Work

Understanding Discourse on Work and Job-Related Well-Being in Public Social Media

Year: 2016

Type: conference-paper

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.

Source: Proceedings of the conference - Association for Computational Linguistics. Meeting

DOI: 10.18653/v1/p16-1099

Authors: Tong Liu, Christopher Homan, Cecilia Ovesdotter Alm, Megan Lytle, Ann Marie White, Henry Kautz

Institutions: Rochester Institute of Technology, University of Rochester Medicine, University of Rochester

Language: English

Topic: Computational and Text Analysis Methods

Subfield: General Social Sciences

Field: Social Sciences

Domain: Social Sciences

SDG: Decent work and economic growth

Open Access status: hybrid

Funders: National Science Foundation, National Institutes of Health, National Institute of General Medical Sciences, National Center for Advancing Translational Sciences