{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T19:35:49Z","timestamp":1778787349991,"version":"3.51.4"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T00:00:00Z","timestamp":1641254400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T00:00:00Z","timestamp":1641254400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Process Lett"],"published-print":{"date-parts":[[2022,6]]},"DOI":"10.1007\/s11063-021-10727-z","type":"journal-article","created":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T12:02:41Z","timestamp":1641297761000},"page":"2219-2247","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["Detection of Fake Job Postings by Utilizing Machine Learning and Natural Language Processing Approaches"],"prefix":"10.1007","volume":"54","author":[{"given":"Aashir","family":"Amaar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wajdi","family":"Aljedaani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8403-1047","authenticated-orcid":false,"given":"Furqan","family":"Rustam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Saleem","family":"Ullah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Vaibhav","family":"Rupapara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephanie","family":"Ludi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,4]]},"reference":[{"key":"10727_CR1","unstructured":"Becker R (2017) Your short attention span could help fake news spread. https:\/\/www.theverge.com\/2017\/6\/26\/15875488\/fake-news-viral-hoaxes-bots-information-overload-twitter-facebook-social-media"},{"key":"10727_CR2","unstructured":"Simmons G (2017) Market incentives that drive fraud: the truth behind reach vs. frequency. https:\/\/medium.com\/@gsimmons\/incentives-for-fraud-reach-vs-frequency-52d62d49ccbf"},{"issue":"6380","key":"10727_CR3","doi-asserted-by":"publisher","first-page":"1146","DOI":"10.1126\/science.aap9559","volume":"359","author":"S Vosoughi","year":"2018","unstructured":"Vosoughi S, Roy D, Aral S (2018) The spread of true and false news online. Science 359(6380):1146\u20131151","journal-title":"Science"},{"issue":"7","key":"10727_CR4","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1016\/j.im.2018.03.009","volume":"55","author":"D Cyr","year":"2018","unstructured":"Cyr D, Head M, Lim E, Stibe A (2018) Using the elaboration likelihood model to examine online persuasion through website design. Inf Manag 55(7):807\u2013821","journal-title":"Inf Manag"},{"issue":"1","key":"10727_CR5","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1080\/08838151.2015.1127248","volume":"60","author":"RA Hayes","year":"2016","unstructured":"Hayes RA, Carr CT, Wohn DY (2016) One click, many meanings: interpreting paralinguistic digital affordances in social media. J Broadcast Electron Media 60(1):171\u2013187","journal-title":"J Broadcast Electron Media"},{"key":"10727_CR6","doi-asserted-by":"publisher","first-page":"412","DOI":"10.1016\/j.chb.2017.03.002","volume":"72","author":"EJ Williams","year":"2017","unstructured":"Williams EJ, Beardmore A, Joinson AN (2017) Individual differences in susceptibility to online influence: a theoretical review. Comput Hum Behav 72:412\u2013421","journal-title":"Comput Hum Behav"},{"issue":"5","key":"10727_CR7","doi-asserted-by":"publisher","first-page":"e0175799","DOI":"10.1371\/journal.pone.0175799","volume":"12","author":"J Cook","year":"2017","unstructured":"Cook J, Lewandowsky S, Ecker UK (2017) Neutralizing misinformation through inoculation: exposing misleading argumentation techniques reduces their influence. PLoS One 12(5):e0175799","journal-title":"PLoS One"},{"issue":"8","key":"10727_CR8","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1016\/j.knosys.2008.03.044","volume":"21","author":"W Zhang","year":"2008","unstructured":"Zhang W, Yoshida T, Tang X (2008) Text classification based on multi-word with support vector machine. Knowl Based Syst 21(8):879\u2013886","journal-title":"Knowl Based Syst"},{"issue":"3","key":"10727_CR9","doi-asserted-by":"publisher","first-page":"5432","DOI":"10.1016\/j.eswa.2008.06.054","volume":"36","author":"J Chen","year":"2009","unstructured":"Chen J, Huang H, Tian S, Qu Y (2009) Feature selection for text classification with Na\u00efve Bayes. Expert Syst Appl 36(3):5432\u20135435","journal-title":"Expert Syst Appl"},{"key":"10727_CR10","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.imavis.2014.10.013","volume":"38","author":"C Wang","year":"2015","unstructured":"Wang C, Huang K (2015) How to use bag-of-words model better for image classification. Image Vis Comput 38:65\u201374","journal-title":"Image Vis Comput"},{"issue":"1","key":"10727_CR11","doi-asserted-by":"publisher","first-page":"6","DOI":"10.3390\/fi9010006","volume":"9","author":"S Vidros","year":"2017","unstructured":"Vidros S, Kolias C, Kambourakis G, Akoglu L (2017) Automatic detection of online recruitment frauds: characteristics, methods, and a public dataset. Future Internet 9(1):6","journal-title":"Future Internet"},{"key":"10727_CR12","doi-asserted-by":"crossref","unstructured":"Ahmed H, Traore I, Saad S (2017) Detection of online fake news using n-gram analysis and machine learning techniques. In: International conference on intelligent, secure, and dependable systems in distributed and cloud environments, pp 127\u2013138. Springer","DOI":"10.1007\/978-3-319-69155-8_9"},{"issue":"1","key":"10727_CR13","doi-asserted-by":"publisher","first-page":"e9","DOI":"10.1002\/spy2.9","volume":"1","author":"H Ahmed","year":"2018","unstructured":"Ahmed H, Traore I, Saad S (2018) Detecting opinion spams and fake news using text classification. Secur Priv 1(1):e9","journal-title":"Secur Priv"},{"key":"10727_CR14","doi-asserted-by":"crossref","unstructured":"Dutta S, Bandyopadhyay SK (2020) Fake job recruitment detection using machine learning approach. Int J Eng Trends Technol 68.4(2020):48\u201353","DOI":"10.14445\/22315381\/IJETT-V68I4P209S"},{"key":"10727_CR15","unstructured":"Shibly F, Sharma U, Naleer H (2021) Performance comparison of two class boosted decision tree snd two class decision forest algorithms in predicting fake job postings. Ann Rom Soc Cell Biol 25(4):2462\u20132472"},{"issue":"2","key":"10727_CR16","doi-asserted-by":"publisher","first-page":"642","DOI":"10.47059\/revistageintec.v11i2.1701","volume":"11","author":"C Anita","year":"2021","unstructured":"Anita C, Nagarajan P, Sairam GA, Ganesh P, Deepakkumar G (2021) Fake job detection and analysis using machine learning and deep learning algorithms. Revista Geintec-Gestao Inovacao e Tecnologias 11(2):642\u2013650","journal-title":"Revista Geintec-Gestao Inovacao e Tecnologias"},{"key":"10727_CR17","doi-asserted-by":"crossref","unstructured":"Aljedaani W, Javed Y, Alenezi M (2020) LDA categorization of security bug reports in chromium projects. In: Proceedings of the 2020 European symposium on software engineering, pp 154\u2013161","DOI":"10.1145\/3393822.3432335"},{"key":"10727_CR18","doi-asserted-by":"crossref","unstructured":"Aljedaani W, Nagappan M, Adams B, Godfrey M (2019) A comparison of bugs across the ios and android platforms of two open source cross platform browser apps. In: 2019 IEEE\/ACM 6th international conference on mobile software engineering and systems (MOBILESoft), pp 76\u201386. IEEE","DOI":"10.1109\/MOBILESoft.2019.00021"},{"key":"10727_CR19","doi-asserted-by":"crossref","unstructured":"Joulin A, Grave E, Bojanowski P, Mikolov T (2016) Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759","DOI":"10.18653\/v1\/E17-2068"},{"issue":"11","key":"10727_CR20","doi-asserted-by":"publisher","first-page":"1078","DOI":"10.3390\/e21111078","volume":"21","author":"F Rustam","year":"2019","unstructured":"Rustam F, Ashraf I, Mehmood A, Ullah S, Choi GS (2019) Tweets classification on the base of sentiments for us airline companies. Entropy 21(11):1078","journal-title":"Entropy"},{"issue":"5","key":"10727_CR21","first-page":"01","volume":"9","author":"R Sugumar","year":"2018","unstructured":"Sugumar R (2018) Improved performance of stemming using efficient stemmer algorithm for information retrieval. J Glob Res Comput Sci 9(5):01\u201305","journal-title":"J Glob Res Comput Sci"},{"key":"10727_CR22","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.compag.2016.08.015","volume":"128","author":"FF Bocca","year":"2016","unstructured":"Bocca FF, Rodrigues LHA (2016) The effect of tuning, feature engineering, and feature selection in data mining applied to rainfed sugarcane yield modelling. Comput Electron Agric 128:67\u201376","journal-title":"Comput Electron Agric"},{"key":"10727_CR23","doi-asserted-by":"crossref","unstructured":"Heaton J (2016) An empirical analysis of feature engineering for predictive modeling. In: SoutheastCon 2016, pp 1\u20136. IEEE","DOI":"10.1109\/SECON.2016.7506650"},{"key":"10727_CR24","doi-asserted-by":"crossref","unstructured":"Eshan SC, Hasan MS (2017) An application of machine learning to detect abusive bengali text. In: 2017 20th International conference of computer and information technology (ICCIT), pp 1\u20136. IEEE","DOI":"10.1109\/ICCITECHN.2017.8281787"},{"issue":"7","key":"10727_CR25","doi-asserted-by":"publisher","first-page":"5619","DOI":"10.1007\/s00500-020-05559-3","volume":"25","author":"X Ye","year":"2021","unstructured":"Ye X, Zheng Y, Aljedaani W, Mkaouer MW (2021) Recommending pull request reviewers based on code changes. Soft Comput 25(7):5619\u20135632","journal-title":"Soft Comput"},{"issue":"1","key":"10727_CR26","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.ijresmar.2018.09.009","volume":"36","author":"J Hartmann","year":"2019","unstructured":"Hartmann J, Huppertz J, Schamp C, Heitmann M (2019) Comparing automated text classification methods. Int J Res Mark 36(1):20\u201338","journal-title":"Int J Res Mark"},{"key":"10727_CR27","doi-asserted-by":"crossref","unstructured":"Safdari N, Alrubaye H, Aljedaani W, Baez BB, DiStasi A, Mkaouer MW (2019) Learning to rank faulty source files for dependent bug reports. In: Big data: learning, analytics, and applications, vol 10989, p 109890B. International Society for Optics and Photonics","DOI":"10.1117\/12.2519226"},{"key":"10727_CR28","doi-asserted-by":"publisher","first-page":"106667","DOI":"10.1016\/j.asoc.2020.106667","volume":"95","author":"B Alkhazi","year":"2020","unstructured":"Alkhazi B, DiStasi A, Aljedaani W, Alrubaye H, Ye X, Mkaouer MW (2020) Learning to rank developers for bug report assignment. Appl Soft Comput 95:106667","journal-title":"Appl Soft Comput"},{"issue":"3","key":"10727_CR29","doi-asserted-by":"publisher","first-page":"128","DOI":"10.14445\/22312803\/IJCTT-V48P126","volume":"48","author":"F Osisanwo","year":"2017","unstructured":"Osisanwo F, Akinsola J, Awodele O, Hinmikaiye J, Olakanmi O, Akinjobi J (2017) Supervised machine learning algorithms: classification and comparison. Int J Comput Trends Technol (IJCTT) 48(3):128\u2013138","journal-title":"Int J Comput Trends Technol (IJCTT)"},{"issue":"2","key":"10727_CR30","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1007\/s11749-016-0481-7","volume":"25","author":"G Biau","year":"2016","unstructured":"Biau G, Scornet E (2016) A random forest guided tour. Test 25(2):197\u2013227","journal-title":"Test"},{"key":"10727_CR31","doi-asserted-by":"crossref","unstructured":"AlOmar EA, Aljedaani W, Tamjeed M, Mkaouer MW, El-Glaly YN (2021) Finding the needle in a haystack: On the automatic identification of accessibility user reviews. In: Proceedings of the 2021 CHI conference on human factors in computing systems, pp 1\u201315","DOI":"10.1145\/3411764.3445281"},{"key":"10727_CR32","doi-asserted-by":"crossref","unstructured":"Xuan S, Liu G, Li Z, Zheng L, Wang S, Jiang C (2018) Random forest for credit card fraud detection. In: 2018 IEEE 15th international conference on networking, sensing and control (ICNSC), pp 1\u20136. IEEE","DOI":"10.1109\/ICNSC.2018.8361343"},{"issue":"20","key":"10727_CR33","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1007\/s00521-020-04761-6","volume":"32","author":"OA Alzubi","year":"2020","unstructured":"Alzubi OA, Alzubi JA, Alweshah M, Qiqieh I, Al-Shami S, Ramachandran M (2020) An optimal pruning algorithm of classifier ensembles: dynamic programming approach. Neural Comput Appl 32(20):91\u2013107","journal-title":"Neural Comput Appl"},{"issue":"3","key":"10727_CR34","doi-asserted-by":"publisher","first-page":"560","DOI":"10.1007\/s11704-018-7151-8","volume":"12","author":"T Sun","year":"2018","unstructured":"Sun T, Zhou Z-H (2018) Structural diversity for decision tree ensemble learning. Front Comput Sci 12(3):560\u2013570","journal-title":"Front Comput Sci"},{"issue":"10","key":"10727_CR35","doi-asserted-by":"publisher","first-page":"933","DOI":"10.1038\/nmeth.4438","volume":"14","author":"N Altman","year":"2017","unstructured":"Altman N, Krzywinski M (2017) Ensemble methods: bagging and random forests. Nat Methods 14(10):933\u2013935","journal-title":"Nat Methods"},{"issue":"13","key":"10727_CR36","doi-asserted-by":"publisher","first-page":"2964","DOI":"10.3390\/s19132964","volume":"19","author":"A Kukkar","year":"2019","unstructured":"Kukkar A, Mohana R, Nayyar A, Kim J, Kang B-G, Chilamkurti N (2019) A novel deep-learning-based bug severity classification technique using convolutional neural networks and random forest with boosting. Sensors 19(13):2964","journal-title":"Sensors"},{"issue":"6","key":"10727_CR37","doi-asserted-by":"publisher","first-page":"860","DOI":"10.1080\/10705511.2018.1473773","volume":"25","author":"PJ Curran","year":"2018","unstructured":"Curran PJ, Cole VT, Bauer DJ, Rothenberg WA, Hussong AM (2018) Recovering predictor-criterion relations using covariate-informed factor score estimates. Struct Equ Model Multidiscip J 25(6):860\u2013875","journal-title":"Struct Equ Model Multidiscip J"},{"key":"10727_CR38","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.physrep.2019.09.005","volume":"839","author":"F Ruehle","year":"2020","unstructured":"Ruehle F (2020) Data science applications to string theory. Phys Rep 839:1\u2013117","journal-title":"Phys Rep"},{"issue":"1","key":"10727_CR39","doi-asserted-by":"publisher","first-page":"012012","DOI":"10.1088\/1742-6596\/1142\/1\/012012","volume":"1142","author":"J Alzubi","year":"2018","unstructured":"Alzubi J, Nayyar A, Kumar A (2018) Machine learning from theory to algorithms: an overview. J Phys Conf Ser 1142(1):012012","journal-title":"J Phys Conf Ser"},{"issue":"2","key":"10727_CR40","doi-asserted-by":"publisher","first-page":"273","DOI":"10.1002\/asi.23649","volume":"68","author":"X Hu","year":"2017","unstructured":"Hu X, Choi K, Downie JS (2017) A framework for evaluating multimodal music mood classification. J Assoc Inf Sci Technol 68(2):273\u2013285","journal-title":"J Assoc Inf Sci Technol"},{"key":"10727_CR41","doi-asserted-by":"crossref","unstructured":"Ray S (2019) A quick review of machine learning algorithms. In: 2019 International conference on machine learning, big data, cloud and parallel computing (COMITCon), pp 35\u201339. IEEE","DOI":"10.1109\/COMITCon.2019.8862451"},{"key":"10727_CR42","doi-asserted-by":"crossref","unstructured":"Rustam F, Reshi AA, Ashraf I, Mehmood A, Ullah S, Khan DM, Choi GS (2020) Sensor-based human activity recognition using deep stacked multilayered perceptron model, vol\u00a08, pp 898\u2013910. IEEE Access","DOI":"10.1109\/ACCESS.2020.3041822"},{"key":"10727_CR43","doi-asserted-by":"crossref","unstructured":"Gosain A, Sardana S (2017) Handling class imbalance problem using oversampling techniques: a review. In: 2017 international conference on advances in computing, communications and informatics (ICACCI), pp 79\u201385. IEEE","DOI":"10.1109\/ICACCI.2017.8125820"},{"key":"10727_CR44","doi-asserted-by":"publisher","first-page":"7940","DOI":"10.1109\/ACCESS.2016.2619719","volume":"4","author":"A Amin","year":"2016","unstructured":"Amin A, Anwar S, Adnan A, Nawaz M, Howard N, Qadir J, Hawalah A, Hussain A (2016) Comparing oversampling techniques to handle the class imbalance problem: a customer churn prediction case study. IEEE Access 4:7940\u20137957","journal-title":"IEEE Access"},{"issue":"11","key":"10727_CR45","doi-asserted-by":"publisher","first-page":"7307","DOI":"10.1007\/s00500-021-05689-2","volume":"25","author":"F Fang","year":"2021","unstructured":"Fang F, Wu J, Li Y, Ye X, Aljedaani W, Mkaouer MW (2021) On the classification of bug reports to improve bug localization. Soft Comput 25(11):7307\u20137323","journal-title":"Soft Comput"},{"key":"10727_CR46","doi-asserted-by":"publisher","first-page":"772","DOI":"10.1016\/j.ins.2019.06.064","volume":"507","author":"J Xu","year":"2020","unstructured":"Xu J, Zhang Y, Miao D (2020) Three-way confusion matrix for classification: a measure driven view. Inf Sci 507:772\u2013794","journal-title":"Inf Sci"},{"key":"10727_CR47","doi-asserted-by":"publisher","first-page":"e645","DOI":"10.7717\/peerj-cs.645","volume":"7","author":"R Jamil","year":"2021","unstructured":"Jamil R, Ashraf I, Rustam F, Saad E, Mehmood A, Choi GS (2021) Detecting sarcasm in multi-domain datasets using convolutional neural networks and long short term memory network model. PeerJ Comput Sci 7:e645","journal-title":"PeerJ Comput Sci"},{"key":"10727_CR48","doi-asserted-by":"crossref","unstructured":"Dey R, Salem FM (2017) Gate-variants of gated recurrent unit (GRU) neural networks. In: (2017) IEEE 60th international midwest symposium on circuits and systems (MWSCAS), pp 1597\u20131600. IEEE","DOI":"10.1109\/MWSCAS.2017.8053243"},{"key":"10727_CR49","doi-asserted-by":"crossref","unstructured":"Zhang Z (2018) Improved adam optimizer for deep neural networks. In: 2018 IEEE\/ACM 26th international symposium on quality of service (IWQoS), pp 1\u20132. IEEE","DOI":"10.1109\/IWQoS.2018.8624183"},{"key":"10727_CR50","doi-asserted-by":"crossref","unstructured":"Rupapara V, Rustam F, Shahzad HF, Mehmood A, Ashraf I, Choi GS (2021) Impact of smote on imbalanced text features for toxic comments classification using RVVC model. IEEE Access","DOI":"10.1109\/ACCESS.2021.3083638"},{"key":"10727_CR51","doi-asserted-by":"crossref","unstructured":"Ranparia D, Kumari S, Sahani A (2020) Fake job prediction using sequential network. In: 2020 IEEE 15th international conference on industrial and information systems (ICIIS), pp 339\u2013343","DOI":"10.1109\/ICIIS51140.2020.9342738"},{"key":"10727_CR52","doi-asserted-by":"crossref","unstructured":"Keerthana B, Reddy AR, Tiwari A (2021) Accurate prediction of fake job offers using machine learning. In: Bhattacharyya D, Thirupathi Rao N (eds) Machine intelligence and soft computing, pp 101\u2013112. Springer","DOI":"10.1007\/978-981-15-9516-5_9"}],"container-title":["Neural Processing Letters"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10727-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11063-021-10727-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11063-021-10727-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,28]],"date-time":"2022-05-28T13:19:59Z","timestamp":1653743999000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11063-021-10727-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,4]]},"references-count":52,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2022,6]]}},"alternative-id":["10727"],"URL":"https:\/\/doi.org\/10.1007\/s11063-021-10727-z","relation":{},"ISSN":["1370-4621","1573-773X"],"issn-type":[{"value":"1370-4621","type":"print"},{"value":"1573-773X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,4]]},"assertion":[{"value":"18 December 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 January 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}