Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Predicting the Topical Stance of Media and Popular Twitter Users   Discovering the stances of media outlets and influential people on current,
debatable topics is important for social statisticians and policy makers. Many
supervised solutions exist for determining viewpoints, but manually annotating
training data is costly. In this paper, we propose a cascaded method that uses
unsupervised learning to ascertain the stance of Twitter users with respect to
a polarizing topic by leveraging their retweet behavior; then, it uses
supervised learning based on user labels to characterize both the general
political leaning of online media and of popular Twitter users, as well as
their stance with respect to the target polarizing topic. We evaluate the model
by comparing its predictions to gold labels from the Media Bias/Fact Check
website, achieving 82.6% accuracy.

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The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
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The categories of this paper are: hep-lat
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The categories of this paper are: econ
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The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys
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The categories of this paper are: math
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The categories of this paper are: plasm-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: ao-sci
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The categories of this paper are: gr-qc
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The categories of this paper are: math-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: nucl-ex
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The categories of this paper are: eess
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The categories of this paper are: stat