{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T22:35:27Z","timestamp":1765319727269,"version":"3.46.0"},"reference-count":50,"publisher":"Springer Science and Business Media LLC","issue":"36","license":[{"start":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T00:00:00Z","timestamp":1748995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T00:00:00Z","timestamp":1748995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-025-20922-y","type":"journal-article","created":{"date-parts":[[2025,6,4]],"date-time":"2025-06-04T06:17:24Z","timestamp":1749017844000},"page":"45601-45632","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Enhancing deceptive reviewer and review detection in e-commerce: analyzing ensemble Attention-based deep learning models"],"prefix":"10.1007","volume":"84","author":[{"given":"Sushil Kumar","family":"Maurya","sequence":"first","affiliation":[]},{"given":"Dinesh","family":"Singh","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,6,4]]},"reference":[{"key":"20922_CR1","doi-asserted-by":"crossref","unstructured":"Crawford M, Khoshgoftaar TM (2021) Using inductive transfer learning to improve hotel review spam detection. 2021 IEEE 22nd International Conference on Information Reuse and Integration for Data Science (IRI). IEEE","DOI":"10.1109\/IRI51335.2021.00040"},{"key":"20922_CR2","doi-asserted-by":"crossref","unstructured":"Aghakhani H et al (2018) Detecting deceptive reviews using generative adversarial networks. 2018 IEEE security and privacy workshops (SPW). IEEE","DOI":"10.1109\/SPW.2018.00022"},{"key":"20922_CR3","doi-asserted-by":"publisher","first-page":"53801","DOI":"10.1109\/ACCESS.2020.2979226","volume":"8","author":"N Hussain","year":"2020","unstructured":"Hussain N et al (2020) Spam review detection using the linguistic and spammer behavioral methods. IEEE Access 8:53801\u201353816","journal-title":"IEEE Access"},{"issue":"5","key":"20922_CR4","doi-asserted-by":"publisher","first-page":"3475","DOI":"10.1007\/s00500-019-04107-y","volume":"24","author":"M Asghar","year":"2020","unstructured":"Asghar M, Zubair et al (2020) Opinion spam detection framework using hybrid classification scheme. Soft Comput 24(5):3475\u20133498","journal-title":"Soft Comput"},{"issue":"4","key":"20922_CR5","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1016\/j.ipm.2019.03.002","volume":"56","author":"R Barbado","year":"2019","unstructured":"Barbado R, Araque O, Carlos A, Iglesias (2019) A framework for fake review detection in online consumer electronics retailers. Inf Process Manag 56(4):1234\u20131244","journal-title":"Inf Process Manag"},{"key":"20922_CR6","unstructured":"Ott M et al (2011) Finding deceptive opinion spam by any stretch of the imagination. ArXiv Preprint ArXiv:11074557"},{"key":"20922_CR7","doi-asserted-by":"crossref","unstructured":"Li J et al (2014) Towards a general rule for identifying deceptive opinion spam. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","DOI":"10.3115\/v1\/P14-1147"},{"key":"20922_CR8","doi-asserted-by":"crossref","unstructured":"Hajek P, Barushka A (2019) A comparative study of machine learning methods for detection of fake online consumer reviews. Proceedings of the 2019 3rd international conference on E-Business and Internet","DOI":"10.1145\/3383902.3383909"},{"key":"20922_CR9","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1016\/j.neucom.2019.08.013","volume":"366","author":"Y Liu","year":"2019","unstructured":"Liu Y, Pang B, Wang X (2019) Opinion spam detection by incorporating multimodal embedded representation into a probabilistic review graph. Neurocomputing 366:276\u2013283","journal-title":"Neurocomputing"},{"key":"20922_CR10","first-page":"1","volume":"12","author":"N Sultana","year":"2020","unstructured":"Sultana N (2020) Deceptive opinion detection using machine learning techniques. Int J Inform Eng Electron Bus 12:1","journal-title":"Int J Inform Eng Electron Bus"},{"key":"20922_CR11","doi-asserted-by":"crossref","unstructured":"Mukherjee A et al (2013) Spotting opinion spammers using behavioral footprints. Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining","DOI":"10.1145\/2487575.2487580"},{"key":"20922_CR12","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1016\/j.ins.2020.03.063","volume":"526","author":"X Tang","year":"2020","unstructured":"Tang X, Qian T, Zhenni You (2020) Generating behavior features for cold-start spam review detection with adversarial learning. Inf Sci 526:274\u2013288","journal-title":"Inf Sci"},{"key":"20922_CR13","doi-asserted-by":"crossref","unstructured":"Xiang L et al (2022) Deep feature fusion for cold-start spam review detection. J Supercomputing : 1\u201316","DOI":"10.1007\/s11227-022-04685-z"},{"key":"20922_CR14","doi-asserted-by":"crossref","unstructured":"Maurya SK, Singh D, Maurya AK\u00a0(2022) Deceptive opinion spam detection approaches: a literature survey. Appl Intell : 1\u201346","DOI":"10.1007\/s10489-022-03427-1"},{"issue":"12","key":"20922_CR15","doi-asserted-by":"publisher","first-page":"18107","DOI":"10.1007\/s11042-021-10602-y","volume":"80","author":"P Bhuvaneshwari","year":"2021","unstructured":"Bhuvaneshwari P, Nagaraja Rao A, Harold Robinson Y (2021) Spam review detection using self attention based CNN and bi-directional LSTM. Multimedia Tools Appl 80(12):18107\u201318124","journal-title":"Multimedia Tools Appl"},{"key":"20922_CR16","doi-asserted-by":"crossref","unstructured":"Sun C, Du Q, Tian G (2016) Exploiting product related review features for fake review detection. Math Probl Eng 2016","DOI":"10.1155\/2016\/4935792"},{"issue":"4","key":"20922_CR17","doi-asserted-by":"publisher","first-page":"576","DOI":"10.1016\/j.ipm.2018.03.007","volume":"54","author":"W Zhang","year":"2018","unstructured":"Zhang W et al (2018) DRI-RCNN: an approach to deceptive review identification using recurrent convolutional neural network. Inf Process Manag 54(4):576\u2013592","journal-title":"Inf Process Manag"},{"key":"20922_CR18","unstructured":"Mikolov T et al (2013) Distributed representations of words and phrases and their compositionality. Adv Neural Inf Process Syst 26"},{"key":"20922_CR19","doi-asserted-by":"crossref","unstructured":"Pennington J, Socher R, Manning CD (2014) Glove: Global vectors for word representation. Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)","DOI":"10.3115\/v1\/D14-1162"},{"issue":"4","key":"20922_CR20","doi-asserted-by":"publisher","first-page":"1595","DOI":"10.1109\/TCYB.2018.2877161","volume":"50","author":"Z Wu","year":"2018","unstructured":"Wu Z et al (2018) hPSD: a hybrid PU-learning-based spammer detection model for product reviews. IEEE Trans Cybernetics 50(4):1595\u20131606","journal-title":"IEEE Trans Cybernetics"},{"issue":"20","key":"20922_CR21","doi-asserted-by":"publisher","first-page":"e4686","DOI":"10.1002\/cpe.4686","volume":"30","author":"L Zhang","year":"2018","unstructured":"Zhang L et al (2018) Spotting review spammer groups: a cosine pattern and network based method. Concurrency Computation: Pract Experience 30(20):e4686","journal-title":"Concurrency Computation: Pract Experience"},{"issue":"3","key":"20922_CR22","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1007\/s10115-017-1068-7","volume":"55","author":"Z Wang","year":"2018","unstructured":"Wang Z et al (2018) Graph-based review spammer group detection. Knowl Inf Syst 55(3):571\u2013597","journal-title":"Knowl Inf Syst"},{"key":"20922_CR23","doi-asserted-by":"publisher","first-page":"105520","DOI":"10.1016\/j.knosys.2020.105520","volume":"193","author":"F Zhang","year":"2020","unstructured":"Zhang F et al (2020) Label propagation-based approach for detecting review spammer groups on e-commerce websites. Knowl Based Syst 193:105520","journal-title":"Knowl Based Syst"},{"key":"20922_CR24","doi-asserted-by":"crossref","unstructured":"Mukherjee A, Liu B, Glance N (2012) Spotting fake reviewer groups in consumer reviews. Proceedings of the 21st international conference on World Wide Web","DOI":"10.1145\/2187836.2187863"},{"key":"20922_CR25","doi-asserted-by":"publisher","first-page":"2559","DOI":"10.1109\/ACCESS.2017.2784370","volume":"6","author":"L Zhang","year":"2017","unstructured":"Zhang L, Wu Z, Cao J (2017) Detecting spammer groups from product reviews: a partially supervised learning model. IEEE Access 6:2559\u20132568","journal-title":"IEEE Access"},{"key":"20922_CR26","doi-asserted-by":"crossref","unstructured":"Fei G et al (2013) Exploiting burstiness in reviews for review spammer detection. Proceedings of the international AAAI conference on web and social media. Vol 7. No. 1","DOI":"10.1609\/icwsm.v7i1.14400"},{"key":"20922_CR27","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1016\/j.ins.2020.05.084","volume":"536","author":"Shu-juan Ji","year":"2020","unstructured":"Ji Shu-juan et al (2020) A burst-based unsupervised method for detecting review spammer groups. Inf Sci 536:454\u2013469","journal-title":"Inf Sci"},{"key":"20922_CR28","doi-asserted-by":"publisher","first-page":"1319","DOI":"10.1109\/ACCESS.2017.2655032","volume":"5","author":"J Rout","year":"2017","unstructured":"Rout J, Kumar et al (2017) Revisiting semi-supervised learning for online deceptive review detection. IEEE Access 5:1319\u20131327","journal-title":"IEEE Access"},{"issue":"3","key":"20922_CR29","doi-asserted-by":"publisher","first-page":"3187","DOI":"10.1007\/s11042-016-3819-y","volume":"76","author":"J Rout","year":"2017","unstructured":"Rout J, Kumar et al (2017) Deceptive review detection using labeled and unlabeled data. Multimedia Tools Appl 76(3):3187\u20133211","journal-title":"Multimedia Tools Appl"},{"key":"20922_CR30","unstructured":"Noekhah S et al (2014) A novel approach for opinion spam detection in e-commerce. Proceedings of the 8th IEEE international conference on E-commerce with focus on E-trust"},{"key":"20922_CR31","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.neucom.2016.10.080","volume":"254","author":"L Li","year":"2017","unstructured":"Li L et al (2017) Document representation and feature combination for deceptive spam review detection. Neurocomputing 254:33\u201341","journal-title":"Neurocomputing"},{"key":"20922_CR32","doi-asserted-by":"crossref","unstructured":"Saumya S, Singh JP (2020) Spam review detection using LSTM autoencoder: an unsupervised approach. Electron Commer Res: 1\u201321","DOI":"10.1007\/s10660-020-09413-4"},{"issue":"3","key":"20922_CR33","doi-asserted-by":"publisher","first-page":"701","DOI":"10.1007\/s00607-019-00763-y","volume":"102","author":"W Liu","year":"2020","unstructured":"Liu W, Jing W, Li Y (2020) Incorporating feature representation into BiLSTM for deceptive review detection. Computing 102(3):701\u2013715","journal-title":"Computing"},{"issue":"8","key":"20922_CR34","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"Sepp Hochreiter and J\u00a8urgen Schmidhuber","year":"1997","unstructured":"Sepp Hochreiter and J\u00a8urgen Schmidhuber (1997) Long short-term memory. Neural Comput 9(8):1735\u20131780","journal-title":"Neural Comput"},{"key":"20922_CR35","doi-asserted-by":"crossref","unstructured":"Wang Z, Li H, Wang H (2022) Vote-based integration of review spam detection algorithms. Appl Intell: 1\u201312","DOI":"10.1007\/s10489-022-03807-7"},{"issue":"1","key":"20922_CR36","doi-asserted-by":"publisher","first-page":"1407","DOI":"10.1016\/j.jksuci.2019.10.002","volume":"34","author":"RMK Saeed","year":"2022","unstructured":"Saeed RMK, Sherine Rady, Tarek F, Gharib (2022) An ensemble approach for spam detection in Arabic opinion texts. J King Saud University-Computer Inform Sci 34(1):1407\u20131416","journal-title":"J King Saud University-Computer Inform Sci"},{"key":"20922_CR37","doi-asserted-by":"crossref","unstructured":"Rao S, Verma AK, Bhatia T (2023) Hybrid ensemble framework with Self-Attention mechanism for social spam detection on imbalanced data. Expert Syst Appl: 119594","DOI":"10.1016\/j.eswa.2023.119594"},{"issue":"6","key":"20922_CR38","doi-asserted-by":"publisher","first-page":"4897","DOI":"10.1007\/s40747-022-00741-6","volume":"8","author":"MA Shaaban","year":"2022","unstructured":"Shaaban MA, Yasser F, Hassan, Shawkat K (2022) Guirguis. Deep convolutional forest: a dynamic deep ensemble approach for spam detection in text. Complex Intell Syst 8(6):4897\u20134909","journal-title":"Complex Intell Syst"},{"key":"20922_CR39","doi-asserted-by":"crossref","unstructured":"Jindal N, Liu B (2008) Opinion spam and analysis. Proceedings of the 2008 international conference on web search and data mining","DOI":"10.1145\/1341531.1341560"},{"key":"20922_CR40","doi-asserted-by":"crossref","unstructured":"Algur SP et al (2010) Conceptual level similarity measure based review spam detection. 2010 International Conference on Signal and Image Processing. IEEE","DOI":"10.1109\/ICSIP.2010.5697509"},{"issue":"4","key":"20922_CR41","first-page":"1","volume":"2","author":"YK Lau, Raymond","year":"2012","unstructured":"Lau, Raymond YK et al (2012) Text mining and probabilistic Language modeling for online review spam detection. ACM Trans Manage Inform Syst (TMIS) 2(4):1\u201330","journal-title":"ACM Trans Manage Inform Syst (TMIS)"},{"key":"20922_CR42","doi-asserted-by":"crossref","unstructured":"Kim S et al (2015) Deep semantic frame-based deceptive opinion spam analysis. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management","DOI":"10.1145\/2806416.2806551"},{"key":"20922_CR43","doi-asserted-by":"crossref","unstructured":"Jia S et al (2018) Fake reviews detection based on LDA. 2018 4th International Conference on Information Management (ICIM). Ieee","DOI":"10.1109\/INFOMAN.2018.8392850"},{"key":"20922_CR44","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.eswa.2018.07.005","volume":"114","author":"Lu-yu Dong","year":"2018","unstructured":"Dong Lu-yu et al (2018) An unsupervised topic-sentiment joint probabilistic model for detecting deceptive reviews. Expert Syst Appl 114:210\u2013223","journal-title":"Expert Syst Appl"},{"key":"20922_CR45","doi-asserted-by":"crossref","unstructured":"McAuley J, Pandey R, Leskovec J (2015) Inferring networks of substitutable and complementary products. Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining","DOI":"10.1145\/2783258.2783381"},{"issue":"1","key":"20922_CR46","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1080\/07421222.2018.1440758","volume":"35","author":"N Kumar","year":"2018","unstructured":"Kumar N et al (2018) Detecting review manipulation on online platforms with hierarchical supervised learning. J Manage Inform Syst 35(1):350\u2013380","journal-title":"J Manage Inform Syst"},{"issue":"2","key":"20922_CR47","doi-asserted-by":"publisher","first-page":"456","DOI":"10.1080\/07421222.2016.1205907","volume":"33","author":"D Zhang","year":"2016","unstructured":"Zhang D et al (2016) What online reviewer behaviors really matter? Effects of verbal and nonverbal behaviors on detection of fake online reviews. J Manage Inform Syst 33(2):456\u2013481","journal-title":"J Manage Inform Syst"},{"key":"20922_CR48","unstructured":"Khalifa MB, Elouedi Z, Lefevre E\u00a0(2020) An Evidential Spammer Detection based on the Suspicious Behaviors\u2019 Indicators. 2020 International Multi-Conference on: Organization of Knowledge and Advanced Technologies(OCTA). IEEE"},{"key":"20922_CR49","doi-asserted-by":"publisher","first-page":"113911","DOI":"10.1016\/j.dss.2022.113911","volume":"166","author":"D Zhang","year":"2023","unstructured":"Zhang D et al (2023) A deep learning approach for detecting fake reviewers: exploiting reviewing behavior and textual information. Decis Support Syst 166:113911","journal-title":"Decis Support Syst"},{"key":"20922_CR50","doi-asserted-by":"crossref","unstructured":"Mewada A, Dewang RK (2024) SUH-AIFRD: A self-training-based hybrid approach for individual fake reviewer detection. Multimedia Tools Appl: 1\u201329","DOI":"10.1007\/s11042-024-18192-1"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-20922-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-025-20922-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-025-20922-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,9]],"date-time":"2025-12-09T22:34:35Z","timestamp":1765319675000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-025-20922-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,4]]},"references-count":50,"journal-issue":{"issue":"36","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["20922"],"URL":"https:\/\/doi.org\/10.1007\/s11042-025-20922-y","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2025,6,4]]},"assertion":[{"value":"12 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 April 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 May 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 June 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This article does not involve human participants or animals.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal participants"}},{"value":"The manuscript is approved by all authors for publication.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The authors declare that they have no conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}