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.
The most parallel work that we can leverage here is the method and corpus developed by REF , which is an effective supervised learning system to detect job-related tweets from individual and business accounts.
Understanding Discourse on Work and Job-Related Well-Being in Public Social Media
ACL
Medicine Computer Science