Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Memetic Spider Monkey Optimization for Spam Review Detection Problem Subject The topic of the resource big data; golden section search; memetic search; optimization; spam review; swarm intelligence Description An account of the resource Spider monkey optimization (SMO) algorithm imitates the spider monkey's fission-fusion social behavior. It is evident through literature that the SMO is a competitive swarm-based algorithm that is used to solve difficult real-life problems. The SMO's search process is a little bit biased by the random component that drives it with high explorative searching steps. A hybridized SMO with a memetic search to improve the local search ability of SMO is proposed here. The newly developed strategy is titled Memetic SMO (MeSMO). Further, the proposed MeSMO-based clustering approach is applied to solve a big data problem, namely, the spam review detection problem. A customer usually makes decisions to purchase something or make an image of someone based on online reviews. Therefore, there is a good chance that the individuals or companies may write spam reviews to upgrade or degrade the stature or value of a trader/product/company. Therefore, an efficient spam detection algorithm, MeSMO, is proposed and tested over four complex spam datasets. The reported results of MeSMO are compared with the outcomes obtained from the six state-of-art strategies. A comparative analysis of the results proved that MeSMO is a good technique to solve the spam review detection problem and improved precision by 3.68%. 2023 Mary Ann Liebert, Inc., publishers. Creator An entity primarily responsible for making the resource Shekhawat S.S.; Sharma H.; Kumar S. Source A related resource from which the described resource is derived Big Data, Vol-11, No. 2, pp. 137-149. Publisher An entity responsible for making the resource available Mary Ann Liebert Inc. Date A point or period of time associated with an event in the lifecycle of the resource 2023-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1089/big.2020.0188" target="_blank" rel="noreferrer noopener">https://doi.org/10.1089/big.2020.0188</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153123142&amp;doi=10.1089%2Fbig.2020.0188&amp;partnerID=40&amp;md5=6d411d93c49b0a8517cd843b96e16500" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85153123142&amp;doi=10.1089%2fbig.2020.0188&amp;partnerID=40&amp;md5=6d411d93c49b0a8517cd843b96e16500</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISSN: 21676461; PubMed ID: 34152859 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Shekhawat S.S., Department of Computer Science and Engineering, Rajasthan Technical University, Kota, India; Sharma H., Department of Computer Science and Engineering, Rajasthan Technical University, Kota, India; Kumar S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India