Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Twitter Job/Employment Corpus: A Dataset of Job-Related Discourse Built
  with Humans in the Loop   We present the Twitter Job/Employment Corpus, a collection of tweets
annotated by a humans-in-the-loop supervised learning framework that integrates
crowdsourcing contributions and expertise on the local community and employment
environment. Previous computational studies of job-related phenomena have used
corpora collected from workplace social media that are hosted internally by the
employers, and so lacks independence from latent job-related coercion and the
broader context that an open domain, general-purpose medium such as Twitter
provides. Our new corpus promises to be a benchmark for the extraction of
job-related topics and advanced analysis and modeling, and can potentially
benefit a wide range of research communities in the future.

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The categories of this paper are: quant-ph
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The categories of this paper are: adap-org
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The categories of this paper are: gr-qc
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The categories of this paper are: nlin
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The categories of this paper are: mtrl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: funct-an
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The categories of this paper are: astro-ph
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The categories of this paper are: eess
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The categories of this paper are: hep-th
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The categories of this paper are: nucl-th
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The categories of this paper are: patt-sol
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The categories of this paper are: alg-geom
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The categories of this paper are: q-alg
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The categories of this paper are: stat
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The categories of this paper are: cond-mat