SAFE: Similarity-Aware Multi-Modal Fake News Detection
- URL: http://arxiv.org/abs/2003.04981v1
- Date: Wed, 19 Feb 2020 02:51:04 GMT
- Title: SAFE: Similarity-Aware Multi-Modal Fake News Detection
- Authors: Xinyi Zhou, Jindi Wu, Reza Zafarani
- Abstract summary: We propose a new method to detect fake news based on its text, images, or their "mismatches"
Such representations of news textual and visual information along with their relationship are jointly learned and used to predict fake news.
We conduct extensive experiments on large-scale real-world data, which demonstrate the effectiveness of the proposed method.
- Score: 8.572654816871873
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Effective detection of fake news has recently attracted significant
attention. Current studies have made significant contributions to predicting
fake news with less focus on exploiting the relationship (similarity) between
the textual and visual information in news articles. Attaching importance to
such similarity helps identify fake news stories that, for example, attempt to
use irrelevant images to attract readers' attention. In this work, we propose a
$\mathsf{S}$imilarity-$\mathsf{A}$ware $\mathsf{F}$ak$\mathsf{E}$ news
detection method ($\mathsf{SAFE}$) which investigates multi-modal (textual and
visual) information of news articles. First, neural networks are adopted to
separately extract textual and visual features for news representation. We
further investigate the relationship between the extracted features across
modalities. Such representations of news textual and visual information along
with their relationship are jointly learned and used to predict fake news. The
proposed method facilitates recognizing the falsity of news articles based on
their text, images, or their "mismatches." We conduct extensive experiments on
large-scale real-world data, which demonstrate the effectiveness of the
proposed method.
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