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

Tong Liu · Christopher Michael Homan · Cecilia Ovesdotter Alm · Megan C. Lytle · Ann Marie White · Henry Kautz

13 citations0/0 files storedAnnual Meeting of the Association for Computational Linguistics (ACL)

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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.

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2004Job demands, job resources, and their relationship with burnout and engagement: a multi‐sample study 2012Linguistic Inquiry and Word Count (LIWC)2009WaterCooler 2012A longitudinal study of Facebook, LinkedIn, & Twitter use

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2018Intercollegiate Social Media Education Ecosystem 2018Sensing and Learning Human Annotators Engaged in Narrative Sensemaking 2019Twitter Job/Employment Corpus: A Dataset of Job-Related Discourse Built with Humans in the Loop

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