Fake Job Post Detection Using Machine Learning

Fake Job Post Detection Using Machine Learning | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 27 May 2025 V1 Latest version Share on Fake Job Post Detection Using Machine Learning Authors : Aman Kumar Singh Jadaun , Raju kumar , Neeraj kumar , and Yogesh saini [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174835478.87545008/v1 571 views 148 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Nowadays, it’s hard for job seekers to figure out which job postings are real and which ones are fake. To solve this problem, we’ve built a smart tool that uses advanced technology to detect and remove fake job ads. We looked at various methods to study job ads and checked how effectively each one functioned. After doing many tests, we saw that mixing different techniques (ensemble methods) gave the top results in finding fake job postings. Our system, Fake Job Post Prediction, is based on a strong and trustworthy model, the Random Forest Algorithm (RFA), which helps a lot with making accuracy better. This system is known for being both fast and accurate, and it achieves an impressive 98% accuracy rate—way better than older techniques. The goal of this tool is to protect job seekers from scams, like fake job offers or requests for money during the application process. By identifying and removing fake job ads early, it makes the job search process much safer and more trustworthy. This tool is a big step forward—it helps people tell the difference between real and fake job opportunities and creates a safer online space for job hunting. In today’s tricky job market, this tool is a must-have for anyone looking for work. Supplementary Material File (fake job post detection using machine learning.docx) Download 712.06 KB Information & Authors Information Version history V1 Version 1 27 May 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords machine learning random forest svm Authors Affiliations Aman Kumar Singh Jadaun Galgotias University View all articles by this author Raju kumar Galgotias University View all articles by this author Neeraj kumar Galgotias University View all articles by this author Yogesh saini [email protected] Galgotias University View all articles by this author Metrics & Citations Metrics Article Usage 571 views 148 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Aman Kumar Singh Jadaun, Raju kumar, Neeraj kumar, et al. Fake Job Post Detection Using Machine Learning. Authorea . 27 May 2025. DOI: https://doi.org/10.22541/au.174835478.87545008/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. 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