Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Weak Supervision for Fake News Detection via Reinforcement Learning   Today social media has become the primary source for news. Via social media
platforms, fake news travel at unprecedented speeds, reach global audiences and
put users and communities at great risk. Therefore, it is extremely important
to detect fake news as early as possible. Recently, deep learning based
approaches have shown improved performance in fake news detection. However, the
training of such models requires a large amount of labeled data, but manual
annotation is time-consuming and expensive. Moreover, due to the dynamic nature
of news, annotated samples may become outdated quickly and cannot represent the
news articles on newly emerged events. Therefore, how to obtain fresh and
high-quality labeled samples is the major challenge in employing deep learning
models for fake news detection. In order to tackle this challenge, we propose a
reinforced weakly-supervised fake news detection framework, i.e., WeFEND, which
can leverage users' reports as weak supervision to enlarge the amount of
training data for fake news detection. The proposed framework consists of three
main components: the annotator, the reinforced selector and the fake news
detector. The annotator can automatically assign weak labels for unlabeled news
based on users' reports. The reinforced selector using reinforcement learning
techniques chooses high-quality samples from the weakly labeled data and
filters out those low-quality ones that may degrade the detector's prediction
performance. The fake news detector aims to identify fake news based on the
news content. We tested the proposed framework on a large collection of news
articles published via WeChat official accounts and associated user reports.
Extensive experiments on this dataset show that the proposed WeFEND model
achieves the best performance compared with the state-of-the-art methods.

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The categories of this paper are: q-bio
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The categories of this paper are: chem-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: stat
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The categories of this paper are: funct-an
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The categories of this paper are: plasm-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
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The categories of this paper are: alg-geom
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The categories of this paper are: solv-int
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The categories of this paper are: supr-con
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The categories of this paper are: gr-qc
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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