JobMiner: a real-time system for mining job-related patterns from social media

Authors:
Yu Cheng;Yusheng Xie;Zhengzhang Chen;Ankit Agrawal;Alok Choudhary;Songtao Guo
Affiliations:
Northwestern University, Evanston, Illinois, USA;Northwestern University, Evanston, Illinois, USA;Northwestern University, Evanston, Illinois, USA;Northwestern University, Evanston, Illinois, USA;Northwestern University, Evanston, Illinois, USA;LinkedIn, Mountain View, California, USA
Venue:
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2013

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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 paper, 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 denoting companies and the edges denoting flow of personnel between companies. We subsequently employ graphmining techniques to mine influential companies and related company groups based on the job-hopping network model. Demonstration on LinkedIn data shows that our system JobMiner can provide a better understanding of the dynamic processes and a more accurate identification of important entities in the job market.