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 languagebased analysis with temporal, geospational, and labor statistics information.
Cite
CITATION STYLE
Liu, T., Homan, C. M., Alm, C. O., White, A. M., Lytle, M. C., & Kautz, H. A. (2016). Understanding discourse on work and job-related weil-being in public social media. In 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers (Vol. 2, pp. 1044–1053). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/p16-1099
Register to see more suggestions
Mendeley helps you to discover research relevant for your work.
