{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T15:11:22Z","timestamp":1778166682496,"version":"3.51.4"},"reference-count":39,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T00:00:00Z","timestamp":1778112000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T00:00:00Z","timestamp":1778112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Cogn Comput"],"published-print":{"date-parts":[[2026,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Sarcasm detection is challenging in natural language processing due to contextual, emotional, and cultural nuances. This paper proposes a dual-encoder BERT (D-BERT) architecture addressing contextual diversity and sentiment reversal challenges in sarcasm detection. We developed D-BERT with two specialized stages: the first BERT extracts deep contextual embeddings while the second performs classification. The model was trained on 149,480 samples from Twitter, Reddit, SemEval, and The Onion, representing diverse communication styles. D-BERT achieved validation accuracies of 91% (95% CI(Confidence Interval): [0.902, 0.918]) on multi-domain data, 86.4% on Twitter, and 83.6% on Reddit, with F1-scores of 0.91, 0.86, and 0.84 respectively. Statistical validation confirmed 13% improvement over single-encoder BERT (McNemar\u2019s\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\varvec{\\chi }^{\\varvec{2}} = \\varvec{178.4}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ,\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\varvec{p}\\varvec{&lt;} \\varvec{0.0001}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    , Cohen\u2019s\n                    <jats:inline-formula>\n                      <jats:tex-math>$$\\varvec{h = 0.82}$$<\/jats:tex-math>\n                    <\/jats:inline-formula>\n                    ), representing large practical effect. Performance variation across platforms highlights domain-specific challenges. Adversarial evaluation relies on 15 qualitative test cases rather than systematic benchmarks. No LLM comparison was conducted. Evaluation demonstrates within-distribution generalization, not cross-domain transfer. Doubled computational cost (24.7ms vs 12.3ms) limits scalability. D-BERT effectively captures semantic and contextual features through dual-stage processing, advancing sarcasm detection while acknowledging scope boundaries for future research.\n                  <\/jats:p>","DOI":"10.1007\/s12559-026-10564-z","type":"journal-article","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T14:12:45Z","timestamp":1778163165000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Dual Encoder Architecture for Robust, Adversarial Aware Sarcasm Detection across Heterogeneous Text Corpora"],"prefix":"10.1007","volume":"18","author":[{"given":"Ramakrishna","family":"Bodige","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rameshbabu","family":"Akarapu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pramod Kumar","family":"P","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,7]]},"reference":[{"key":"10564_CR1","doi-asserted-by":"crossref","unstructured":"Abaskohi A, Rasouli A, Zeraati T, et al. 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