Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Fine-Grained Reasoning for Fake News Detection   The detection of fake news often requires sophisticated reasoning skills,
such as logically combining information by considering word-level subtle clues.
In this paper, we move towards fine-grained reasoning for fake news detection
by better reflecting the logical processes of human thinking and enabling the
modeling of subtle clues. In particular, we propose a fine-grained reasoning
framework by following the human information-processing model, introduce a
mutual-reinforcement-based method for incorporating human knowledge about which
evidence is more important, and design a prior-aware bi-channel kernel graph
network to model subtle differences between pieces of evidence. Extensive
experiments show that our model outperforms the state-of-the-art methods and
demonstrate the explainability of our approach.

35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
30
The categories of this paper are: q-alg
17
The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
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The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
19
The categories of this paper are: eess