{"title":"出于招聘目的分析社交媒体数据","authors":"L. Bohmova, David Chudán","doi":"10.18267/j.aip.111","DOIUrl":null,"url":null,"abstract":"Social media networks are tools that recruiters can utilize during a recruitment process. Most importantly, social media networks can be used in conjunction with applications capable of downloading information about their potential candidates. The aim of this article is to present a creation process of a model that could be helpful in recruiting area. A crucial part of this model is application software that downloads user’s data, particularly from Facebook profiles. This model should propose appropriate analytical methods for data processing. The output of this article is employee recruitment model that can be used as a guide to utilize the potential of social media networks by HR professionals. Test run of this model on our population sample showed prediction accuracy of 68 % to 84 %.","PeriodicalId":36592,"journal":{"name":"Acta Informatica Pragensia","volume":null,"pages":null},"PeriodicalIF":0.8000,"publicationDate":"2018-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":"{\"title\":\"Analyzing Social Media Data for Recruiting Purposes\",\"authors\":\"L. Bohmova, David Chudán\",\"doi\":\"10.18267/j.aip.111\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Social media networks are tools that recruiters can utilize during a recruitment process. Most importantly, social media networks can be used in conjunction with applications capable of downloading information about their potential candidates. The aim of this article is to present a creation process of a model that could be helpful in recruiting area. A crucial part of this model is application software that downloads user’s data, particularly from Facebook profiles. This model should propose appropriate analytical methods for data processing. The output of this article is employee recruitment model that can be used as a guide to utilize the potential of social media networks by HR professionals. Test run of this model on our population sample showed prediction accuracy of 68 % to 84 %.\",\"PeriodicalId\":36592,\"journal\":{\"name\":\"Acta Informatica Pragensia\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.8000,\"publicationDate\":\"2018-06-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"6\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Acta Informatica Pragensia\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.18267/j.aip.111\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Informatica Pragensia","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18267/j.aip.111","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Analyzing Social Media Data for Recruiting Purposes
Social media networks are tools that recruiters can utilize during a recruitment process. Most importantly, social media networks can be used in conjunction with applications capable of downloading information about their potential candidates. The aim of this article is to present a creation process of a model that could be helpful in recruiting area. A crucial part of this model is application software that downloads user’s data, particularly from Facebook profiles. This model should propose appropriate analytical methods for data processing. The output of this article is employee recruitment model that can be used as a guide to utilize the potential of social media networks by HR professionals. Test run of this model on our population sample showed prediction accuracy of 68 % to 84 %.