelling Sarcasm in Twitter, a Novel Approach**Francesco Barbieri, Horacio Saggion, Francesco Ronzano*2014 · 217 citations*](https://md.research.elamur.ai/free/en/papers/barbieri-2014-modelling-sarcasm-twitter-novel-approach/) 5. [**Automatic Sarcasm Detection: A Survey**Aditya Joshi, Pushpak Bhattacharyya, Mark James Carman*2016 · 214 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2016-automatic-sarcasm-detection-survey/) 6. [**Tweet Sarcasm Detection Using Deep Neural Network**Meishan Zhang, Yue Zhang, Guohong Fu*2016 · 189 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2016-tweet-sarcasm-detection-deep-neural/) 7. [**Magnets for Sarcasm: Making Sarcasm Detection Timely, Contextual and Very Personal**Aniruddha Ghosh, Tony Veale*2017 · 174 citations*](https://md.research.elamur.ai/free/en/papers/ghosh-2017-magnets-sarcasm-making-sarcasm-detection/) 8. [**Creating and Characterizing a Diverse Corpus of Sarcasm in Dialogue**Shereen Oraby, Vrindavan Harrison, Lena Reed, et al.*2016 · 163 citations*](https://md.research.elamur.ai/free/en/papers/oraby-2016-creating-characterizing-diverse-corpus-sarcasm/) 9. [**Investigating Pretrained Language Models for Graph-to-Text Generation**Leonardo F. R. Ribeiro, Martin Schmitt, Hinrich Schütze, et al.*2021 · 149 citations*](https://md.research.elamur.ai/free/en/papers/ribeiro-2021-investigating-pretrained-language-models-graph/) 10. [**Putting Sarcasm Detection into Context: The Effects of Class Imbalance and Manual Labelling on Supervised Machine Classification of Twitter Conversations**Gavin Abercrombie, Dirk Hovy*2016 · 85 citations*](https://md.research.elamur.ai/free/en/papers/abercrombie-2016-putting-sarcasm-detection-into-context/) 11. [**Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog**Shereen Oraby, Vrindavan Harrison, Amita Misra, et al.*2017 · 70 citations*](https://md.research.elamur.ai/free/en/papers/oraby-2017-are-you-serious-rhetorical-questions/) 12. [**Weisfeiler-Leman in the Bamboo: Novel AMR Graph Metrics and a Benchmark for AMR Graph Similarity**Juri Opitz, Angel Daza, Anette Frank*2021 · 47 citations*](https://md.research.elamur.ai/free/en/papers/opitz-2021-weisfeiler-leman-bamboo-novel-amr/) 13. [**Modelling Irony in Twitter: Feature Analysis and Evaluation**Francesco Barbieri, Horacio Saggion*2014 · 42 citations*](https://md.research.elamur.ai/free/en/papers/barbieri-2014-modelling-irony-twitter-feature-analysis/) 14. [**Reactive Supervision: A New Method for Collecting Sarcasm Data**Boaz Shmueli, Lun-Wei Ku, Soumya Ray*2020 · 36 citations*](https://md.research.elamur.ai/free/en/papers/shmueli-2020-reactive-supervision-new-method-collecting/) 15. [**A Dual-Channel Framework for Sarcasm Recognition by Detecting Sentiment Conflict**Yiyi Liu, Yequan Wang, Aixin Sun, et al.*2022 · 34 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-dual-channel-framework-sarcasm-recognition/) 16. [**Is Sarcasm Detection A Step-by-Step Reasoning Process in Large Language Models?**Ben Yao, Yazhou Zhang, Qiuchi Li, et al.*2024 · 33 citations*](https://md.research.elamur.ai/free/en/papers/yao-2024-sarcasm-detection-step-step-reasoning/) 17. [**Chinese Irony Corpus Construction and Ironic Structure Analysis**Yi-jie Tang, Hsin-Hsi Chen*2014 · 33 citations*](https://md.research.elamur.ai/free/en/papers/tang-2014-chinese-irony-corpus-construction-ironic/) 18. [**Getting Reliable Annotations for Sarcasm in Online Dialogues**Reid Swanson, Stephanie Lukin, Luke Eisenberg, et al.*2014 · 23 citations*](https://md.research.elamur.ai/free/en/papers/swanson-2014-getting-reliable-annotations-sarcasm-online/) 19. [**Assessing the Cross-linguistic Utility of Abstract Meaning Representation**Shira Wein, Nathan Schneider*2024 · 17 citations*](https://md.research.elamur.ai/free/en/papers/wein-2024-assessing-cross-linguistic-utility-abstract/) 20. [**How Challenging is Sarcasm versus Irony Classification?: A Study With a Dataset from English Literature**Aditya Joshi, Vaibhav Tripathi, Pushpak Bhattacharyya, et al.*2016 · 10 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2016-how-challenging-is-sarcasm-versus/) 21. [**Survey of Abstract Meaning Representation: Then, Now, Future**Behrooz Mansouri*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/mansouri-2025-survey-abstract-meaning-representation-then/) 83 more citing papers of our library have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/authors/stephanie-m-lukin/) — Stephanie M. Lukin