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Traditional machine learning methods often fail to capture the nuanced and often contradictory nature of sarcastic expression. To address these challenges, this paper presents a comprehensive and computationally efficient framework for Urdu sarcasm detection. We first mitigate severe class imbalance through strategic down\u2010sampling and back\u2010translation\u2010based data augmentation. We then conduct extensive benchmarking of traditional deep learning architectures against fine\u2010tuned pre\u2010trained language models, including multilingual, monolingual, and Twitter\u2010specific variants. Building on these insights, we propose DeepSarc, a novel hybrid model that integrates the contextual embeddings from XLM\u2010T, the multi\u2010scale feature extraction capabilities of dilated convolutional neural networks (DCNNs), and the sequential dependency modeling of bidirectional long short\u2010term memory (BiLSTM) networks. While slightly more computationally intensive than simpler alternatives, DeepSarc achieves a state\u2010of\u2010the\u2010art F1\u2010score, significantly outperforming existing approaches. Our results establish a new benchmark for sarcasm detection in low\u2010resource languages and provide a scalable, high\u2010performance framework adaptable to diverse linguistic contexts.<\/jats:p>","DOI":"10.1002\/cpe.70405","type":"journal-article","created":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T01:23:36Z","timestamp":1763342616000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DeepSarc: A Transformer\u2010Based Deep Learning Approach for Sarcasm Detection in Social Media Text"],"prefix":"10.1002","volume":"37","author":[{"given":"Rafiul","family":"Haq","sequence":"first","affiliation":[{"name":"College of Intelligence and Computing Tianjin University  Tianjin China"}]},{"given":"Xiaowang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing Tianjin University  Tianjin China"}]},{"given":"Sofonias","family":"Yitagesu","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing Tianjin University  Tianjin China"}]},{"given":"Wahab","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science University of Science and Technology  Bannu Pakistan"}]},{"given":"Zhiyong","family":"Feng","sequence":"additional","affiliation":[{"name":"College of Intelligence and Computing Tianjin University  Tianjin China"}]}],"member":"311","published-online":{"date-parts":[[2025,11,16]]},"reference":[{"key":"e_1_2_9_2_1","article-title":"A Novel Mathematical Modeling in Shift in Emotion for Gauging the Social Influential in Big Data Streams With Hybrid Sarcasm Detection","volume":"34","author":"Kumaran P.","year":"2021","journal-title":"Concurrency and Computation: Practice and Experience"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2023.01.054"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2024.102378"},{"issue":"5","key":"e_1_2_9_5_1","article-title":"Automatic Sarcasm Detection: A Survey","volume":"50","author":"Joshi A.","year":"2017","journal-title":"ACM Computing Surveys"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.asej.2024.102736"},{"key":"e_1_2_9_7_1","doi-asserted-by":"crossref","unstructured":"H.JangandD.Frassinelli \u201cGeneralizable Sarcasm Detection Is Just Around the Corner of Course! 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