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Employment relations: a data driven analysis of job markets using online job boards and online professional networks

Published: 23 August 2017 Publication History

Abstract

Data from online job boards and online professional networks present an opportunity to understand job markets as well as how professionals transition from one job/career to another. We propose a data driven approach to begin to understand a slice of the South African job market. We do this by analysing data from career websites as well as a South African online professional networks. Our goals are to be able to group jobs given their descriptions, characterise career paths as well as to have some building blocks to be able to extract job position hierarchies given a description.

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Cited By

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  • (2024)Data science for job market analysis: A survey on applications and techniquesExpert Systems with Applications10.1016/j.eswa.2024.124101251(124101)Online publication date: Oct-2024
  • (2022)Tackling Climate Change with Machine LearningACM Computing Surveys10.1145/348512855:2(1-96)Online publication date: 7-Feb-2022

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cover image ACM Conferences
WI '17: Proceedings of the International Conference on Web Intelligence
August 2017
1284 pages
ISBN:9781450349512
DOI:10.1145/3106426
© 2017 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 August 2017

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WI '17 Paper Acceptance Rate 118 of 178 submissions, 66%;
Overall Acceptance Rate 118 of 178 submissions, 66%

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View all
  • (2024)Data science for job market analysis: A survey on applications and techniquesExpert Systems with Applications10.1016/j.eswa.2024.124101251(124101)Online publication date: Oct-2024
  • (2022)Tackling Climate Change with Machine LearningACM Computing Surveys10.1145/348512855:2(1-96)Online publication date: 7-Feb-2022

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