Annotation-Scheme Reconstruction for "Fake News" and Japanese Fake News
Dataset
- URL: http://arxiv.org/abs/2204.02718v1
- Date: Wed, 6 Apr 2022 10:42:39 GMT
- Title: Annotation-Scheme Reconstruction for "Fake News" and Japanese Fake News
Dataset
- Authors: Taichi Murayama, Shohei Hisada, Makoto Uehara, Shoko Wakamiya, Eiji
Aramaki
- Abstract summary: "Fake news" is a complex phenomenon that involves a wide range of issues.
We propose a novel annotation scheme with fine-grained labeling based on detailed investigations of existing fake news datasets.
Using the annotation scheme, we construct and publish the first Japanese fake news dataset.
- Score: 1.7149364927872013
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Fake news provokes many societal problems; therefore, there has been
extensive research on fake news detection tasks to counter it. Many fake news
datasets were constructed as resources to facilitate this task. Contemporary
research focuses almost exclusively on the factuality aspect of the news.
However, this aspect alone is insufficient to explain "fake news," which is a
complex phenomenon that involves a wide range of issues. To fully understand
the nature of each instance of fake news, it is important to observe it from
various perspectives, such as the intention of the false news disseminator, the
harmfulness of the news to our society, and the target of the news. We propose
a novel annotation scheme with fine-grained labeling based on detailed
investigations of existing fake news datasets to capture these various aspects
of fake news. Using the annotation scheme, we construct and publish the first
Japanese fake news dataset. The annotation scheme is expected to provide an
in-depth understanding of fake news. We plan to build datasets for both
Japanese and other languages using our scheme. Our Japanese dataset is
published at https://hkefka385.github.io/dataset/fakenews-japanese/.
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