Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Like Article, Like Audience: Enforcing Multimodal Correlations for
  Disinformation Detection   User-generated content (e.g., tweets and profile descriptions) and shared
content between users (e.g., news articles) reflect a user's online identity.
This paper investigates whether correlations between user-generated and
user-shared content can be leveraged for detecting disinformation in online
news articles. We develop a multimodal learning algorithm for disinformation
detection. The latent representations of news articles and user-generated
content allow that during training the model is guided by the profile of users
who prefer content similar to the news article that is evaluated, and this
effect is reinforced if that content is shared among different users. By only
leveraging user information during model optimization, the model does not rely
on user profiling when predicting an article's veracity. The algorithm is
successfully applied to three widely used neural classifiers, and results are
obtained on different datasets. Visualization techniques show that the proposed
model learns feature representations of unseen news articles that better
discriminate between fake and real news texts.

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The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
13
The categories of this paper are: quant-ph
26
The categories of this paper are: bayes-an
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
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The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom