title: Understanding Discourse on Work and Job-Related Well-Being in Public Social Media.
abstract: We construct a humans-in-the-loop supervised learning framework that integrates crowdsourcing feedback and local knowledge to detect job-related tweets from individual and business accounts. Using data-driven ethnography, we examine discourse about work by fusing language-based analysis with temporal, geospational, and labor statistics information.
authors: Golisano College of Computing and Information Sciences, Rochester Institute of Technology.
authors: Tong
authors: T
authors: Liu
authors: Golisano College of Computing and Information Sciences, Rochester Institute of Technology.
authors: Christopher M
authors: CM
authors: Homan
authors: College of Liberal Arts, Rochester Institute of Technology.
authors: Cecilia Ovesdotter
authors: CO
authors: Alm
authors: University of Rochester Medical Center.
authors: Ann Marie
authors: AM
authors: White
authors: University of Rochester Medical Center.
authors: Megan C
authors: MC
authors: Lytle
authors: Department of Computer Science, University of Rochester.
authors: Henry A
authors: HA
authors: Kautz