[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123828-en":3,"doc-seo-123828-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123828,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Identifying Real and Fake Job Posting using Machine Learning","Job portals and internet platforms host vast numbers of vacancies, yet many applicants cannot verify whether an employer and posting are genuine or fabricated. Scammers exploit weak employer background checks to publish realistic-looking offers, causing job seekers to waste substantial money and time. This study proposes a machine learning approach using natural language processing on job text to predict authenticity. Using the Employment Scam Aegean Dataset and text cleaning, models are trained and evaluated.","Journal of Advanced Zoology  \nISSN: 0253-7214  \nVolume 44 IssueS-6 Year 2023 Page 622:627  \nIdentifying Real and Fake Job Posting using Machine Learning  \nSherina Sara Jaison1*, Dr Mallikarjuna Kodabagi2  \n1,2School of Computing and Information Technology, Reva University  \n1r19mds10@Cit.Reva.Edu.In  \n[2](2mallikarjun.mk@reva.edu.in)[mallikarjun.mk@reva.edu.in](2mallikarjun.mk@reva.edu.in)  \n*Corresponding author’s E-mail: r19mds10@Cit.Reva.Edu.In  \n\n| Article History\u003Cbr>Received: 06 June 2023\u003Cbr>Revised: 05 Sept 2023\u003Cbr>Accepted: 29 Nov 2023\u003Cbr>CC License\u003Cbr>CC-BY-NC-SA 4.0 | Abstract\u003Cbr>According to researches, there are around 188 million unemployed people around the globe. We may find many job vacancies on job portals and across the internet to help the job seekers. India alone has more than a hundred job portals. One major issue people face here is that the job seekers are not sure if the employer is real or fake. Most of these portals do not have a system that could check if the employer, posting ajob is real or fake. Scammers are making use of this opportunity to post fake job offers which might look genuine to the job seekers applying for it. This way the poor job seekers might lose a large amount of money and time. A best possible solution for this problem would be that the job portal itself being able to identify if the job being posted is real or fake. This paper suggests using a machine learning model to achieve this goal. The idea here is to use natural language processing to understand and analyze the job posting and then making use of a machine learning model to predict if the job posting is real or fake. The first step is to import a dataset which has real life real and fake job posting. In this project, Employment Scam Aegean Dataset provided by University of Aegean Laboratory of Information and Communication system Security is being used. This dataset contains 18000 samples containing real life job postings. Various text cleaning techniques like lemmatization, stop words removal and special characters and punctuation removal is done on the data. Once the text data is processed, various algorithms like Random Forest, Linear SVC, Gradient Boosting Classifier, Gaussian naïve Bayes classifier and XGB classifier is used to test the performance of the model. The best two algorithms with respect to the percentage of accuracy with which the models could classify real andfake job posting was taken into consideration. Random Forest and Linear SVC could give accuracy close to 98%. Both of these algorithms were tuned using GridSearchCV , a library function which is apart of sklearn’s model selection package. After tuning, the performance of both these algorithms increased and Linear SVC gave a better accuracy score of 99%. Hence Linear SVC is being used in this project for predicting real and fake job posting on a job portal..\u003Cbr>Keywords: Random Forest, Linear SVC, Natural Language Processing, Machine Learning |\n| --- | --- |\n\n1. Introduction  \nNeed for a secure job is one of the most important aspects that is required for an individual. The spammers are making use of this opportunity to post fake job postings and grab money from the job seekers. Every day five out of ten people fall prey to this trap. The main reason for this is the lack of background verification and checks made by the job portals with respect to the authenticity of the employer. The basic procedure to register as an employer for most of the job portals is quite simple. The Employer has to just enter the email-ID and the company details in order to register to the job portal. This way it is easy for the scammers to fake his/her identity and register as an employer and trap the job seekers promising them jobs of high salaries. Through this project, we have implemented natural language processing to process the text data of 18,000 real life fake and real job posting and finally use a machine learning model to classify if a job is real or fake based on t","cbCaillLmypDovKS","https://ap.wps.com/l/cbCaillLmypDovKS","pdf",354202,1,6,"English","en",105,"# Introduction\n# Literature Survey","[{\"question\":\"Why are fake job postings a serious problem for job seekers?\",\"answer\":\"Job seekers cannot reliably confirm employer authenticity, and scammers exploit this gap to post fraudulent offers. Many victims lose money and time pursuing non-existent jobs.\"},{\"question\":\"What dataset and preprocessing are used in the proposed approach?\",\"answer\":\"The work uses the Employment Scam Aegean Dataset with 18,000 real-life job posting samples. It applies text cleaning such as lemmatization, stop-word removal, and removal of special characters and punctuation.\"},{\"question\":\"Which machine learning model performs best, and what accuracy is reported?\",\"answer\":\"After testing multiple algorithms, Linear SVC is reported to achieve the highest performance. The tuned model reaches about 99% accuracy for classifying real versus fake postings.\"}]","Identifying Real and Fake Job Posting using Machine Learning | PDF",1785818761,15,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"identifying-real-and-fake-job-posting-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/identifying-real-and-fake-job-posting-using-machine-learning/123828/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are fake job postings a serious problem for job seekers?","Question",{"text":75,"@type":76},"Job seekers cannot reliably confirm employer authenticity, and scammers exploit this gap to post fraudulent offers. Many victims lose money and time pursuing non-existent jobs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and preprocessing are used in the proposed approach?",{"text":80,"@type":76},"The work uses the Employment Scam Aegean Dataset with 18,000 real-life job posting samples. It applies text cleaning such as lemmatization, stop-word removal, and removal of special characters and punctuation.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performs best, and what accuracy is reported?",{"text":84,"@type":76},"After testing multiple algorithms, Linear SVC is reported to achieve the highest performance. 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