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In this study, we propose a novel ensemble model\u2014the\n            <jats:bold>Multi-type Classifier Ensemble (MtCE)<\/jats:bold>\n            \u2014combined with a textual-based featuring method, which is relatively independent of the system, to detect fake online consumer reviews. Unlike other ensemble models that utilise only the same type of single classifier, our proposed ensemble utilises several customised machine learning classifiers (including deep learning models) as its base classifiers. The results of our experiments show that the MtCE can adequately detect fake reviews, and that it outperforms other single and ensemble methods in terms of accuracy and other measurements for all the relevant public datasets used in this study. Moreover, if set correctly, the parameters of MtCE, such as base-classifier types, the total number of base classifiers, bootstrap, and the method to vote on output (e.g., majority or priority), can further improve the performance of the proposed ensemble.\n          <\/jats:p>","DOI":"10.1145\/3568676","type":"journal-article","created":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T13:01:45Z","timestamp":1666357305000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["A Multi-type Classifier Ensemble for Detecting Fake Reviews Through Textual-based Feature Extraction"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1929-7399","authenticated-orcid":false,"given":"Gregorius Satia","family":"Budhi","sequence":"first","affiliation":[{"name":"School of Information and Physical Sciences, The University of Newcastle, Callaghan, NSW, Australia, and Informatics Department, Petra Christian University, Surabaya, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8285-1903","authenticated-orcid":false,"given":"Raymond","family":"Chiong","sequence":"additional","affiliation":[{"name":"School of Information and Physical Sciences, The University of Newcastle, Callaghan, NSW, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2023,4,5]]},"reference":[{"issue":"10","key":"e_1_3_2_2_2","article-title":"Finding rotten eggs: A review spam detection model using diverse feature sets","volume":"12","author":"Akram A. 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