JobMiner: A real-Time system for mining job-related patterns from social media

Yu Cheng, Yusheng Xie, Zhengzhang Chen, Ankit Agrawal, Alok Choudhary, Songtao Guo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Scopus citations

Abstract

The various kinds of booming social media not only provide a platform where people can communicate with each other, but also spread useful domain information, such as career and job market information. For example, LinkedIn publishes a large amount of messages either about people who want to seek jobs or companies who want to recruit new members. By collecting information, we can have a better understanding of the job market and provide insights to job-seekers, companies and even decision makers. In this pa- per, we analyze the job information from the social network point of view. We first collect the job-related information from various social media sources. Then we construct an inter-company job-hopping network, with the vertices de- noting companies and the edges denoting flow of personnel between companies. We subsequently employ graph mining techniques to mine influential companies and related company groups based on the job-hopping network model. Demonstration on LinkedIn data shows that our system Job- Miner can provide a better understanding of the dynamic processes and a more accurate identification of important entities in the job market.

Original languageEnglish (US)
Title of host publicationKDD 2013 - 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
EditorsRajesh Parekh, Jingrui He, Dhillon S. Inderjit, Paul Bradley, Yehuda Koren, Rayid Ghani, Ted E. Senator, Robert L. Grossman, Ramasamy Uthurusamy
PublisherAssociation for Computing Machinery
Pages1450-1453
Number of pages4
ISBN (Electronic)9781450321747
DOIs
StatePublished - Aug 11 2013
Event19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013 - Chicago, United States
Duration: Aug 11 2013Aug 14 2013

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
VolumePart F128815

Other

Other19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013
Country/TerritoryUnited States
CityChicago
Period8/11/138/14/13

Keywords

  • Graph mining
  • Influence analysis
  • Job market
  • Social media
  • Temporal network

ASJC Scopus subject areas

  • Software
  • Information Systems

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