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 this https URL.

研究假新闻检测任务的众多假新闻数据集基本关注新闻的真实性，但这样单方面是无法解释 “假新闻” 的，作者提出了基于细节调查的新注释方案，以细分标注的方式捕获假新闻的多个方面，进一步对其进行深入理解和研究，并构建并发布了第一个日本假新闻数据集。

针对 “假新闻” 的注释方案重建及日本假新闻数据集

Annotation-Scheme Reconstruction for "Fake News" and Japanese Fake News Dataset

We present a human-and-model-in-the-loop process for dynamically generating
datasets and training better performing and more robust hate detection models.
We provide a new dataset of ~40,000 entries, generated and labelled by trained
annotators over four rounds of dynamic data creation. It includes ~15,000
challenging perturbations and each hateful entry has fine-grained labels for
the type and target of hate. Hateful entries make up 54% of the dataset, which
is substantially higher than comparable datasets. We show that model
performance is substantially improved using this approach. Models trained on
later rounds of data collection perform better on test sets and are harder for
annotators to trick. They also perform better on HateCheck, a suite of
functional tests for online hate detection. We provide the code, dataset and
annotation guidelines for other researchers to use. Accepted at ACL 2021.

本研究提出了一种利用人员与模型共同协作的动态数据集生成和训练模型的方法，生成了大约 40,000 项新数据并标注为 15,000 项令人挑战的扰动。通过这种方法训练出的模型性能大幅提升，具有更高的鲁棒性和更好的性能。