{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T09:16:20Z","timestamp":1772010980429,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T00:00:00Z","timestamp":1771459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T00:00:00Z","timestamp":1771977600000},"content-version":"vor","delay-in-days":6,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soc. Netw. Anal. Min."],"DOI":"10.1007\/s13278-026-01579-3","type":"journal-article","created":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T10:24:30Z","timestamp":1771496670000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Efficient sarcasm detection in social media using hybrid CapsNet-LSTM fusion and feature optimization"],"prefix":"10.1007","volume":"16","author":[{"given":"Sundas Shireen","family":"Awan","sequence":"first","affiliation":[]},{"given":"Suleman","family":"Amjad","sequence":"additional","affiliation":[]},{"given":"Shujaat","family":"Ali","sequence":"additional","affiliation":[]},{"given":"Dilawar","family":"Shah","sequence":"additional","affiliation":[]},{"given":"Muhammad","family":"Tahir","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,2,19]]},"reference":[{"issue":"1","key":"1579_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3375547","volume":"14","author":"M Abulaish","year":"2020","unstructured":"Abulaish M, Kamal A, Zaki MJ (2020) A survey of figurative language and its computational detection in online social networks. ACM Trans Web (TWEB) 14(1):1\u201352","journal-title":"ACM Trans Web (TWEB)"},{"key":"1579_CR2","first-page":"135","volume":"55","author":"F Barbieri","year":"2015","unstructured":"Barbieri F, Ronzano F, Saggion H (2015) Is this tweet satirical? A computational approach for satire detection in Spanish. Procesamiento del Lenguaje Nat 55:135\u2013142","journal-title":"Procesamiento del Lenguaje Nat"},{"issue":"3","key":"1579_CR3","first-page":"1","volume":"3","author":"SK Bharti","year":"2016","unstructured":"Bharti SK, Babu KS, Jena SK (2016) Parsing-based sarcasm detection from Twitter data. Int J Rough Sets Data Anal 3(3):1\u201314","journal-title":"Int J Rough Sets Data Anal"},{"key":"1579_CR4","unstructured":"Chakrabarty T, Muresan S, Peng N (2020) Generating sarcastic responses in dialogues using sentiment controlled neural networks. Proceedings of ACL 2020"},{"key":"1579_CR5","unstructured":"Davidov D, Tsur O, Rappoport A (2010) Semi-supervised recognition of sarcastic sentences in Twitter and Amazon. Proceedings of the Fourteenth Conference on Computational Natural Language Learning"},{"key":"1579_CR6","unstructured":"Devlin J, Chang MW, Lee K, Toutanova K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. InProceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)(pp. 4171\u20134186). Association for Computational Linguistics."},{"issue":"21","key":"1579_CR7","doi-asserted-by":"publisher","first-page":"32967","DOI":"10.1007\/s11042-023-14653-1","volume":"82","author":"RK Dey","year":"2023","unstructured":"Dey RK, Das AK (2023) Modified term frequency-inverse document frequency based deep hybrid framework for sentiment analysis. Multimed Tools Appl 82(21):32967\u201332990","journal-title":"Multimed Tools Appl"},{"key":"1579_CR8","doi-asserted-by":"publisher","first-page":"64393","DOI":"10.1007\/s11042-023-17953-8","volume":"83","author":"RK Dey","year":"2024","unstructured":"Dey RK, Das AK (2024) Neighbour adjusted dispersive flies optimization based deep hybrid sentiment analysis framework. Multimed Tools Appl 83:64393\u201364416","journal-title":"Multimed Tools Appl"},{"key":"1579_CR9","doi-asserted-by":"publisher","DOI":"10.1142\/S0219622025500452","author":"RK Dey","year":"2025","unstructured":"Dey RK, Das AK (2025) Modified Binary Particle Swarm Optimization (MBPSO) Based Deep Hybrid Framework for Sentiment Analysis. Int J Inform Technol Decis Making. https:\/\/doi.org\/10.1142\/S0219622025500452","journal-title":"Int J Inform Technol Decis Making"},{"issue":"7","key":"1579_CR10","first-page":"1163","volume":"29","author":"DIH Farias","year":"2018","unstructured":"Farias DIH, Paredes-Valverde MA, S\u00e1nchez-Rada JF, Iglesias CA (2018) Applying a multilingual and multimodal approach for sarcasm detection. Neural Comput Appl 29(7):1163\u20131173","journal-title":"Neural Comput Appl"},{"key":"1579_CR11","doi-asserted-by":"crossref","unstructured":"Faruqui M, Tsvetkov Y, Yogatama D, Dyer C, Smith NA (2016) Morphological inflection generation using character sequence-to-sequence learning. NAACL","DOI":"10.18653\/v1\/N16-1077"},{"key":"1579_CR12","doi-asserted-by":"crossref","unstructured":"Felbo B, Mislove A, S\u00f8gaard A, Rahwan I, Lehmann S (2017) Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm. Conference on Empirical Methods in Natural Language Processing (EMNLP)","DOI":"10.18653\/v1\/D17-1169"},{"key":"1579_CR13","unstructured":"Filatova E (2012) Irony and sarcasm: Corpus generation and analysis using crowdsourcing. Proc LREC"},{"key":"1579_CR14","first-page":"186","volume":"2017","author":"D Ghosh","year":"2017","unstructured":"Ghosh D, Fabbri AR, Muresan S (2017) The role of conversation context for sarcasm detection in online interactions. SIGdial 2017:186\u2013196","journal-title":"SIGdial"},{"key":"1579_CR15","unstructured":"Ghosh S, Muresan S (2020) What does this emoji mean? A vector semantics approach to sarcasm detection in social media. Proceedings of the Workshop on Figurative Language Processing"},{"key":"1579_CR16","doi-asserted-by":"crossref","unstructured":"Ghosh A, Veale T (2016) Fracking sarcasm using a neural network. 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis","DOI":"10.18653\/v1\/W16-0425"},{"key":"1579_CR17","unstructured":"Gonzalez-Ibanez R, Muresan S, Wacholder N (2011) Identifying sarcasm in Twitter: A closer look. 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies"},{"key":"1579_CR18","doi-asserted-by":"crossref","unstructured":"Gupta R, Kumar J, Agrawal H (2020) A statistical approach for sarcasm detection using Twitter data. 2020 4th International Conference on Intelligent Computing and Control Systems (ICICCS), IEEE","DOI":"10.1109\/ICICCS48265.2020.9120917"},{"key":"1579_CR19","unstructured":"Hazarika D, Zimmermann R, Poria S (2021) Multimodal sarcasm detection: A new benchmark and dataset. IEEE Trans Affect Comput"},{"key":"1579_CR20","unstructured":"Hengle A, Kshirsagar A, Patel M (2021) Combining context-free and contextualized representations for Arabic sarcasm detection and sentiment identification. arXiv preprint arXiv:2103.05683"},{"key":"1579_CR21","doi-asserted-by":"crossref","unstructured":"Huang Y-H, Huang H-H, Chen H-H (2017) Irony detection with attentive recurrent neural networks. 39th European Conference on IR Research (ECIR), Springer","DOI":"10.1007\/978-3-319-56608-5_45"},{"issue":"1","key":"1579_CR22","first-page":"10","volume":"13","author":"A Ivanov","year":"2020","unstructured":"Ivanov A, Popescu-Belis A (2020) Sarcasm detection for Romanian social media texts using transformer models. Rom J Human-Comput Interact 13(1):10\u201318","journal-title":"Rom J Human-Comput Interact"},{"issue":"5","key":"1579_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3124420","volume":"50","author":"A Joshi","year":"2017","unstructured":"Joshi A, Bhattacharyya P, Carman MJ (2017) Automatic sarcasm detection: A survey. ACM Comput Surv (CSUR) 50(5):1\u201322","journal-title":"ACM Comput Surv (CSUR)"},{"issue":"1","key":"1579_CR24","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s12559-021-09821-0","volume":"14","author":"A Kamal","year":"2022","unstructured":"Kamal A, Abulaish M (2022) Cat-BiGRU: Convolution and attention with bi-directional GRU for self-deprecating sarcasm detection. Cogn Comput 14(1):91\u2013109","journal-title":"Cogn Comput"},{"key":"1579_CR25","doi-asserted-by":"publisher","first-page":"7881","DOI":"10.1109\/ACCESS.2022.3143799","volume":"10","author":"S Khan","year":"2022","unstructured":"Khan S, Choudhury T, Razzak I (2022) HCovBi-caps: Hate speech detection using convolutional and Bi-directional gated recurrent unit with Capsule network. IEEE Access 10:7881\u20137894","journal-title":"IEEE Access"},{"key":"1579_CR26","doi-asserted-by":"crossref","unstructured":"Kumar A, Narapareddy VT, Srikanth V (2021) Adversarial and auxiliary features-aware BERT for sarcasm detection. 3rd ACM India Joint International Conference on Data Science & Management of Data (CODS-COMAD)","DOI":"10.1145\/3430984.3431024"},{"key":"1579_CR27","unstructured":"Li S, Caragea C (2021) Multitask learning with gradient-guided architecture search for sarcasm detection. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)"},{"issue":"1","key":"1579_CR28","first-page":"1","volume":"10","author":"J Liu","year":"2018","unstructured":"Liu J, Wang Y, Liu Q (2018) Learning neural fusion models for detecting sarcasm in multimodal social platforms. ACM Trans Intell Syst Technol (TIST) 10(1):1\u201325","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"key":"1579_CR29","unstructured":"Liu Y, Ott M, Goyal N, Du J, Joshi M, Chen D, Levy O, Lewis M, Zettlemoyer L, Stoyanov V (2019). RoBERTa: A robustly optimized BERT pretraining approach."},{"key":"1579_CR30","unstructured":"Maynard D, Greenwood MA (2014) Who cares about sarcastic tweets? Investigating the impact of sarcasm on sentiment analysis. LREC"},{"issue":"4","key":"1579_CR31","doi-asserted-by":"publisher","first-page":"465","DOI":"10.1080\/09720529.2019.1637152","volume":"22","author":"P Mehndiratta","year":"2019","unstructured":"Mehndiratta P, Soni D (2019) Identification of sarcasm using word embeddings and hyperparameter tuning. J Discrete Math Sci Cryptography 22(4):465\u2013489","journal-title":"J Discrete Math Sci Cryptography"},{"issue":"5","key":"1579_CR32","first-page":"62","volume":"31","author":"A Mishra","year":"2016","unstructured":"Mishra A, Bhattacharyya P (2016) Detecting sarcasm in a resource-constrained language using sentiment flow. IEEE Intell Syst 31(5):62\u201370","journal-title":"IEEE Intell Syst"},{"key":"1579_CR33","unstructured":"Mishra A, Tater A, Bhattacharyya P (2016) Domain adaptive sarcasm detection in a resource-poor language. ACL Workshop Sentiment Anal"},{"key":"1579_CR34","doi-asserted-by":"crossref","unstructured":"Naseem U, Razzak I, Musial K, Imran M (2020) Towards improved deep contextual embedding for the identification of irony and sarcasm. 2020 International Joint Conference on Neural Networks (IJCNN), IEEE","DOI":"10.1109\/IJCNN48605.2020.9207237"},{"key":"1579_CR35","unstructured":"Nayel H, Amer E, Allam A (2021) Machine learning-based model for sentiment and sarcasm detection. Sixth Arabic Natural Language Processing Workshop (WANLP)"},{"key":"1579_CR36","doi-asserted-by":"crossref","unstructured":"Oraby S, Harrison V, Reed L, Hernandez ED, Riloff E, Walker MA (2016) Creating and characterizing a diverse corpus of sarcasm in dialogue. LREC","DOI":"10.18653\/v1\/W16-3604"},{"key":"1579_CR37","unstructured":"Potamias RA, Lykousas N, Vafeiadis N, Papadopoulos S (2020) Transformer-based irony detection in social media: The role of pre-training and data. 58th Annual Meeting of the ACL, 1919\u20131929"},{"key":"1579_CR38","doi-asserted-by":"publisher","first-page":"17309","DOI":"10.1007\/s00521-020-05102-3","volume":"32","author":"RA Potamias","year":"2020","unstructured":"Potamias RA, Siolas G, Stafylopatis A (2020) A transformer-based approach to irony and sarcasm detection. Neural Comput Appl 32:17309\u201317320","journal-title":"Neural Comput Appl"},{"key":"1579_CR39","doi-asserted-by":"crossref","unstructured":"Rajadesingan A, Zafarani R, Liu H (2015) Sarcasm detection on Twitter: A behavioral modeling approach. Proceedings of the 8th ACM International Conference on Web Search and Data Mining (WSDM)","DOI":"10.1145\/2684822.2685316"},{"issue":"4","key":"1579_CR40","first-page":"1099","volume":"51","author":"A Reyes","year":"2017","unstructured":"Reyes A, Rosso P, Veale T (2017) A multidimensional approach for detecting irony in Twitter. Lang Resour Eval 51(4):1099\u20131119","journal-title":"Lang Resour Eval"},{"key":"1579_CR41","doi-asserted-by":"crossref","unstructured":"Riloff E, Qadir A, Surve P, De Silva L, Gilbert N, Huang R (2013) Sarcasm as contrast between a positive sentiment and negative situation. Conference on Empirical Methods in Natural Language Processing (EMNLP)","DOI":"10.18653\/v1\/D13-1066"},{"key":"1579_CR42","unstructured":"Sabour S, Frosst N, Hinton GE. (2017). Dynamic routing between capsules.Advances in Neural Information Processing Systems, 30, 3856\u20133866.https:\/\/arxiv.org\/abs\/1710.09829"},{"key":"1579_CR43","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s10791-025-09683-2","volume":"28","author":"RT Sairaj","year":"2025","unstructured":"Sairaj RT, Balasundaram SR (2025) Ensemble learning with RAG model to reduce redundant question topics in auto-generated exam questions. Discover Comput 28:161. https:\/\/doi.org\/10.1007\/s10791-025-09683-2","journal-title":"Discover Comput"},{"issue":"1","key":"1579_CR44","first-page":"219","volume":"52","author":"SM Sarsam","year":"2018","unstructured":"Sarsam SM, Al-Samarraie H (2018) A systematic review of sarcasm detection in social media: Opportunities and challenges. Artif Intell Rev 52(1):219\u2013239","journal-title":"Artif Intell Rev"},{"key":"1579_CR45","unstructured":"Sanh V, Debut L, Chaumond J, Wolf T. (2020). DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. InAdvances in Neural Information Processing Systems (NeurIPS 2020), Workshop on Challenges and Perspectives in Creating Large Language Models."},{"key":"1579_CR46","doi-asserted-by":"crossref","unstructured":"Schifanella R, De Francisci Morales G, Aiello LM (2016) Detecting sarcasm in multimodal social platforms. Proceedings of the 24th ACM International Conference on Multimedia, 113\u2013117","DOI":"10.1145\/2964284.2964321"},{"key":"1579_CR47","unstructured":"Tay Y, Tuan LA, Hui SC. (2018). A compare-aggregate model with dynamic-clip attention for deep text ranking. InProceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI '18)(pp. 4054\u20134060).https:\/\/arxiv.org\/abs\/1804.07567"},{"issue":"1","key":"1579_CR48","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1177\/15705838251343009","volume":"20","author":"R Tharaniya Sairaj","year":"2025","unstructured":"Tharaniya Sairaj R, Balasundaram SR (2025) Ontology Mapping for Retrieval Augmented Modelling to Reduce Factual Hallucinations in Pretrained Language Model-Based Auto-Generated Questions. Appl Ontol 20(1):69\u201388. https:\/\/doi.org\/10.1177\/15705838251343009","journal-title":"Appl Ontol"},{"key":"1579_CR49","doi-asserted-by":"crossref","unstructured":"Wallace BC, Choe DK, Charniak E, Mihalcea R (2014) Humans require context to infer ironic intent (so computational models should too). 52nd Annual Meeting of the Association for Computational Linguistics","DOI":"10.3115\/v1\/P14-2084"},{"key":"1579_CR50","unstructured":"Yang Z, Dai Z, Yang Y, Carbonell J, Salakhutdinov R, Le QV (2019). XLNet: Generalized autoregressive pretraining for language understanding. InAdvances in Neural Information Processing Systems, 32(pp. 5753\u20135763)."},{"issue":"1","key":"1579_CR51","first-page":"11286","volume":"36","author":"A Zhang","year":"2022","unstructured":"Zhang A, Sun A, Rao S, Liu Y (2022) Sarcasm detection with graph neural network and BERT. Proc AAAI Conf Artif Intell 36(1):11286\u201311294","journal-title":"Proc AAAI Conf Artif Intell"}],"container-title":["Social Network Analysis and Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13278-026-01579-3","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13278-026-01579-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13278-026-01579-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T08:44:45Z","timestamp":1772009085000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13278-026-01579-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,19]]},"references-count":51,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1579"],"URL":"https:\/\/doi.org\/10.1007\/s13278-026-01579-3","relation":{},"ISSN":["1869-5469"],"issn-type":[{"value":"1869-5469","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,19]]},"assertion":[{"value":"30 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 January 2026","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 January 2026","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 February 2026","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"41"}}