{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T08:01:27Z","timestamp":1771488087204,"version":"3.50.1"},"reference-count":38,"publisher":"Emerald","issue":"5","license":[{"start":{"date-parts":[[2021,2,11]],"date-time":"2021-02-11T00:00:00Z","timestamp":1613001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJPCC"],"published-print":{"date-parts":[[2021,12,10]]},"abstract":"<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title>\n<jats:p>The purpose of this paper is to enhance the performance of spammer identification problem in online social networks. Hyperparameter tuning has been performed by researchers in the past to enhance the performance of classifiers. The AdaBoost algorithm belongs to a class of ensemble classifiers and is widely applied in binary classification problems. A single algorithm may not yield accurate results. However, an ensemble of classifiers built from multiple models has been successfully applied to solve many classification tasks. The search space to find an optimal set of parametric values is vast and so enumerating all possible combinations is not feasible. Hence, a hybrid modified whale optimization algorithm for spam profile detection (MWOA-SPD) model is proposed to find optimal values for these parameters.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title>\n<jats:p>In this work, the hyperparameters of AdaBoost are fine-tuned to find its application to identify spammers in social networks. AdaBoost algorithm linearly combines several weak classifiers to produce a stronger one. The proposed MWOA-SPD model hybridizes the whale optimization algorithm and salp swarm algorithm.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Findings<\/jats:title>\n<jats:p>The technique is applied to a manually constructed Twitter data set. It is compared with the existing optimization and hyperparameter tuning methods. The results indicate that the proposed method outperforms the existing techniques in terms of accuracy and computational efficiency.<\/jats:p>\n<\/jats:sec>\n<jats:sec>\n<jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title>\n<jats:p>The proposed method reduces the server load by excluding complex features retaining only the lightweight features. It aids in identifying the spammers at an earlier stage thereby offering users a propitious environment.<\/jats:p>\n<\/jats:sec>","DOI":"10.1108\/ijpcc-09-2020-0130","type":"journal-article","created":{"date-parts":[[2021,2,13]],"date-time":"2021-02-13T09:53:41Z","timestamp":1613210021000},"page":"462-482","source":"Crossref","is-referenced-by-count":12,"title":["Hyperparameter tuning of AdaBoost algorithm for social spammer identification"],"prefix":"10.1108","volume":"17","author":[{"given":"Krithiga","family":"R.","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ilavarasan","family":"E.","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"140","published-online":{"date-parts":[[2021,2,11]]},"reference":[{"key":"key2021123111191189300_ref001","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2018.10.069","article-title":"An efficient salp swarm-inspired algorithm for parameters identification of photovoltaic cell models","volume":"179","year":"2018","journal-title":"Energy Conversion and Management"},{"key":"key2021123111191189300_ref002","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.future.2018.03.020","article-title":"A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem","volume":"85","year":"2018","journal-title":"Future Generation Computer Systems"},{"key":"key2021123111191189300_ref003","article-title":"Machine learning with python cookbook: practical solutions from preprocessing to deep learning, first edition","year":"2018"},{"key":"key2021123111191189300_ref005","doi-asserted-by":"publisher","article-title":"Tuning parameter estimation in SCAD-support vector machine using firefly algorithm with application in gene selection and cancer classification, computers in biology and medicine","year":"2018","DOI":"10.1016\/j.compbiomed.2018.10.034"},{"key":"key2021123111191189300_ref006","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2018.04.025","article-title":"Evolving support vector machines using whale optimization algorithm for spam profiles detection on online social networks in different lingual contexts","year":"2018","journal-title":"Knowledge Based Systems"},{"key":"key2021123111191189300_ref008","first-page":"34","article-title":"Hyperopt-Sklearn: automatic hyperparameter configuration for Scikit-Learn","volume-title":"Proc. of the 13th Python in Science Conf., SCIPY","year":"2014"},{"key":"key2021123111191189300_ref009","doi-asserted-by":"publisher","first-page":"396","DOI":"10.1016\/j.patrec.2019.05.021","article-title":"Classification complexity assessment for hyper-parameter optimization","volume":"125","year":"2019","journal-title":"Pattern Recognition Letters"},{"key":"key2021123111191189300_ref010","doi-asserted-by":"publisher","first-page":"301","DOI":"10.1109\/INCISCOS.2018.00050","article-title":"Parameters tuning and optimization for reinforcement learning algorithms using evolutionary computing","volume-title":"International Conference on Information Systems and Computer Science (INCISCOS)","year":"2018"},{"key":"key2021123111191189300_ref011","first-page":"2035","article-title":"Model selection of SVMs using GA approach","volume-title":"IEEE International Joint Conference on Neural Networks Vol. 3","year":"2004"},{"key":"key2021123111191189300_ref012","first-page":"3","article-title":"Hyperparameter tuning in python using optunity","year":"2014"},{"issue":"6","key":"key2021123111191189300_ref013","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1016\/S1874-1029(13)60052-X","article-title":"Advance and prospects of AdaBoost algorithm","volume":"39","year":"2013","journal-title":"Acta Automatica Sinica"},{"issue":"3","key":"key2021123111191189300_ref014","doi-asserted-by":"crossref","first-page":"131","DOI":"10.3233\/IDA-1997-1302","article-title":"Feature selection for classification","volume":"1","year":"1997","journal-title":"Intelligent Data Analysis"},{"key":"key2021123111191189300_ref015","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/RoboMech.2016.7813162","article-title":"Feature selection and support vector machine hyper-parameter optimisation for spam detection","volume-title":"2016 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech)","year":"2016"},{"issue":"2","key":"key2021123111191189300_ref016","first-page":"1","article-title":"Grey wolf optimizer: a review of recent variants and applications","volume":"30","year":"2017","journal-title":"Neural Computing and Applications"},{"key":"key2021123111191189300_ref017","first-page":"148","article-title":"Experiments with a new boosting algorithm","volume-title":"Proc. Thirteenth International Conference on Machine Learning","year":"1996"},{"key":"key2021123111191189300_ref018","first-page":"42","article-title":"Fish image segmentation using salp swarm algorithm","year":"2018"},{"issue":"8","key":"key2021123111191189300_ref019","doi-asserted-by":"publisher","DOI":"10.1007\/s12652-018-1031-9","article-title":"Improved salp swarm algorithm based on particle swarm optimization for feature selection","volume":"10","year":"2018","journal-title":"Journal of Ambient Intelligence and Humanized Computing"},{"key":"key2021123111191189300_ref020","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1016\/j.neucom.2018.07.044","article-title":"Ioannis Korkontzelos, detection of spam-posting accounts on twitter","volume":"315","year":"2018","journal-title":"Neurocomputing"},{"issue":"3","key":"key2021123111191189300_ref021","doi-asserted-by":"publisher","first-page":"345","DOI":"10.1080\/15397734.2016.1213639","article-title":"Enhanced whale optimization algorithm for sizing optimization of skeletal structures","volume":"45","year":"2017","journal-title":"Mechanics Based Design of Structures and Machines"},{"key":"key2021123111191189300_ref022","doi-asserted-by":"publisher","DOI":"10.1016\/j.micpro.2020.103451","article-title":"A reliable modified whale optimization algorithm based approach for feature selection to classify twitter spam profiles","year":"2020","journal-title":"Microprocessors and Microsystems"},{"issue":"1","key":"key2021123111191189300_ref023","doi-asserted-by":"publisher","first-page":"260","DOI":"10.14704\/WEB\/V17I1\/WEB17003","article-title":"A novel hybrid algorithm to classify spam profiles in Twitter","volume":"17","year":"2020","journal-title":"Webology"},{"key":"key2021123111191189300_ref004","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1007\/s12559-017-9542-9","article-title":"Evolutionary population dynamics and grasshopper optimization approaches for feature selection problems","volume":"145","year":"2018","journal-title":"Knowledge-Based Systems"},{"key":"key2021123111191189300_ref024","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.advengsoft.2016.01.008","article-title":"The whale optimization algorithm","volume":"95","year":"2016","journal-title":"Advances in Engineering Software"},{"key":"key2021123111191189300_ref031","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.advengsoft.2017.07.002","article-title":"Salp swarm algorithm: a bio-inspired optimizer for engineering design problems","volume":"114","year":"2017","journal-title":"Advances in Engineering Software"},{"issue":"23","key":"key2021123111191189300_ref025","doi-asserted-by":"publisher","first-page":"24931","DOI":"10.1007\/s11042-017-4638-5","article-title":"Liver segmentation in MRI images based on whale optimization algorithm","volume":"76","year":"2017","journal-title":"Multimedia Tools and Applications"},{"key":"key2021123111191189300_ref026","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2017.09.001","article-title":"Fuzzy self-tuning PSO: a settings-free algorithm for global optimization","volume":"39","year":"2017","journal-title":"Swarm and Evolutionary Computation"},{"key":"key2021123111191189300_ref027","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1016\/j.apenergy.2017.05.029","article-title":"Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm","volume":"200","year":"2017","journal-title":"Applied Energy"},{"key":"key2021123111191189300_ref028","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1016\/j.engappai.2019.01.011","article-title":"Enhanced salp swarm algorithm: application to variable speed wind generators","volume":"80","year":"2019","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"key2021123111191189300_ref029","doi-asserted-by":"publisher","first-page":"255","DOI":"10.15439\/2019F183","article-title":"Deep learning hyper-parameter tuning for sentiment analysis in twitter based on evolutionary algorithms","volume-title":"the Proceedings of the Federated Conference on Computer Science and Information Systems, ISSN 2300-5963 ACSIS, Vol. 18","year":"2019"},{"key":"key2021123111191189300_ref030","doi-asserted-by":"publisher","first-page":"7164","DOI":"10.1109\/ACCESS.2017.2779794","article-title":"Optimal hyper-parameter tuning of SVM classifiers with application to medical diagnosis","volume":"6","year":"2018","journal-title":"IEEE Access"},{"key":"key2021123111191189300_ref032","unstructured":"Smartinsights (2020), available at: www.smartinsights.com\/social-media-marketing\/social-media-strategy\/new-global-social-media-research\/"},{"key":"key2021123111191189300_ref033","first-page":"1","article-title":"Auto tuning of RNN hyper-parameters using Cuckoo search algorithm","year":"2019"},{"key":"key2021123111191189300_ref034","article-title":"Hyperparameter tuning for big data using Bayesian optimisation","volume-title":"23rd International Conference on Pattern Recognition (ICPR), M\u00e9xico, December 4-8","year":"2016"},{"issue":"6","key":"key2021123111191189300_ref035","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/0305215X.2010.508524","article-title":"The development of a hybridized particle swarm for kriging hyperparameter tuning","volume":"43","year":"2011","journal-title":"Engineering Optimization"},{"key":"key2021123111191189300_ref036","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.swevo.2019.06.002","article-title":"cPSO-CNN: an efficient PSO-based algorithm for fine-tuning hyper-parameters of convolutional neural networks","volume":"49","year":"2019","journal-title":"Swarm and Evolutionary Computation"},{"key":"key2021123111191189300_ref037","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.eswa.2017.02.017","article-title":"A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring","volume":"78","year":"2017","journal-title":"Expert Systems with Applications"},{"key":"key2021123111191189300_ref007","doi-asserted-by":"publisher","first-page":"1203","DOI":"10.1016\/j.jclepro.2019.01.150","article-title":"Novel bio-inspired mimetic salp swarm algorithm and application to MPPT for PV systems considering partial shading condition","volume":"215","year":"2019","journal-title":"Journal of Cleaner Production"},{"key":"key2021123111191189300_ref038","volume-title":"Social Media Mining: An Introduction","year":"2014"}],"container-title":["International Journal of Pervasive Computing and Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJPCC-09-2020-0130\/full\/xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.emerald.com\/insight\/content\/doi\/10.1108\/IJPCC-09-2020-0130\/full\/html","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T22:05:48Z","timestamp":1753394748000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.emerald.com\/ijpcc\/article\/17\/5\/462-482\/162849"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,2,11]]},"references-count":38,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,2,11]]},"published-print":{"date-parts":[[2021,12,10]]}},"alternative-id":["10.1108\/IJPCC-09-2020-0130"],"URL":"https:\/\/doi.org\/10.1108\/ijpcc-09-2020-0130","relation":{},"ISSN":["1742-7371","1742-7371"],"issn-type":[{"value":"1742-7371","type":"print"},{"value":"1742-7371","type":"print"}],"subject":[],"published":{"date-parts":[[2021,2,11]]}}}