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We present the Twitter Job/Employment Corpus, a collection of tweetsannotated by a humans-in-the-loop supervised learning framework that integratescrowdsourcing contributions and expertise on the local community and employmentenvironment. Previous computational studies of job-related phenomena have usedcorpora collected from workplace social media that are hosted internally by theemployers, and so lacks independence from latent job-related coercion and thebroader context that an open domain, general-purpose medium such as Twitterprovides. Our new corpus promises to be a benchmark for the extraction ofjob-related topics and advanced analysis and modeling, and can potentiallybenefit a wide range of research communities in the future.

Twitter Job/Employment Corpus: A Dataset of Job-Related Discourse Built with Humans in the Loop