{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T07:25:18Z","timestamp":1772609118541,"version":"3.50.1"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"10","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T00:00:00Z","timestamp":1765238400000},"content-version":"vor","delay-in-days":8,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100006595","name":"Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii","doi-asserted-by":"publisher","award":["COFUND-CETP-SMART-LEM-1"],"award-info":[{"award-number":["COFUND-CETP-SMART-LEM-1"]}],"id":[{"id":"10.13039\/501100006595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J. King Saud Univ. Comput. Inf. Sci."],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1007\/s44443-025-00379-7","type":"journal-article","created":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T13:55:15Z","timestamp":1765288515000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["LLM-as-a-judge for sarcasm detection using supervised fine-tuning of transformers"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9005-5181","authenticated-orcid":false,"given":"Simona-Vasilica","family":"Oprea","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adela","family":"B\u00e2ra","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,12,9]]},"reference":[{"key":"379_CR1","doi-asserted-by":"publisher","unstructured":"Al-Jamal WQ, Mustafa AM, Ali MZ (2022) Sarcasm detection in Arabic short text using deep learning. 2022 13th International Conference on Information and Communication Systems, ICICS 2022. https:\/\/doi.org\/10.1109\/ICICS55353.2022.9811153","DOI":"10.1109\/ICICS55353.2022.9811153"},{"key":"379_CR2","doi-asserted-by":"publisher","DOI":"10.3390\/app13095586","author":"R Ali","year":"2023","unstructured":"Ali R, Farhat T, Abdullah S, Akram S, Alhajlah M, Mahmood A, Iqbal MA (2023) Deep learning for sarcasm identification in news headlines. Appl Sci. https:\/\/doi.org\/10.3390\/app13095586","journal-title":"Appl Sci"},{"key":"379_CR3","doi-asserted-by":"publisher","unstructured":"Alqahtani A, Alsheddi A, Alhenaki L (2023) Text-based sarcasm detection on social networks: a systematic review. Int J Adv Computer Sci Appl. https:\/\/doi.org\/10.14569\/IJACSA.2023.0140336","DOI":"10.14569\/IJACSA.2023.0140336"},{"key":"379_CR4","doi-asserted-by":"publisher","unstructured":"Ashok DM, Nidhi Ghanshyam A, Salim SS, Burhanuddin Mazahir D, Thakare BS (2020) Sarcasm detection using genetic optimization on LSTM with CNN. 2020 International Conference for Emerging Technology, INCET 2020. https:\/\/doi.org\/10.1109\/INCET49848.2020.9154090","DOI":"10.1109\/INCET49848.2020.9154090"},{"key":"379_CR5","doi-asserted-by":"publisher","DOI":"10.17762\/ijritcc.v10i2s.5942","author":"RA Bagate","year":"2022","unstructured":"Bagate RA, Suguna R (2022) Sarcasm detection on text for political domain\u2014 an explainable approach. Int J Recent Innov Trends Comput Commun. https:\/\/doi.org\/10.17762\/ijritcc.v10i2s.5942","journal-title":"Int J Recent Innov Trends Comput Commun"},{"key":"379_CR6","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-28244-7_7","author":"D Bandyopadhyay","year":"2023","unstructured":"Bandyopadhyay D, Kumari G, Ekbal A, Pal S, Chatterjee A, Bn V (2023) A knowledge infusion based multitasking system for sarcasm detection in meme. Lecture Notes Comput Sci (Including Subseries Lecture Notes Artificial Intell Lecture Notes in Bioinform). https:\/\/doi.org\/10.1007\/978-3-031-28244-7_7","journal-title":"Lecture Notes Comput Sci (Including Subseries Lecture Notes Artificial Intell Lecture Notes in Bioinform)"},{"key":"379_CR7","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2022.3169864","author":"W Chen","year":"2022","unstructured":"Chen W, Lin F, Zhang X, Li G, Liu B (2022) Jointly learning sentimental clues and context incongruity for sarcasm detection. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2022.3169864","journal-title":"IEEE Access"},{"key":"379_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2021.102600","author":"ZL Chia","year":"2021","unstructured":"Chia ZL, Ptaszynski M, Masui F, Leliwa G, Wroczynski M (2021) Machine learning and feature engineering-based study into sarcasm and irony classification with application to cyberbullying detection. Inf Process Manag. https:\/\/doi.org\/10.1016\/j.ipm.2021.102600","journal-title":"Inf Process Manag"},{"key":"379_CR9","doi-asserted-by":"publisher","DOI":"10.1007\/s12559-021-09832-x","author":"Y Du","year":"2022","unstructured":"Du Y, Li T, Pathan MS, Teklehaimanot HK, Yang Z (2022) An effective sarcasm detection approach based on sentimental context and individual expression habits. Cogn Comput. https:\/\/doi.org\/10.1007\/s12559-021-09832-x","journal-title":"Cogn Comput"},{"key":"379_CR10","doi-asserted-by":"publisher","unstructured":"Farha IA, Oprea SV, Wilson SR, Magdy W (2022) SemEval-2022 Task 6: iSarcasmEval, intended sarcasm detection in english and Arabic. SemEval 2022 - 16th Int Workshop Semantic Eval Proc Workshop. https:\/\/doi.org\/10.18653\/v1\/2022.semeval-1.111","DOI":"10.18653\/v1\/2022.semeval-1.111"},{"key":"379_CR11","unstructured":"Filatova E (2012) Irony and sarcasm: Corpus generation and analysis using crowdsourcing. Proc 8th Int Conf Language Resour Eval, LREC 2012."},{"key":"379_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2022.01.008","author":"V Govindan","year":"2022","unstructured":"Govindan V, Balakrishnan V (2022) A machine learning approach in analysing the effect of hyperboles using negative sentiment tweets for sarcasm detection. J King Saud Univ-Comput Inf Sci. https:\/\/doi.org\/10.1016\/j.jksuci.2022.01.008","journal-title":"J King Saud Univ-Comput Inf Sci"},{"key":"379_CR13","doi-asserted-by":"publisher","unstructured":"Gregory H, Li S, Mohammadi P, Tarn N, Draelos R, Rudin C (2020) A transformer approach to contextual sarcasm detection in twitter. Proc Annual Meeting Assoc Comput Linguistics. https:\/\/doi.org\/10.18653\/v1\/P17","DOI":"10.18653\/v1\/P17"},{"key":"379_CR14","doi-asserted-by":"publisher","DOI":"10.3390\/e25060878","author":"S Hao","year":"2023","unstructured":"Hao S, Yao J, Shi C, Zhou Y, Xu S, Li D, Cheng Y (2023) Enhanced semantic representation learning for sarcasm detection by integrating context-aware attention and fusion network. Entropy. https:\/\/doi.org\/10.3390\/e25060878","journal-title":"Entropy"},{"key":"379_CR15","doi-asserted-by":"publisher","DOI":"10.1145\/3124420","author":"A Joshi","year":"2018","unstructured":"Joshi A, Bhattacharyya P, Carman MJ (2018) Automatic sarcasm detection: a survey. ACM Comput Surv. https:\/\/doi.org\/10.1145\/3124420","journal-title":"ACM Comput Surv"},{"key":"379_CR16","doi-asserted-by":"publisher","unstructured":"Khattri A, Joshi A, Bhattacharyya P, Carman MJ (2015) Your sentiment precedes you: Using an author\u2019s historical tweets to predict sarcasm. 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2015 at the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015 - Proceedings. https:\/\/doi.org\/10.18653\/v1\/w15-2905","DOI":"10.18653\/v1\/w15-2905"},{"key":"379_CR17","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2963630","author":"A Kumar","year":"2020","unstructured":"Kumar A, Narapareddy VT, Srikanth VA, Malapati A, Neti LBM (2020) Sarcasm detection using multi-head attention based bidirectional LSTM. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2019.2963630","journal-title":"IEEE Access"},{"key":"379_CR18","unstructured":"Liang B, Lin Z, Qin B, Xu R (2022) Topic-oriented sarcasm detection: new task, new dataset and new method. 21st Chinese National Conference on Computational Linguistic, CCL 2022."},{"key":"379_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2023.01.001","author":"R Misra","year":"2023","unstructured":"Misra R, Arora P (2023) Sarcasm detection using news headlines dataset. AI Open. https:\/\/doi.org\/10.1016\/j.aiopen.2023.01.001","journal-title":"AI Open"},{"key":"379_CR20","doi-asserted-by":"publisher","unstructured":"Nezhad ZB, Deihimi MA (2021) Sarcasm detection in Persian. J Inform Commun Technol. https:\/\/doi.org\/10.32890\/jict.20.1.2021.6249","DOI":"10.32890\/jict.20.1.2021.6249"},{"key":"379_CR21","doi-asserted-by":"publisher","unstructured":"Prasad CR, Reddy NA, Varma GR, Shuaib M (2023) Sarcasm detection with glove and word2vec models. ARPN J Eng Appl Sci. https:\/\/doi.org\/10.59018\/0523154","DOI":"10.59018\/0523154"},{"key":"379_CR22","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3247427","author":"A Rahma","year":"2023","unstructured":"Rahma A, Azab SS, Mohammed A (2023) A comprehensive survey on Arabic sarcasm detection: approaches, challenges and future trends. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2023.3247427","journal-title":"IEEE Access"},{"key":"379_CR23","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3076789","author":"MS Razali","year":"2021","unstructured":"Razali MS, Halin AA, Ye L, Doraisamy S, Norowi NM (2021) Sarcasm detection using deep learning with contextual features. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2021.3076789","journal-title":"IEEE Access"},{"key":"379_CR24","doi-asserted-by":"publisher","DOI":"10.1177\/1470785320921779","author":"SM Sarsam","year":"2020","unstructured":"Sarsam SM, Al-Samarraie H, Alzahrani AI, Wright B (2020) Sarcasm detection using machine learning algorithms in Twitter: a systematic review. Int J Market Res. https:\/\/doi.org\/10.1177\/1470785320921779","journal-title":"Int J Market Res"},{"key":"379_CR25","doi-asserted-by":"publisher","DOI":"10.3390\/math10050844","author":"E Savini","year":"2022","unstructured":"Savini E, Caragea C (2022) Intermediate-task transfer learning with BERT for sarcasm detection. Mathematics. https:\/\/doi.org\/10.3390\/math10050844","journal-title":"Mathematics"},{"issue":"2","key":"379_CR26","doi-asserted-by":"publisher","first-page":"91","DOI":"10.24818\/18423264\/59.2.25.06","volume":"59","author":"BK Shrivash","year":"2025","unstructured":"Shrivash BK, Verma DK, Pandey P (2025) A novel framework for text preprocessing using NLP approaches and classification using random forest grid search technique for sentiment analysis. Econ Comput Econ Cyber Stud Res 59(2):91\u2013107. https:\/\/doi.org\/10.24818\/18423264\/59.2.25.06","journal-title":"Econ Comput Econ Cyber Stud Res"},{"key":"379_CR27","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2899260","author":"LH Son","year":"2019","unstructured":"Son LH, Kumar A, Sangwan SR, Arora A, Nayyar A, Abdel-Basset M (2019) Sarcasm detection using soft attention-based bidirectional long short-term memory model with convolution network. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2019.2899260","journal-title":"IEEE Access"},{"key":"379_CR28","doi-asserted-by":"publisher","unstructured":"Tasnim N, Sultana N (2023) Pairwise learning approach using siamese neural network for contextual sarcasm detection. 2023 26th International Conference on Computer and Information Technology, ICCIT 2023. https:\/\/doi.org\/10.1109\/ICCIT60459.2023.10441610","DOI":"10.1109\/ICCIT60459.2023.10441610"},{"key":"379_CR29","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-91560-5_5","author":"H Wang","year":"2021","unstructured":"Wang H, Song X, Zhou B, Wang Y, Gao L, Jia Y (2021) Performance evaluation of pre-trained models in sarcasm detection task. Lecture Notes Comput Sci (Including Subseries Lecture Notes Artif Intell Lecture Notes Bioinform). https:\/\/doi.org\/10.1007\/978-3-030-91560-5_5","journal-title":"Lecture Notes Comput Sci (Including Subseries Lecture Notes Artif Intell Lecture Notes Bioinform)"},{"key":"379_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.102132","author":"J Wang","year":"2024","unstructured":"Wang J, Yang Y, Jiang Y, Ma M, Xie Z, Li T (2024) Cross-modal incongruity aligning and collaborating for multi-modal sarcasm detection. Inf Fusion. https:\/\/doi.org\/10.1016\/j.inffus.2023.102132","journal-title":"Inf Fusion"},{"key":"379_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.102883","author":"Z Wen","year":"2022","unstructured":"Wen Z, Gui L, Wang Q, Guo M, Yu X, Du J, Xu R (2022) Sememe knowledge and auxiliary information enhanced approach for sarcasm detection. Inf Process Manage. https:\/\/doi.org\/10.1016\/j.ipm.2022.102883","journal-title":"Inf Process Manage"},{"key":"379_CR32","doi-asserted-by":"publisher","DOI":"10.32985\/ijeces.15.1.7","author":"AD Yacoub","year":"2024","unstructured":"Yacoub AD, Slim SO, Aboutabl AE (2024) A survey of sentiment analysis and sarcasm detection: challenges, techniques, and trends. Int J Electr Comput Eng Syst. https:\/\/doi.org\/10.32985\/ijeces.15.1.7","journal-title":"Int J Electr Comput Eng Syst"},{"key":"379_CR33","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3093416","author":"F Yao","year":"2023","unstructured":"Yao F, Sun X, Yu H, Zhang W, Liang W, Fu K (2023) Mimicking the brain\u2019s cognition of sarcasm from multidisciplines for Twitter sarcasm detection. IEEE Trans Neural Netw Learn Syst. https:\/\/doi.org\/10.1109\/TNNLS.2021.3093416","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"379_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-98355-0_23","author":"L Zhang","year":"2022","unstructured":"Zhang L, Zhao X, Song X, Fang Y, Li D, Wang H (2022) A novel Chinese sarcasm detection model based on retrospective reader. Lecture Notes Comput Sci (Including Subseries Lecture Notes Artif Intell Lecture Notes Bioinform). https:\/\/doi.org\/10.1007\/978-3-030-98355-0_23","journal-title":"Lecture Notes Comput Sci (Including Subseries Lecture Notes Artif Intell Lecture Notes Bioinform)"},{"key":"379_CR35","doi-asserted-by":"publisher","DOI":"10.1145\/3533430","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Ma D, Tiwari P, Zhang C, Masud M, Shorfuzzaman M, Song D (2023) Stance-level sarcasm detection with BERT and stance-centered graph attention networks. ACM Trans Internet Technol. https:\/\/doi.org\/10.1145\/3533430","journal-title":"ACM Trans Internet Technol"}],"container-title":["Journal of King Saud University Computer and Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00379-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s44443-025-00379-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s44443-025-00379-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T18:47:53Z","timestamp":1767638873000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s44443-025-00379-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":35,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["379"],"URL":"https:\/\/doi.org\/10.1007\/s44443-025-00379-7","relation":{},"ISSN":["1319-1578","2213-1248"],"issn-type":[{"value":"1319-1578","type":"print"},{"value":"2213-1248","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"27 August 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"The authors report there are no competing interests to declare.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"357"}}